Handbook of Combustion, Volume 1 (Fundamentals and Safety)

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1 Combustion Fundamentals Mohammad Janbozorgi, Kian Eisazadeh Far, and Hameed Metghalchi

1.1 Introduction

Combustion is a complex subject in chemical physics. A deep understanding of combustion science requires a solid grasp of a wide spectrum of scientific disciplines, such as quantum mechanics, thermodynamics, chemical kinetics, and fluid dynamics. On the application level, combustion phenomena can be classified based on interactions between exothermic chemical reactions and fluid mechanics. Such an interaction depends heavily on the relative order of magnitude of the time and spatial scales of each individual phenomenon, leading to different forms of combustion. Premixed combustion occurs when the fluid mixing is sufficiently fast as to create a near-uniform distribution of fuel/air mixture in the reactor. Depending on the thermodynamic conditions, premixed combustion can also be either strictly kinetically controlled (e.g., autoignition), or convection/reaction/diffusion controlled (e.g., premixed flames). The former condition underlies the operation of homogeneous charged-compression-ignition (HCCI) engines, diesel engines and rapid compression machines (RCMs), and is essential in understanding the engine knock. The latter introduces a fundamental physico-chemical property for any premixed mixture, that is, laminar burning speed. Knowledge of this property is crucially important in spark-ignition engines, partly to prevent autoignition. In the case of slow mixing and fast reaction, nonpremixed or diffusion flames will be observed. This chapter is devoted to an analysis of the above-mentioned modes of combustion, with special emphasis placed on laminar burning speeds and flame structures of different hydrocarbons at high pressures, and the experimental methods to measure them. Such data are extremely important for the validation of any reliable chemical kinetic mechanism, and will be especially useful for internal combustion engine designers.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright
2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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1.2 Combustion Thermodynamics

Combustion is defined as an energy-evolving (exothermic) chemical transformation [1]. While strictly involving time-dependent chemical reactions, the final yield of combustion, and how much energy can be extracted from a fuel/air mixture under a specified process, are restricted by the laws of thermodynamics. A stoichiometric mixture of fuel and air is defined as a mixture containing just enough oxygen to theoretically burn the hydrocarbon fuel to water and carbon dioxide (only hydrocarbon fuels are considered in this chapter). The equivalence ratio is commonly used to indicate quantitatively whether a fuel/oxidizer mixture is rich, lean, or stoichiometric [2]: w¼

ðA=FÞstoic ðF=AÞact ¼ ðA=FÞact ðF=AÞstoic

ð1:1Þ

Fuel-rich, fuel-lean, and stoichiometric mixtures are defined by w > 1, w < 1, and w ¼ 1, respectively. In Equation 1.1, A is the mass of air, F the mass of fuel, and the “stoic” and “act” subscripts represent the stoichiometric and actual mixtures, respectively. 1.2.1 Enthalpy of Reaction

The enthalpy of reaction or enthalpy of combustion is defined as the net change of enthalpy due to a chemical reaction. This quantity takes a positive value for an endothermic reaction, and a negative value for an exothermic reaction. This means that in the former reaction the energy is absorbed by the reacting system, whereas in the latter reaction it evolves as a result of the reaction. Considering the global stoichiometric combustion chemical reaction of a generic nonoxygenated hydrocarbon with air:
y  y H2 O þ 3:76 jN2 ; Cx Hy þ jðO2 þ 3:76N2 Þ ! xCO2 þ j ¼ xþ 2 4 ð1:2Þ this statement translates to DhR ¼ hprod ðTf Þ
hreac ðTR Þ
y hH2 O ðTf Þ þ 3:76 jhN2 ðTf Þ ¼ xhCO2 ðTf Þ þ 2
hCx Hy ðTR Þ
jhO2 ðTR Þ
3:76 jhN2 ðTR Þ

ð1:3Þ

where h is the enthalpy and T is the temperature. Depending on the thermodynamic process that the system undergoes, the products’ temperature – also called the flame temperature (Tf ) – may be different from the temperature of reactants (TR ). The heat

1.2 Combustion Thermodynamics

of combustion represents the amount of energy released or absorbed by the mixture during an isothermal chemical conversion. For example, CO þ H2 OðgÞ ! CO2 þ H2 ðgÞ;

DhR ¼
41:16 kJ ðTf ¼ TR ¼ 298:15 KÞ

means that 41.16 kJ of energy will be released to the surroundings if 1 mole of carbon monoxide reacts completely with 1 mole of water vapor at constant pressure to produce 1 mole of carbon dioxide and 1 mole of hydrogen molecule [1]. The total enthalpy of a species A is defined as: hA ¼ hf;A ðTref Þ þ Dhs;A ðTref ; TÞ

ð1:4Þ

The first term in Equation 1.4 represents the enthalpy of formation, and is defined as the net change in enthalpy associated with breaking the chemical bonds of the standard state elements and forming new bonds to create the compound of interest [2]. Here, the standard state elements are taken to be the most stable state of that element at the temperature of interest and the pressure of 1 atmosphere. For the common elements of combustion interest at Tref ¼ 298:15 K, these states are carbon (C) as graphite, molecular hydrogen (H2), oxygen (O2), nitrogen (N2) as ideal gases, and atomic sulfur (S) as solid [1]. The natural consequence of this definition is that the enthalpy of formation of, for example, an oxygen atom (O) is half of the bond dissociation energy of the oxygen molecule. The second term in Equation 1.4 represents the sensible enthalpy change and is defined as: ðT Dhs;A ðTref ; TÞ ¼

cp;A ðTÞ dT

ð1:5Þ

Tref

Clearly, any departures from the standard state enthalpy are reflected in this term. Values of the specific heat at constant pressure, cp ðTÞ, are tabulated for many species in the CHEMKIN [3] database. 1.2.2 Flame Temperature

Flame temperature is the temperature reached at the state of chemical equilibrium in a reacting system. The energy balance, dE ¼ dQ
dW, implies that, for a fixed type of work interaction with the surrounding environment, an adiabatic process, dQ ¼ 0, has the highest flame temperature. Furthermore, depending on the relative order of magnitude of the chemical energy release time scale, tch , and that of boundary work interaction through acoustic wave propagation, ta , this adiabatic temperature falls between two extremes: (a)

tch  ta , resulting in dV ¼ 0. This further results in dW ¼ pdV ¼ 0 and therefore dE ¼ 0, known as constant energy–constant volume flame temperature, Tf ;ðE;VÞ . Here, p is the pressure and V represents volume.

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(b) tch  ta , resulting in dp ¼ 0. This further translates to dH ¼ dðE þ pVÞ ¼ 0, known as constant enthalpy–constant pressure flame temperature, Tf ;ðH;pÞ . Since in the latter case part of the energy is used to do work against the surrounding environment, it results in a lower flame temperature, that is, Tf ;ðH;pÞ < Tf ;ðE;VÞ . The first case is usually assumed to be true for autoignition in closed systems (e.g., shock tubes), while the latter is assumed to be the case for combustion in open systems at low Mach numbers (e.g., gas turbine combustors, Bunsen burners). 1.2.3 Chemical Equilibrium

There are two different approaches to determine the chemical equilibrium composition of a reacting mixture. One is based on the application of the Law of Mass Action [1], and the other on the method of Lagrange multipliers [4]. If Nsp is the number of species and Ne is the number of atomic elements in the system, then the first method requires the compilation of Nsp
Ne independent chemical reactions, followed by the simultaneous solution of the same number of equations, one for each reaction. Obviously, as the number of chemical species increases, the problem becomes more tedious. An excellent coverage of this approach can be found in Ref. [1]. The second method, which does not have the shortcomings of the first approach, consists of considering all conceivable reaction mechanisms between the chemical species, and using the maximum entropy principle without specifying explicitly any of the reaction mechanisms [4]. As pointed out by Keck [5], an equilibrium state is meaningful only when the constraints subject to which such a state is attained are carefully determined, and all equilibrium states are in fact constrained equilibrium states. At temperatures of interest to combustion, nuclear and ionization reactions can be assumed frozen, and the fundamental constraints imposed on the system are the conservation of neutral atoms. If Ni , aij and SðU; V; Ni Þ represent, respectively, the number of moles of species i, the number of jth atomic element in species i, and the entropy of a closed adiabatic system, then Cj ¼

Nsp X

aij Ni ;

j ¼ 1; . . . ; Ne

ð1:6Þ

i¼1

is the total number of moles of atomic element j in the system, which is conserved during chemical conversion. Therefore, the problem reduces to determining a chemical composition which maximizes SðU; V; Ni Þ subject to the relationship in Equation 1.6. Using the method of undetermined Lagrange multipliers, it can be easily shown that such a composition will be given by [5]: Ni ¼ Q i exp


Ne X j¼1

! aij cj ;

i ¼ 1; . . . ; Nsp

ð1:7Þ

1.3 Chemical Kinetics

where cj is the constraint potential (Lagrange multiplier) conjugate to elemental constraint Cj , and Q i is the partition function of species i, which is defined as follows: Qi ¼

   m ðTÞ p V exp
i ; Ru T Ru T

i ¼ 1; . . . ; Nsp

ð1:8Þ

In Equation 1.8, mi ðTÞ represents the standard Gibbs free energy of species i at temperature T, and Ru is the universal gas constant. As this function takes on finite values for every species, Equation 1.7 shows that, in principle, all species made of the declared atomic elements are present at chemical equilibrium state, no matter how small their concentration. This further explains why full conversion to carbon dioxide and water in a stoichiometric mixture, as given by Equation 1.2, is a hypothetical situation. Substituting Equation 1.7 back into Equation 1.6 forms a set of Ne transcendental equations for cj ’s. The level of reduction in the number of equations to be solved is dazzling; from Nsp which could easily go up to several thousands for heavy hydrocarbons to a maximum of five for atomic elements of carbon, oxygen, hydrogen, nitrogen and sulfur for almost any hydrocarbon fuels. This method forms the basis of the widely used equilibrium codes of STANJAN [6] and NASA [7].

1.3 Chemical Kinetics 1.3.1 Combustion Chemical Reactions

The development of models for describing the dynamic evolution of chemically reacting systems is a fundamental objective of chemical kinetics. This task involves identifying the chemical reactions in the most elementary level, and also the rate at which such reactions proceed. The conventional approach to this problem involves first specifying the state and species variables to be included in the model, compiling a “full set” of rate-equations for these variables based on a “full set” of elementary chemical reactions, and then integrating this set of equations to obtain the timedependent behavior of the system [8]. Such models are frequently referred to as detailed kinetic models (DKMs). The most widely known and used such model is GRI-MECH 3.0 for the combustion of methane at high temperatures and low pressures [9]. A DKM can easily include several hundred chemical species and several thousand chemical reactions for heavy hydrocarbons [10]. (An extensive study of DKMs is provided in Chapter 2.) Combustion chemical reactions are in general chain reactions, which means that the products of one reaction serve as the reactants of other reactions. However, independent of the fuel molecule, these reactions can be classified into four groups: .

Initiation reactions, which start the chain, involve collision with fuel by oxygen molecule and, as the radical pool is populated, by radicals. FU þ O2 ¼ FR þ HO2

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.

.

.

and FU þ X ¼ FR þ XH, in which FU, FR, and X represent the fuel molecule, fuel radical and radical pool (O, OH, H, HO2, etc.), respectively, fall into this class. If the temperature is sufficiently high, the cracking of either C
H bonds or C
C bonds in the fuel molecule becomes also important. Chain-branching reactions change the number of radicals and populate the radical pool. H þ O2 ¼ OH þ O is one of the most important chain-branching reactions in combustion applications. Chain-propagating reactions change the type of radical, while conserving the number of radicals in the reactions. H þ H2O ¼ H2 þ OH represents these reactions. Three-body reactions , sometimes known as chain-terminating reactions or equivalently, dissociation–recombination reactions, change the number of moles of the mixture; for example, H þ OH þ M ¼ H2O þ M. As the recombination of radicals is highly exothermic and usually involves only a small rotational energy barrier, these reactions cannot be bimolecular and require interaction with a third body (M), to which the energy of molecule formation is disposed. Otherwise, this energy would dissociate the products into the original reactants. Depending on their molecular size, different molecules have different third-body efficiencies. A more detailed presentation of this subject is provided in Chapter 2.

1.3.2 Kinetic Rate Equations

Assuming that changes in the chemical composition of the system are the results of elementary reactions of the type: Nsp X i¼1

n
ik xi $

Nsp X

nikþ xk ;

k ¼ 1; . . . ; Nr

ð1:9Þ

i¼1

then the molar rate of change of each chemical species can be expressed as Nr X 1 dNi þ
¼ v_ i ¼ ðnikþ
n
ik Þðrk
rk Þ; V dt k¼1

i ¼ 1; . . . ; Nsp

ð1:10Þ

where rkþ ¼ kfkþ ðTÞ

Nsp Y l¼1

þ

½xl nlk ;

rk
¼ k
rk ðTÞ

Nsp Y




½xl nlk

ð1:11Þ

l¼1

The temperature dependence of kf and kr are represented by the Arrhenius form as   Ea : ð1:12Þ kðTÞ ¼ A0 exp
Ru T

1.3 Chemical Kinetics

The pre-exponential factor or collision frequency, A0 , is weakly temperaturedependent over the temperatures reached in combustion applications. Such temperature dependence is represented by a modified form of A0 ¼ AT n , in which n is called the “temperature exponent.” The exponential part, on the contrary, is strongly temperature-dependent. This part represents the fraction of molecules possessing enough energy to surmount the activation energy barrier, Ea and undergo chemical reactions. The typical value of this parameter for combustion of hydrocarbons is 40–45 kcal mol–1. When considering Equation 1.10, at the state of dynamic equilibrium the forward and reverse reaction rates must balance, which leads to the Principle of Detailed Balancing; sp þ kfk ðTÞ Y
¼ ½xl ðnlk
nlk Þ krk ðTÞ l¼1

N

Kc ðTÞ ¼

ð1:13Þ

Although this relationship is obtained under chemical equilibrium, the first equality in Equation 1.13 also holds at nonequilibrium conditions. The reason for this is that chemical reactions are assumed to be too slow to disturb the Maxwell– Boltzmann distribution of energy among internal molecular degrees of freedom, hence local thermodynamic equilibrium among internal degrees of freedom. This further allows the definition of a single temperature during chemical relaxation. The principle of detailed balance provides a tool to determine the reverse rate of reaction, kr based on the forward rate constant and equilibrium coefficient. A less common practice is also to assign the reverse rate independently of the forward rate [11]. However, this approach is less accurate than using the equilibrium constant, for the obvious reason that the thermodynamic data are known much more accurately than the kinetic data. 1.3.3 Chemical Time Scales and Nonequilibrium Effects

Each species and reaction in a kinetic mechanism evolves based on definite time scales. Species time scales can be defined as follows: t
1 i ¼

Nr 1 dNi 1 X þ
¼ ðn þ
n
ik Þðrk
rk Þ; Ni dt ½Ni  k¼1 ik

i ¼ 1; . . . ; Nsp

ð1:14Þ

in which use has been made of Equation 1.10. In this form, the chemical time scale is determined based on the collective effect of all chemical reactions which either consume or produce species i. If only one reaction is considered, say the kth reaction, then the reaction time scale can be defined as trk ¼ Maxftrki g, in which trki ¼ Ni/nrk is the reaction time based on species participating in reaction k. Combustion chemical reactions are usually characterized by a wide spectrum of chemical time scales. When a chemical system undergoes either heat or work interaction with the surrounding environment on a time scale text , depending on how the chemical time scales compare with text , the system could be either in the state of local thermodynamic

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equilibrium (LTE), text  Maxfti g, nonequilibrium, Minfti g < text < Maxfti g, or frozen equilibrium, text  Minfti g. The sudden expansion of combustion products in an internal combustion engine, or through a hypersonic nozzle and sudden cooling of combustion products through a heat exchanger with constant area, are examples of such interactions. According to the principle of Le Chatelier, the internal dynamics shifts towards minimizing the effect of external change and re-establishing a new chemical equilibrium, consistent with the new values of the state variables. If the interaction lowers the gas temperature and density of a highly dissociated mixture, then the internal dynamics will shift in the exothermic direction so as to minimize the cooling effect of interaction. As a result, three-body recombination reactions – for example, H þ H þ M ¼ H2 þ M and H þ O2 þ M ¼ HO2 þ M – become an important part of the energy restoration process. Bimolecular reactions also shift towards the exothermic direction. From a kinetics standpoint, three-body reactions have small or zero activation energies, which makes them almost temperature-insensitive and rather highly pressure- (density) sensitive, whereas the rate of bimolecular reactions which involve activation energies are temperature-sensitive [12]. Therefore, sudden cooling to low temperatures and lowering of the density will depress the rate of recombination and exothermic bimolecular reactions markedly, and the exothermic processes will lag in their attempt to restore the equilibrium. A failure to release the latent energy of molecule formation enhances the cooling and puts the system farther out of equilibrium. If the expansion is fast enough, then the exothermic lag grows indefinitely and the composition becomes frozen [13]. An important situation where predictions based on equilibrium fail is the predictions of CO at the exhaust of an internal combustion engine. Here, the main reaction step in oxidation of CO to CO2 is CO þ OH ¼ CO2 þ H, which involves an activation energy of about 18 kcal mol
1. This energy barrier makes the reaction temperature-sensitive such that, when the temperature falls, the reaction becomes slower, and so does the energyrestoration process. Such an effect shows itself in departures from LTE predictions. Janbozorgi et al. have examined is redundant the expansion stroke of an internal combustion engine with an intermediate piston speed, and compared the kinetic predictions with frozen and LTE predictions, as shown in Figure 1.1 [12]. Clearly, during the early stages of expansion, where the piston speed is slow, the state of the gas follows the LTE predictions and departures emerge as the piston speed increases. 1.3.4 Kinetics Simplification and Reduction

Considering the fact that a DKM involves a wide spectrum of chemical time scales associated with chemical species, the system of Equation 1.10 comprises a set of stiff ordinary differential equations (ODEs), which could be computationally expensive for reacting flows. As a result, a great deal of effort has been devoted to developing methods for reducing the size of DMKs. A quasi-steady-state approximation (QSSA) [14, 15], which is usually employed for short-lived radicals, assumes that after a so-called “induction period” the reactions

1.3 Chemical Kinetics

Figure 1.1 CO predictions during expansion stroke using different models at an engine speed of 3000 rpm.

consuming radicals become much faster than those producing them; hence a low, stationary level of these intermediates emerges. Mathematically, this is equivalent with zero net rate of change in Equation 1.10, which is a deliberate transition from differential to algebraic equations for these intermediates. However, deciding which radicals this assumption can be applied to requires a good deal of knowledge and physical intuition on the part of the kineticist. A partial equilibrium approximation (PEA) [16] is invoked for reactions which reach a state of dynamic equilibrium. The entropy generation due to a chemical reaction k can be expressed as: sp 1X dNi 1 ¼
m ¼ T i¼1 i dt T

N

dSk jE;V

Nsp X

! mi nik dlk

ð1:15Þ

i¼1

where lk is the progress variable of reaction k. The necessary and sufficient condition for reaction k to be in equilibrium is that the entropy must be a maximum with respect to all possible changes dlk , and so sp dSk 1X jE;V ¼ m nik ¼ 0 T i¼1 i dlk

N

ð1:16Þ

is the constraint to be satisfied by a reaction to be in partial equilibrium. As mentioned in Ref. [17], however, the check for whether a reaction k satisfies partial equilibrium assumption or not, should be based on the order of magnitude of the net rate of change due to reaction k compared to net rate of production and net rate of consumption due to that reaction individually. The more advanced methods are based on ideas from dynamical systems, computational singular perturbation (CSP) [18] and inertial low-dimensional manifold (ILDM) [19], to automatically

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identify the species and reactions for which QSSA and PEA hold. Other elegant methods, such as Adaptive Chemistry [20], Directed Relation Graph (DRG) [21], the ICE-PIC method [22] and rate-controlled constrained-equilibrium (RCCE) [5, 23] have been proposed and developed. Whilst a detailed presentation of combustion kinetics modeling has been undertaken in Chapter 8, details of the RCCE method are provided in the next section. 1.3.4.1 Rate-Controlled Constrained-Equilibrium (RCCE) Method The idea of RCCE is a logical extension of chemical equilibrium constrained to the conservation of neutral atoms (as discussed in Section 1.2.3). Owing to their very high activation energies, slow ionization and nuclear reactions can be assumed frozen over the energies and time scales encountered in combustion applications, leading to conservation of atomic elements. By the same token, the cascade of constraints in a chemically reacting system can be easily extended based on the existence of classes of slow chemical or energy-exchange reactions which, if completely inhibited, would prevent the relaxation of the system to the complete chemical equilibrium. For instance, a heavy hydrocarbon does not break down into smaller fragments unless the C–C bonds are broken; the total number of moles in a reacting system does not change unless a three-body reaction occurs; and radicals are not generated in the absence of chain-branching reactions, the definition of a single temperature in a chemically reacting system is based on the observation that thermal equilibration among translation, rotation and vibration is in general faster than the chemical reactions [12]. Consistent with the perfect gas assumption and definition (Equation 1.6), the constraints imposed on the system by the reactions are assumed to be a linear combination of the mole numbers of the species present in the system: Cj ¼

Nsp X

aij Ni ;

j ¼ 1; . . . ; Nc

ð1:17Þ

i¼1

where Cj includes kinetic constraints in addition to the elemental constraints defined earlier, and aij has the same meaning as before; the value of constraints j in species i. The mathematical work is exactly the same as that presented under chemical equilibrium, and the constrained-equilibrium composition of the system is, therefore, expressed by Equations 1.7 and 1.8. By taking the time derivative of Equation 1.17 and using Equation 1.10, it is possible easily to obtain: C_ j ¼

Nr X k¼1

bjk ðrkþ
rk
Þ;

bjk ¼

Nsp X

aij nik

ð1:18Þ

i¼1

where Nr is the number of reactions in the mechanism. Clearly, any reaction k that does not change all constraints j is in constrained-equilibrium, and not required. The working equations of RCCE in terms of constraint potentials have been derived for a constant volume, constant energy system in Ref. [8]. Since the oxidation of any heavy hydrocarbon fuel is characterized by essentially the complete fragmentation of large molecules to a mixture of small hydrocarbons, it

1.3 Chemical Kinetics

4500

RCCE

P = 100 atm

DKM

Temperature (K)

4000

P = 50 atm

P = 10 atm

3500

P = 1 atm

3000 2500 2000 1500 1000 -4

-3

-2 -1 Log t (s)

0

1

Figure 1.2 Ignition delay times of stoichiometric CH4/O2 mixtures at T ¼ 900 K and various pressures.

is a mixture of the smaller fuel fragments, mostly C1 and C2, that eventually react to form the final product of combustion and release heat [24]. On the basis of this fact, Janbozorgi et al. [8] considered the tail of this process – namely the oxidation of C1 fuels (CH4, CH3OH, CH2O) – and determined a set of constraints that were able to accurately model the C1 chemistry in a unified manner over a wide range of initial temperatures, pressures, and equivalence ratios. Figures 1.2 and 1.3 show the predictions of their model against a detailed kinetic mechanism which included 29 species and 133 reactions. It was concluded that the dominant oxidation path of CH4 at low temperature is through the formation of methyl peroxides: CH4 ! CH3 ! CH3 OO ! CH3 OOH ! CH2 OOH ! CH2 O ! CHO ! CO; 4500

RCCE DKM

P = 50 atm

4000 Temperature (K)

P = 100 atm

P = 10 atm

P = 1 atm

3500 3000 2500 2000 1500 -7

-6

-5 Log t (s)

-4

-3

Figure 1.3 Ignition delay times of stoichiometric CH4/O2 mixtures at T ¼ 1500 K and various pressures.

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whereas at higher temperature formation of the methoxy radical, CH3O, dominated the chemistry: CH4 ! CH3 ! CH3 O ! CH3 OH ! CH2 OH ! CH2 O ! CHO ! CO

The competition between the following two reactions differentiates the low- and high-temperature paths: CH3 þ O2 þ M ¼ CH3 OO þ M CH3 þ O2 ¼ CH3 O þ O

The formation of alkyl peroxides at low temperatures is responsible for the observed cool flame phenomenon in higher hydrocarbons, and is the most important aspect of the low-temperature combustion chemistry of hydrocarbon fuels. Cool flames are described in detail in Chapter 13, while general low-temperature combustion chemistry is described in Chapter 2. It should be noted that, as GRI-MECH 3.0 does not involve alkyl peroxides, it is incapable of modeling low-temperature homogeneous oxidation of methane.

1.4 Laminar Premixed Flames

The competition between chemical energy release and energy loss through the boundaries determines a global time scale, known as the “ignition delay time,” tig ðT; p; Fuel; wÞ. Knowledge of this characteristic time is crucial in the design and operation of a number of practical and research devices, such as diesel engines, spark-ignition (SI) engines, HCCI engines and RCMs. In SI engines, the chemical activities in the end gas are accelerated by the isentropic compression due to piston motion and the propagating flame which, under the correct thermodynamic conditions, can lead to the well-known knocking phenomenon. It has been well established [25–27] that, depending on the initial temperature and gradients in temperature and mixture composition within the reactor, the reaction zone can have different speeds, ranging from the laminar premixed flame speed to infinity. Such gradients could be due to imperfect mixing. Assuming that the temperature gradient is the only nonuniformity in a motionless mixture, successive points along the gradient have different delay times, leading to the propagation of an autoignition wave [25]. In general, a characteristic autoignition velocity (uig) relative to the unburned gas can be defined as:   qtig rT ð1:19Þ u
1 ¼ ig qT It has been recognized [28, 29] that, if this velocity is comparable with the acoustic velocity, then the pressure wave generated by the combustion energy release can couple with the autoignition front, with mutual reinforcement of both fronts and a very rapid reaction. When the autoignition wave moves much faster or slower than

1.4 Laminar Premixed Flames

the acoustic velocity, however, such coupling does not occur and the combustion is less intense [25]. In the extreme slow motion of this wave which, according to Equation 1.19, corresponds to a large gradient between burned and unburned gases, the overall chemical time scale becomes comparable with the diffusion time scales and laminar flames emerge. However, for faster velocities molecular transports do not play an important role in determining the wave structure. For this reason, laminar flames are usually called “diffusion waves,” and “detonation waves” are a shock wave coupled with an autoignition front. A flame is a self-sustaining propagation of a localized combustion zone at subsonic velocities [2]. The balance between the molecular mass transport of fresh reactants into the reaction zone, chemical energy release within this zone, and the energy carried from the reaction zone back into the reactants by heat conduction over the length d, determines the wave structure. In this case, the velocity defined in Equation 1.19 is a unique chemico-physical property of the mixture, known as “laminar flame speed.” A typical laminar flame structure is shown in Figure 1.4. Due to a high concentration of radicals in the reaction zone, and the steep gradient of concentrations towards the preheat zone, light species (e.g., H, H2, O, and OH) diffuse back into the preheat zone and partially react with fuel and oxygen. These reactions are responsible for the partial combustion energy release in the preheat zone. For this reason, the addition of hydrogen to a fuel mixture can enhance the flame speed, as it promotes the chemistry in the preheat zone. In contrast, absorption of the highly reactive hydrogen atom in stable molecules or less-reactive radicals may result in a lowering of the flame speed. The important reactions that hydrogen atom can undergo are: H þ O2 ¼ OH þ O H þ O2 þ M ¼ HO2 þ M

Figure 1.4 Structure of a one-dimensional laminar premixed flame.

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The first reaction – “chain branching” – has a high activation energy, which makes it temperature-sensitive. It is, therefore, faster within the reaction zone. The second reaction, however, is a three-body reaction and is almost temperature-insensitive and rather, as mentioned earlier, is highly pressure-sensitive. These reactions are favored in the recombination direction as the pressure is elevated. As the pressure is raised, the rate of conversion of active hydrogen radicals to less-active hydroperoxy radicals within the preheat zone is increased, and both the flame speed and flame thickness are reduced. Flame speed can also be reduced by adding flame inhibitor chemicals, such as I2 and Br2 which, once dissociated to I and Br radicals, will act as a sink for hydrogen by forming stable I–H and Br–H molecules. 1.4.1 Governing Equations

In the absence of body forces and external energy sources, and also in the limit of lowMach number flows, the momentum equation can be neglected and the equations governing the propagation of a laminar flame can be written as [30]: .

Mass balance qr qru þ ¼0 qt qx

.

Species mass balance qrYi q ðrðu þ Vi ÞYi Þ ¼ v_ i Mi ; þ qx qt

.

ð1:20Þ

Energy balance

Nsp P i¼1

i ¼ 1; . . . ; Nsp
1

ð1:21Þ

Yi ¼ 1

    Nsp X qT qT q qT qT þu ¼
K
r rcp hi v_ i Mi þ qt qx qx qx qx i¼1

Nsp X

! cp;i Yi Vi

i¼1

ð1:22Þ

where, r, u, Vi , p, Mi , Yi and cp , are mixture density, convective velocity, diffusion velocity, pressure, molecular mass, mass fraction, and heat capacity at constant pressure of species i, respectively. K is the thermal conductivity of the mixture and v_ i is the net molar rate of consumption of species i. These equations are closed when using the Fick’s law for the diffusion velocities: Vi ¼
rDim

qYi qx

ð1:23Þ

where Dim is the diffusion coefficient of species i into the rest of the mixture. A concise discussion of the difference between binary and multicomponent diffusion coefficients can be found in Ref. [2]. Equations 1.20–1.22 describe the dynamics of the

1.4 Laminar Premixed Flames

flame propagation until it reaches the steady-state flame speed SL . Several standard software packages are available that can be used to solve these equations, among which PREMIX [31] is the most widely used. While it is possible to solve numerically for the flame structure using detailed kinetic mechanisms, several approximate mathematical techniques have also been developed to solve these equations. An excellent detailed description of different analytical methods for solving laminar premixed flame structure can be found in Ref. [17]. 1.4.2 Experimental Approach

Laminar burning speed is a fundamental thermo-physico-chemical property of each fuel–air mixture, which depends only on the temperature, pressure, and mixture composition. It characterizes the rate at which the unburned reactants are consumed or, equivalently, the production rate of the burned gas. As laminar burning speed is a mixture property, its measurement is of fundamental importance in several respects. Of particular interest in practical applications is to know how fast the flame will propagate within an internal combustion engine. It may also serve as a benchmark data for testing the predictive capabilities of chemical kinetic mechanisms developed for several fuels over a wide range of thermodynamic conditions (pressure and temperature). The reliability of such data depends critically on the accuracy of the measurements and the experimental methods. The accuracy requirement of the laminar burning speeds becomes far more demanding at higher temperatures and pressures, under which conditions gas turbine combustors and internal combustion engines operate. Another application of laminar burning speed measurement is in turbulent flame speed correlations. Abdel-Gayed and Bradley, [32], have shown that the turbulent flame speed of a mixture can be estimated reasonably accurately by knowing the laminar burning speed of the mixture at the corresponding temperature and pressure [32]. Consequently, these authors proposed a correlation with the following form:  0 n ST u ¼ 1þa ð1:24Þ SL SL where ST and SL are the turbulent and laminar burning speeds, respectively, a and n are model constants, and u0 is the turbulent fluctuations velocity. A thorough explanation of theoretical basis of turbulent combustion can be found in Chapter 9. 1.4.2.1 Laminar Burning Speed Measurement Techniques Laminar burning speed measurement is a sensitive process, which requires careful design of the experimental apparatus and accurate data acquisition system. Some of the more advanced methods to measure this parameter are described in the following section. 1.4.2.1.1 Counter Flow Flame Methods In this method, two opposing nozzles are used to obtain a plug flow, in which configuration two streams of fresh reactants are

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Figure 1.5 Configuration of counter flow twin flames.

impinged against each other so as to produce two stationary flames and a stagnation point; this configuration is termed “counter flow twin flames.” A second version involves one nozzle blowing hot inert gases, while another nozzle drives out any unburned premixed gases. The advantage of the counter flow twin flame is that it lowers the aerodynamic stretch effects; the configuration is shown in Figure 1.5. In this technique, the laminar burning speed is obtained by precisely mapping the flow velocity field using an optical method such as digital particle image velocimetry (DPIV). The velocity field is composed of axial and radial components; the gradients in the axial and radial components introduce stretch and strain effects, which could considerably affect the burning speed. After mapping the velocity field by using the DPIV method, the minimum axial velocity is defined as the reference “stretched flame speed.” The stretch rates at the reference point are then modified by changing the upstream velocity. After having achieved the burning speeds at different stretch rates, the laminar burning speeds can be defined by extrapolating the stretched burning speeds to zero-stretched burning speed [32, 33]. 1.4.2.1.2 Outwardly Propagating Constant Pressure Spherical Flame Method In this method, an outwardly propagating spherical flame is used to measure the laminar burning speed [34–37], with the pressure and temperature of the unburned gas remaining constant while the flame expands. In order to achieve this, a spherical vessel with optical windows is located in a Schlieren set-up to record the location of the flame front, and consequently to measure the flame front speed, Sf . A typical snapshot of a propagating spherical flame is shown in Figure 1.6, where the stretched laminar burning speed can be defined as:   rb ð1:25Þ Sn ¼ Sf ru

Here, Sn is the stretched laminar burning speed, rb is the density of burned gas, and ru is the density of the unburned gas zone. The stretch rate in spherical flames is

1.4 Laminar Premixed Flames

Figure 1.6 Outwardly propagating spherical flame in a spherical vessel.

k¼

1 dA 2 dr 2 ¼ ¼ Sf A dt r dt r

ð1:26Þ

which is a function of flame radius, r and flame speed, Sf. After measuring Sn , it is plotted against stretch rate k, and the zero-stretch laminar burning speeds are then obtained using an extrapolation method. The predictions of this method are most reliable when flame radius is too large for stretch effects to be unimportant. Extrapolation of Stretched Laminar Burning Speeds In the methods explained above for burning speed measurement, stretch is important; hence, an extrapolation process is required to obtain a zero-stretch laminar burning speed. Especially in counter flow twin flames, the stretch rate is high, usually greater than 300 s
1. Consequently, different extrapolation functions are normally used, including linear, polynomial, and logarithmic functions [38–40], each of which provides a different prediction for the zero-stretch laminar burning speed. As yet, however, there is no consensus as to which method gives the most accurate prediction. 1.4.2.1.3 Flat Flame Burner Method In this method, a flat flame burner is used to produce a perfect unstretched laminar flame [41, 42]. In fact, the burning speed can correlate with stream velocity. However, as a large heat transfer occurs from the flame to the burner and cooling water, the results obtained must be corrected. Unfortunately, when using this method the laminar burning speeds can be determined over only a limited range of temperatures and pressures. 1.4.2.1.4 Constant Volume Spherical Vessel Method In this technique, the propagating flame compresses the unburned gas isentropically. The main advantages of this method compared to others are:

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. .

Laminar burning speeds can be measured at high temperatures and pressures. The stretch effects are very small due to large flame radii and, therefore, no zerostretch extrapolation is required.

Spherical chambers have been used by Metghalchi and Keck [41] to measure burning speeds for a wide range of fuels, equivalence ratios, diluents concentrations, pressures, and temperatures. A more comprehensive explanation of this approach is presented in the following section. 1.4.2.1.5 Thermodynamic Model The theoretical model used to calculate the burning speed from the pressure rise is based on that previously developed by Metghalchi and Keck [41], but has been modified to include corrections for energy losses due to electrodes and radiation from the burned gas to the wall, as well as the temperature gradient in the preheat zone. In this case, it is assumed that gases in the combustion chamber can be divided into burned and unburned gas regions, separated by a reaction layer of negligible thickness. The burned gas in the center of chamber is divided into n shells, where n is proportional to the combustion duration. Although the burned gas temperature of each shell is different, all of the burned gases are in chemical equilibrium with each other. The burned gases are surrounded by a preheat zone having a variable temperature, and this is itself surrounded by unburned gases. A thermal boundary layer separates the unburned gas from the wall. The effect of energy transfer from the burned gas to the spark electrodes is considered by a thermal boundary layer. A schematic of the model used for this method is shown in Figure 1.7, and further details are available in Refs [43, 44]. The system of equations comprises the constancy of volume and energy balance, namely xðb

ðvbs ðT 0 ; pÞ
vus Þdx0 ¼ vi
vus þ

1 ðVeb þ Vwb þ Vph Þ m

ð1:27Þ

ebs ðT 0 ; pÞ
eus Þ dx0 ¼ ei
eus þ

  1 pVph
Q r m cu
1

ð1:28Þ

0 xðb

0

where vi ¼ ðVc
Ve Þ=m and ei ¼ Ei =m are the initial specific volume and energy of the unburned gas in the chamber, vbs is the specific volume of isentropically compressed burned gas, and vus is the specific volume of isentropically compressed unburned gas. Vwb , Vph and Veb are the displacement volume of the wall boundary layer, the displacement volume of the preheat zone ahead of the reaction layer, and the displacement volume of the electrode boundary layer, respectively. ebs , eus , cu and Q r are the specific energy of isentropically compressed burned gas, specific energy of isentropically compressed unburned gas, specific heat ratio of unburned gas and radiation energy loss from the burned gas zone, respectively. The above equations have been solved for two unknowns; burned mass fraction and the burned gas temperature of the last layer. Given pressure, pðtÞ, as a function of time, the equations can be solved numerically using the method of shells to obtain the burned mass

1.4 Laminar Premixed Flames

Figure 1.7 Outwardly propagating spherical flame in a spherical vessel; a two-zone model with thermal boundary layer.

fraction, xb ðtÞ, as a function of time and temperature distribution Tðr; tÞ. Ultimately, the burning speed may be defined as: Sb ¼

_b _ m m ¼ ru Ab ru Ab

ð1:29Þ

where Ab is the area of a sphere having a volume equal to that of the burned gas. In this method, the measured laminar burning speed data from spherical vessel (pressure method) can be fitted to the following power law relationship: h i T a  p b u Sb ¼ Sb 1 þ a1 ð1
wÞ þ a2 ð1
wÞ2 p Tu

ð1:30Þ

where Sb is the burning speed, at an arbitrary thermodynamic reference point ðp ; Tu Þ, in cm s–1. w is the mixture equivalence ratio, Tu is the unburned gas temperature (in K), and p is the mixture pressure in atmosphere. a1 ; a2 , a, and b are model constants that are different for the different mixtures. The values of the model constants and parameters for three hydrocarbon fuels, JP8, JP10, and ethanol, are listed in Table 1.1. Also shown (in Figure 1.8) are the model predictions for laminar burning speeds of JP8–air mixture at three different equivalence ratios, an initial temperature of 500 K, and an initial pressure of 1 atm.

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Table 1.1 Model constants and parameters of Equation 1.29 for three hydrocarbon fuels.

JP8 JP10 Ethanol

Sb (cm s–1)

a1

a2

a

b

T (K)

p (atm)

93.6 62 35.5

0.22 1.51
1.89


4.4
0.89 2.09

2.13 2.02 1.85


0.18
0.16
0.2

500 450 300

1 1 1

1.5 Diffusion Flames

In many practical combustion systems, the fuel and oxidizer (air) are not mixed before combustion. In these cases, the fuel and oxidizers mix due to convection or diffusion and the reaction takes place instantaneously; these flames are called diffusion flame or nonpremixed flame. Examples of diffusion flames are flames in furnaces, candle flames, ramjet, and jet engines. A typical one-dimensional diffusion flame, where the fuel and oxidizer flow in different directions, mix, and react, is shown in Figure 1.9. The energy of combustion is then transported in both directions into cold regions; profiles of the temperature and concentrations of fuel and oxidizer are shown in Figure 1.9. Diffusion flames may either be laminar or turbulent. As diffusion flames have been discussed in detail in many classical combustion texts [45–52], only laminar diffusion flames will be reviewed at this point. A classical example of a diffusion flame, initially presented by Burk and Schumann [53], describes the steady-state coaxial of a gaseous fuel issuing into an oxidizing environment. The flame shape and its height have been investigated, on

Figure 1.8 Laminar burning speeds of JP8–air mixtures at Ti ¼ 500 K, pi ¼ 1 atm, and equivalence ratios of 0.8, 0.9, and 1.0.

1.5 Diffusion Flames

Figure 1.9 Structure of a diffusion flame. (a) Physical condition of a nonpremixed flame; (b) Temperature and concentration profiles.

a theoretical basis, in this situation. One practically important aspect of a diffusion flame is that of droplet burning, which occurs in many combustors. In most devices, the fuel is sprayed into the combustor, where shear forces between the fuel and oxidizer cause the spray to break up into droplets. A typical spherical droplet combustion describing the liquid drop, fuel vapor, and reaction (flame) zone, is shown in Figure 1.10. The first stage is the evaporation of a droplet, which follows the d2 law that has been verified experimentally: ð1:31Þ

V Fue ap l or

Th En erm er al gy

Reaction Zone

A ir Th e En rm er al gy

d2 ¼ d2
Kv t

rs Droplet

Tboiling ρ l

Figure 1.10 Droplet combustion.

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where Kv is the evaporation coefficient, d is the diameter of liquid drop, and d is the initial diameter. The evaporation coefficient can be determined as: Kv ¼

8rl av lnð1 þ BÞ rv

ð1:32Þ

where rl , rv , and al are the density of the liquid and vapor, and the thermal conductivity at the surface of the drop, respectively. B is the Spalding transfer number, which is defined as: B ¼ b¥
bl ¼

YFl
YF¥ 1
YFl

ð1:33Þ

where b ¼ YF =ðYFl
1Þ, and b¥ ¼ YF¥ =ðYFl
1Þ. Also YFl is the mass fraction of fuel at the surface of the liquid, and YF¥ is the mass fraction of the fuel far away. The mass burning rate, flame position, and flame temperature of a single fuel droplet have been reviewed extensively by Kuo [45]. The mass burning rate is given by: _ F ¼ 4prl rl Dl lnð1 þ BÞ m

ð1:34Þ

where rl , rl , and Dl represent, respectively, the radius of the droplet, the density, and the diffusion coefficient at the droplet surface. The flame location can be determined as: rflame ¼

rl v2l Dl ln½1 þ ðF=OÞst YO¥ 

ð1:35Þ

where ðF=OÞst is the stoichiometric fuel–air ratio, YO¥ is the concentration of the oxidizer far away from flame, and vl is the flow speed at the surface, which can be obtained as: vl ¼

_F m 4prl2 rl

ð1:36Þ

The flame temperature can be calculated using the following relationship:   rflame vl b¥
bl þ 1 ¼ ln ð1:37Þ D bO;T
bl þ 1 where bO;T ¼

YO ðF=OÞst Dhr;F þ cp T Dhv þ ðF=OÞst YO;l Dhr;F

ð1:38Þ

A more in-depth discussion of the subject, droplet and spray combustion is presented in Chapter 7.

1.6 Conclusions

The fundamentals of combustion have been reviewed in this chapter. Combustion thermodynamics, chemical equilibrium, and time-dependent chemical kinetics in

References

general, and rate-controlled constrained-equilibrium in particular, have been described. The laminar premixed flame structure, and the experimental methods used to determine burning speeds have also been reviewed in depth. Finally, the details of diffusion flames have been presented under an extremely fast chemistry approximation. For reasons of space limitation in this chapter, many other subjects are reviewed in much greater detail in the following chapters of this volume.

References 1 Strehlow, R.A. (1984) Combustion 2 3

4

5

6 7

8

9

10

11

Fundamentals, McGraw-Hill. Turns, S.R. (2000) An Introduction to Combustion, McGraw-Hill. Kee, R.J., Rupley, F.M., and Miller, J.A. (1992) CHEMKIN-II: A Fortran Chem Kinetics Package for the Analysis of Gas Phase Chem Kinetics. Sandia National Laboratories, SAND89-8009B, UC-706. Gyftopolous, E.P. and Beretta, G.P. (2005) Thermodynamics, Foundations and Applications, Dover. Keck, J.C. (1990) Rate-controlled constrained-equilibrium theory of chemical reactions in complex systems. Prog. Energy Combust. Sci., 16, 125–154. Reynolds, W.C. (1987) STANJAN Program, 7:ME270. Gordon, S. and McBride, B. (1971) NASA Glenn Research Center, pp. NASA SP273. Janbozorgi, M., Ugarte, S., Metghalchi, H., and Keck, J.C. (2009) Combustion modeling of mono-carbon fuels using the rate-controlled constrained-equilibrium method. Combust. Flame, 156 (10), 1871–1885. Gregory, P., Smith, D., Golden, M., Frenklach, M., Moriarty, N.W., Eiteneer, B., Goldenberg, M., Bowman, C.T., Hanson, R.K.,Song,S.,Gardiner,W.C.,Jr,Lissianski, V.V., and Qin, Z. GRI-MECH 3.0. Westbrook, C.K., Pitz, W.J., Herbinet, O., Curran, H.J., and Silke, E.J. (2009) A comprehensive detailed chemical kinetic reaction mechanism for combustion of n-alkane hydrocarbons from n-octane to n-hexadecane. Combust. Flame, 156, 181–199. Tanaka, A., Ayala, P., and Keck, J.C. (2003) A reduced chemical kinetic model for

12

13

14

15 16

17 18

19

20

21

HCCI combustion of primary reference fuels in a rapid compression machine. Combust. Flame, 133, 467–481. Janbozorgi, M. and Metghalchi, H. (2009) Rate-controlled constrained-equilibrium theory applied to expansion of combustion products in the power stroke of an internal combustion engine. Int. J. Thermodyn., 12, 44–50. Eschenroeder, A.Q. (1967) Reaction Kinetics in Hypersonic Flows - Advances in Chemical Physics, vol. 13, John Wiley & Sons, Inc. Benson, S.W. (1952) The induction period in chain reactions. J. Chem. Phys., 20, 1605–1612. Benson, S.W. (1960) The Foundations of Chemical Kinetics, Krieger. Rein, M. (1992) The partial-equilibrium approximation in reacting flows. Phys. Fluids A, 4, 873–886. Law, C.K. (2006) Combustion Physics, Cambridge University Press. Lam, S.H. and Goussis, D.A. (1988) Understanding complex chemical kinetics with computational singular perturbation. Proc. Combust. Inst., 22, 931–941. Maas, U. and Pope, S.B. (1992) Simplifying chemical kinetics: Intrinsic lowdimensional manifolds in composition space. Combust. Flame, 88, 239–264. Oluwole, O.O., Bhattacharjee, B., Tolsma, J.E., Barton, P.I., and Green, W.H. (2006) Rigorous valid ranges for optimallyreduced kinetic models. Combust. Flame, 146, 348–365. Lu, T. and Law, C.K. (2005) Linear time reduction of large kinetic mechanisms with directed relation graph: n-Heptane and iso-octane. Combust. Flame, 144, 24–36.

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22 Ren, Z., Pope, S.B., Vladimirsky, A., and

23

24 25

26

27

28

29

30

31

32

33

34

Guckenheimer, J.M. (2006) The ICE-PIC method for the dimension reduction of chemical kinetics. J. Chem. Phys., 124, 114111. Keck, J.C. and Gillespie, D. (1971) Ratecontrolled partial-equilibrium method for treating reacting gas mixtures. Combust. Flame, 17, 237–241. Gardiner, W.C. (1999) Gas-Phase Combustion Chemistry, Springer. Pilling, M.J. (1997) Comprehensive Chemical Kinetics, Low-Temperature Combustion and Autoignition, Springer. Clarke, J.F. (1989) Fast flames, waves and detonations. Prog. Energy Combust. Sci., 15, 241–271. Makhviladze, G.M. and Rogatykh, D.I. (1991) Nonuniformities in initial temperature and concentration as a cause of explosive chemical reactions in combustible gases. Combust. Flame, 87, 347–356. Zeldovich, Y.B., Librovich, V.B., Makhviladze, G.M., and Sivashinsky, G.I. (1970) Development of detonation in a non-uniformly preheated gas. Astronautica Acta, 15, 313. Zeldovich, Y.B. (1980) Regime classification of an exothermic reaction with nonuniform initial conditions. Combust. Flame, 39, 211. Poinsot, T. and Veynante, D. (2005) Theoretical and Numerical Combustion, Edwards. Kee, R.J., Smooke, J.F., and Miller, J.A. (1985) PREMIX: A Fortran program for modeling steady laminar flames. SAND85-8240, Sandia National Laboratories. Abdel-Gayed, R.G. and Bradley, D. (1989) Combustion regimes and the straining of turbulent premixed flames. Combust. Flame, 76, 213–218. Bradley, D., Lawes, M., and Mansour, M. (2009) Explosion bomb measurements of ethanol/air laminar gaseous flame characteristics at pressures up to 1.4 MPa. Combust. Flame, 7, 1462–1470. Bradley, R.A., Hicks, M., Lawes, C.G.W., and Sheppard, R.W. (1998) The measurement of laminar burning velocities and Markstein numbers for

35

36

37

38

39

40

41

42

43

44

45

iso-octane/air and iso-octane–n-heptane/ air mixtures at elevated temperatures and pressures in an explosion bomb. Combust. Flame, 115, 126–144. Leylegian, J., Sun, H., and Law, C.K. (2005) Laminar flame speeds and kinetic modeling of hydrogen/chlorine combustion. Combust. Flame, 143, 199–210. Kelley, J. and Law, C.K. (2009) Nonlinear effects in the extraction of laminar flame speeds from expanding spherical flames. Combust. Flame, 156 (9), 1844–1851. Tahtouh, T., Halter, F., and MounamRousselle, C. (2009) Measurement of laminar burning speeds and Markstein lengths using a novel methodology. Combust. Flame, 156 (9), 1735–1743. Tien, J. and Matalon, M. (1991) On the burning velocity of stretched flames. Combust. Flame, 84, 238–248. Sher, E. and Ozdor, N. (1992) Laminar burning velocities of n-butane/air mixtures enriched with hydrogen. Combust. Flame, 89, 214–220. Bosschaart, K.J. and de Goey, L.P.H. (2004) The laminar burning velocity of flames propagating in mixtures of hydrocarbons and air measured with the heat flux method. Combust. Flame, 136, 261–269. Metghalchi, M. and Keck, J.C. (1982) Burning velocities of mixtures of air with methanol, isooctane, and indolene at high pressure and temperature. Combust. Flame, 48, 191–210. Parsinejad, F., Arcari, C., and Metghalchi, H. (2006) Flame structure and burning speed of JP-10 air mixtures. Combust. Sci. Technol., 178, 975–1000. Rahim, F., Eisazadeh Far, K., Parsinejad, F., Andrews, R.J., and Metghalchi, H. (2008) A thermodynamic model to calculate burning speed of methane-airdiluent mixtures. Int. J. Thermodyn., 11 (4), 151–161. Parsinejad, F., Arcari, C., and Metghalchi, H. (2006) Flame structure and burning speed of JP-10 air mixtures. Combust. Sci. Technol., 178, 975–1000. Kuo, K.K. (1986) Principles of Combustion, John Wiley & Sons, Inc.

References 46 Bradley, D. (1979) Flames and Combustion 47 48

49 50

Phenomena, Chapman & Hall. Chigier, N. (1979) Energy, Combustion and Environment, McGraw-Hill. Kanury, A.M. (1975) Introduction to Combustion Phenomena, Gordon and Breach. Strahle, W.C. (1993) Introduction to Combustion, Gordon and Breach. Warnatz, J., Maas, U., and Dibble, R. (2001) Combustion: Physical and Chemical

Fundamentals, Modeling and Simulation, Experiments, Pollutant Formation, Springer-Verlag. 51 Williams, F.A. (1983) Combustion Theory, Addison Wesley. 52 Glassman, I. (1996) Combustion, Academic Press. 53 Burke, S.P. and Schumann, T.E. (1928) Diffusion flames. Ind. Eng. Chem., 29 (20), 998–1004.

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2 Combustion Chemistry Ravi Fernandes

2.1 Introduction

Combustion science involves complex interactions between different fields, such as thermodynamics, chemical kinetics, fluid mechanics, heat and mass transfer and turbulence. Consequently, the solutions to combustion problems involve much effort using both theoretical formulations and experimental investigations. In this chapter, combustion science will be examined from a chemical perspective, focusing mainly on chemical kinetics and its applications in combustion modeling. The objective of combustion modeling is to simulate a combustion process and to develop predictive capabilities of its behavior under varied combustion conditions for diverse applications in combustion. The details of combustion modeling are provided in Chapter 8, and are beyond the scope of this chapter. Chemical kinetics forms an important part of combustion science in solving problems in combustion; examples include the formation of pollutants, ignition events in combustors, engine knocks, explosions, detonations, and so on. Chemical kinetics in general deals with the quantitative study of rates of chemical reactions, and of the factors on which they depend. It is the study of the rates and mechanism by which one chemical species is converted to another. All chemical reactions take place at a definite rate, which depends in turn on the factors such as concentrations of the chemical species, the temperature and pressure, the presence of catalyst, and effects due to radiation. More details on chemical kinetics in general can be obtained from other books on chemical kinetics, such as those listed in Refs [1, 2]. A combustion reaction in general terms can be described as a chemical reaction in which a fuel combines with an oxidizer (usually oxygen from air) to form products. Fuels may be classified into three types, namely solid, liquid, and gaseous: .

Solid fuels include wood, charcoal, coal, solid propellants (as used in rocket technology). Coal is one of the major solid fuels that is still used today in electricity generation (see Vol. 4 Ch. 6). The details of solid fuels will be described at length in Vol. 4 Ch 1 and Ch. 2.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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.

.

Liquid fuels form the major class of present-day automobile and conventional transportation fuels, such as diesel, gasoline, kerosene, biodiesel, and alcohols. Most liquid fuels are derived from fossil sources. Gaseous fuels include natural gas, which is composed primarily of methane with other components such as ethane, propane, butanes, and higher hydrocarbons. Natural gas is used particularly in gas turbines. (Further information on gaseous fuels is provided in Vol. 3 Ch.1).

Now, let us consider an example of a combustion reaction wherein the fuel, for example methane (CH4), combines with oxygen (O2) to form carbon dioxide (CO2) and water (H2O): CH4 þ 2O2 ! CO2 þ 2H2 O

ð2:1Þ

For the above reaction in 2.1, the components on the left side of the arrow constitute the reactants (CH4 and O2), while those on the right side constitute the products (CO2 and H2O) of the reaction. The stoichiometric coefficients 2, 1, and 2 denote the number of moles of oxygen, CO2 and water, respectively, that participate in the combustion of 1 mole of methane, and these are used to balance the chemical reaction. Moreover, molecules and atoms are conveniently counted in terms of moles, where 1 mole (1 mol or 1 g-mol of compound equals 6. 02 252
1023 molecules (or atoms). In this context, it is also important to define the term heat of combustion (DHc0 ). This is defined as the energy released when 1 mol of fuel undergoes complete combustion with the oxidizer, and is expressed in terms of energy per mole (J mol1) of fuel; it is calculated as the difference between the heat of formation (D fH0) of the products and the reactants. Further details are available in Chapter 6, describing thermodynamics. In the present chapter, terms such as fuel–oxidant ratio, equivalence ratio, and stoichiometry will also be encountered; these are defined as follows: .

The fuel–oxidant ratio (F/O), which is also known as the air–fuel ratio (AFR), is defined as the ratio of the masses of the fuel to the oxidant, and given as: F mass of fuel ¼ O mass of oxidant

.

ð2:2Þ

The equivalence ratio, w, is defined as the ratio of the actual fuel–oxidant ratio to the fuel–oxidant ratio under stoichiometric conditions, or complete combustion. The equivalence ratio is represented on a molar basis as in Equations 2.3a and 2.3b, where n is the number of moles. w¼

ðnfuel =noxidant Þact ðnfuel =noxidant Þst

ð2:3aÞ

It is important to note that w is related to lambda (l), as: w¼

1 l

ð2:3bÞ

2.1 Introduction

which is another measure of the stoichiometry of a mixture. As for most practical applications, either the amount of residual oxygen or the amount of unburned hydrocarbons in exhaust gases is measured, for which the lambda values seem much easier to correlate. So, for stoichiometric conditions, w ¼ 1. The reaction in Equation 2.1 is stoichiometric, wherein the oxygen present is sufficient enough to convert the fuel so as to completely yield CO2 and H2O, by which maximum heat is released and maximum chemical energy is available for mechanical work. When 0 < w < 1, the situation is called fuel–lean (if there is an excess of oxidizer) such as: CH4 þ 3O2 ! CO2 þ 2H2 O þ O2

ð2:4Þ

whilst when 1 < w < 1, the situation is termed fuel–rich (if there is excess of fuel) such as 1:4 CH4 þ 2O2 ! CO2 þ 2H2 O þ 0:4CH4 .

ð2:5Þ

Stoichiometry, y, is defined as the ratio of the mole percent of the fuel in the combustible mixture to the mole percent of the fuel under stoichiometric conditions; in other words: y  Xfuel =Xfuel;st

ð2:6Þ

where Xfuel is the actual mole fraction of the fuel, and Xfuel;st the mole fraction of the fuel in a stoichiometric mixture. The mole fraction of the fuel in a stoichiometric mixture with oxygen (if the chemical equation is written to describe exactly the reaction of 1 mole of fuel) can be calculated as: Xfuel;st ¼

1 1 þ nO2 ;st

ð2:7Þ

where nO2 ;st refers to the number of moles of oxygen under stoichiometric conditions. It is important to note that, although the equivalence ratios (w) and stoichiometry (y) are closely related to each other, they are not identical. Under ideal conditions, hydrocarbon (fuel) combustion is the disassembly of molecules into an eventual conversion to CO2 and water, and could be summarized in a single step as described in Equation 2.1. However, in reality, the reaction pathways that facilitate combustion are complex and involve an immense network of reaction steps. Each of these reaction steps can be represented in a similar form of expression (as in Equation 2.1) at the molecular level, to describe molecular events that are responsible for the observed changes. The reaction of hydroxyl radicals (OH) with molecular hydrogen to form water and hydrogen atoms is an example of such a molecular event: OH þ H2 ! H2 O þ H

Reaction 2.8 is referred to as a reaction step or an elementary reaction.

ð2:8Þ

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A set of several reaction steps constitutes a reaction mechanism. Combustion reactions occur because several elementary reactions combine to produce the transformation of fuel and the oxidizer to form combustion products. Determining the mechanism of a reaction is more difficult than might be thought, and may require contributions from experimental investigations and detailed theory in understanding such processes. Within the context of combustion chemistry and chemical kinetics, terms such as free radicals may be encountered. These are highly reactive species such as atoms (H, O, N, Cl, Br) or radical species (OH, CH, CH3). These chemical species have unpaired electrons and can very readily react with other molecules, or even with themselves (radical self-reactions). In chemical kinetics, the reaction rate is defined as the derivative of the concentration of a species with respect to the reaction time involved. Thus, in Equation 2.8 the increase in concentration of H, or the decrease in concentration of OH per unit time due to this reaction, would be the measure of its rate. To explain this in more detail, let us consider a reaction of the type: kf

A þ B ! C þ D

ð2:9Þ

In this case, the reaction rate (considering the consumption of species A in the reaction) can be written as: d½A=dt ¼ kf  ½Aa  ½Bb

ð2:10Þ

where a and b are the reaction orders with respect to the species A and B, and kf is called the rate coefficient. The overall reaction order can be expressed as the sum of both the exponents a and b. In most cases, however, some species participating in a reaction step are in excess, in which case the change in its concentrations is negligible during the reaction. This allows one to determine the effective rate coefficient from the rate coefficient, with near-constant concentrations of the species in excess (e.g., species B); then, by using k1 ¼ k [B], it is possible to write this in simplified form as: d½A=dt ¼ k1  ½Aa

ð2:11Þ

The temporal change of the concentration of species A can be calculated by integrating this differential equation. Elementary reactions of the type in Equation 2.9 are called bimolecular elementary reactions, because they involve two reactants (atoms, molecules, radicals). Unimolecular reactions describe the rearrangement or a dissociation of a molecule such as: A ! Products

ð2:12Þ

Unimolecular rate coefficients also depend on the total molar concentrations [M], and are also termed bath gas densities in gas-phase reactions. The rate coefficients therefore could be explicitly written as k(T ) or k(T, [M]), rather than k alone. Rate coefficients are not constants (as the name might suggest), and do in fact change by orders of magnitudes, taking into account the variations in temperature throughout

2.1 Introduction

the regions of the flame in a combustion process. Thus, it is essential to note these dependencies in an explicit fashion. Termolecular reactions are usually recombination reactions of the type: H þ CO þ H2 O ! HCO þ H2 O

ð2:13Þ

In combustion processes, the concentration of a given species (atom, radical, molecule) is affected by a number of elementary reactions. The rate of change in the concentration of each species must therefore be determined by adding up the effects of all elementary reactions that produce a species, and by subtracting from the elementary steps that consume it. Elementary reactions may proceed in either direction; thus, a reverse of the reaction in Equation 2.9 is also possible, for which the rate law can be expressed as follows: d½A=dt ¼ kr  ½Cd  ½De

ð2:14Þ

At chemical equilibrium, the forward and backward reactions will have the same rate on a microscopic level. However, on a macroscopic level no net reaction can be observed. Thus, at chemical equilibrium we have: kf  ½Aa  ½Bb ¼ kr  ½Cc  ½Dd ½Cc ½Dd a

½A ½B

b

¼

kf kr

ð2:15Þ

ð2:16Þ

This expression corresponds to the equilibrium constant of the reaction, which can be calculated from thermodynamic data (as discussed in Chapter 6), which derives an important relationship between the rate coefficients of the forward (kf) and reverse reactions (kr). kf ¼ Kc kr

ð2:17Þ

This fundamental relationship is a form of the principle of detailed balancing, which allows the rate-coefficient ratio to be related to thermodynamic quantities. For this, the van’t Hoff isochore must be used (see Chapter 6): d ln Kp =dð1=TÞ ¼ DH0 =R

ð2:18Þ

where Kp is the equilibrium constant in terms of partial pressures, and DH0 is the standard enthalpy change of the reaction. By using the integrated form of the van’t Hoff isochore (refer to Chapter 6), ln Kp ¼ DG0 =RT ¼ DS0 =RDH 0 =RT

ð2:19Þ

Accurate thermodynamic data therefore can be used for modeling the kinetics of reactions where both the forward and reverse directions of the same elementary

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reaction occur. This is especially common in high-temperature combustion reactions, where even those with large DH0 values proceed close to equilibrium conditions. For practical combustion modeling, the two opposing directions are considered separately; then, if one of these is insignificant it may be omitted for consideration. One type of reaction that frequently occurs in combustion is the chain reaction. Such reactions consist of a series of consecutive, competitive and opposing reaction steps with variable reaction rate constants. Among this class of reactions one can include: chain-initiation reactions, where free radicals are produced in a reaction step; chain-terminating reactions, where free radicals are consumed or destroyed to form stable species, thereby terminating the chain; and chain-propagating reactions, which are elementary reactions wherein the ratio of free radicals in the product and the reactant equals 1; as a consequence, the chain process is continued – hence this reaction may also be referred to as chain-carrying. However, if the ratio of the radicals is greater than 1, then chain-branching processes will occur. Chain reactions have great significance in combustion chemistry, since explosions, engine knocking, cool flames, and auto-ignition phenomena are all related to this class of reaction. Details of chain reactions in the low-temperature oxidation of hydrocarbons will be discussed later in this chapter.

2.2 Temperature Dependence of Rate Coefficients

The rate coefficients depend heavily on temperature, in a non-linear fashion. Such dependence on temperature is often described by a simple representation called Arrhenius law, as derived by Svante Arrhenius in 1889 [3], and which describes the temperature dependence of k as:   Ea 0 ð2:20Þ k ¼ A  exp  RT However, measurements performed with high accuracy over a wide temperature range have shown that the temperature dependence of A0 , the pre-exponential factor, is usually small in comparison to the exponential dependence. Therefore the preexponential factor is modified to the product of AT b, which includes the effect of collision terms and the steric factor associated with the orientation of the colliding molecules. The rate coefficient can therefore be represented as:   Ea ð2:21Þ k ¼ A  T b  exp  RT where R is the gas constant and Ea is the activation energy; these correspond to an energy barrier that must be overcome during the reaction, as shown in Figure 2.1. In the case of dissociation reactions, the activation energy is close to the bond energy of the bond which is split.

2.3 Pressure Dependence of Rate Coefficients

Energy

A+B Ea(f) reactants Ea(r)

∆Hf C+D products Reaction coordinate

Figure 2.1 Energy diagram of an exothermic reaction of the type A þ B ! C þ D. The reaction coordinate is the path of minimum potential energy from reactants to products with changing interatomic distance.

Based on the figure above, it is seen that the potential energy of the reactants, Ereactants > Eproducts, and therefore the reaction is termed exothermic. It should also be noted that the activation energies for the forward and reverse reactions are not equal; that is, the two reactions have different specific rate constants. In addition, DHr refers to the enthalpy of the reaction, and is negative for exothermic reactions (this will be discussed further in Chapter 6).

2.3 Pressure Dependence of Rate Coefficients

The pressure dependence of reactions can be understood through the Lindemann model [4, 5], which characterizes the types of elementary processes involved in a simple reaction mechanism of unimolecular reactions. A unimolecular dissociation reaction comprises of three steps: (i) collisional activation to form excited (energized) molecules AB (Equation 2.22a); (ii) collisional deactivation (Equation 2.22b) by losing excess energy through collisions with surrounding molecules; and (iii) unimolecular fragmentation (Equation 2.23a) to form products: AB þ M ! AB þ M

k8

ð2:22aÞ

k8

ð2:22bÞ

AB þ M ! AB þ M k9

AB ! A þ B

ð2:23aÞ

Similarly, the reverse reactions can be considered, which would be the recombination of A and B, and comprise an association step that forms AB , redissociation of

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the activated species AB , and the collisional deactivation step: k9

A þ B ! AB

ð2:23bÞ

k9

AB ! A þ B

ð2:23aÞ

k8

AB þ M ! AB þ M

ð2:23bÞ

In case of dissociation, reaction only occurs after the molecule AB has gained enough energy through collisions to rupture the bond adjoining A and B. However, in the recombination case, the molecule AB retains all the energy after the encounter until subsequent collisions with M (other molecules present, such as bath gas) allow it to dissipate the energy. The reaction scheme for dissociation and recombination reactions can be treated with the same formal rate coefficients, k8, k8, k9 and k9, and by considering “steadystate” conditions, the first-order rate coefficients can be defined which are [M]dependent for dissociation, as well as [M]-dependent second-order rate coefficients for recombination given by: kdiss  

1 d½AB 1 d½AB and krec  þ ½AB dt ½A½B dt

ð2:24Þ



  By using the steady-state assumption d½AB dt ¼ 0 to [AB ] in the mechanism above,

 kdiss ¼ k8 ½M

   k9 k8 ½M and krec ¼ k9 k9 þ k8 ½M k9 þ k8 ½M

ð2:25Þ

It is important to note that the terms in the brackets in Equation 2.25 become unity for dissociation at low pressures and for recombination at high pressures; they also tend to become zero for dissociation at high pressures and recombination at low pressures. By the principle of detailed balancing, kdiss and krec follow the equilibrium relationship where the equilibrium constant Kc is the product of the two equilibrium constants k8/k8 for the collisional activation/deactivation, and k9/k9 for the dissociation/association steps. From the above statements, for kdiss, in Equation 2.25 it can be shown that the rate coefficient is dependent on [M], which is therefore pressure-dependent. Furthermore, as krec is related to the dissociation rate constant through the equilibrium relationship, it also becomes [M]-dependent. A typical plot of rate coefficient dependence on pressure can be depicted as shown in Figure 2.2. This so-called “fall-off curve” describes the transition of the rate coefficient from the “low-pressure range,” where it is proportional to [M], to the “high-pressure range,” where it approaches a constant value which is the limiting rate coefficient denoted as k1 and is independent of bath gas density. The limiting rate coefficients are critical in characterizing the general position of the fall-off curves. Another parameter defined in the fall-off curves is the F-center or the “center of the fall-

2.3 Pressure Dependence of Rate Coefficients

log k

k∞

k0 k T1

T2

log [M] Figure 2.2 Representation of fall-off curves for a thermal unimolecular dissociation or recombination reaction for two different temperatures, T1 and T2.

off curve”; this can be defined as the concentration [M] at which the lines representing the limiting rate coefficients k0 (¼ k0 [M]) and k1 (as indicated in Figure 2.2) intersect. In the case of recombination at low pressures, the association and redissociation of AB are much more frequent than collisional stabilization, such that an equilibrium between A, B, and AB is established which can be described with the ratio k9/k9. In this case, the collisional stabilization (Equation 2.22b) becomes rate determining. At high pressures, the collisions occur so frequently that the association rate of the reactants A and B determines the recombination rate. Through detailed theories, it is possible to calculate the two rate constants k0 and k1 from the molecular parameters, and to provide functions for the “fall-off” range that are in close agreement with experiments. The empirical representation for the rate coefficients in the “fall-off” range was suggested [6, 7] by the following expression: krec =k1  ½x=ð1 þ xÞF

ð2:26Þ

with x ¼ k0 =k1 and F as the broadening factor. The broadening factor is dependent on x, the ratio of the limiting rate constants k0 and k1, and can be given as:  log x 2 1 1þ½ N  FðxÞ ¼ Fc ð2:27Þ where N ¼ 0.75–1.27 log Fc is the optimized fitting parameter, and Fc is a weak function of the temperature and of the nature of the bath gas M, which can be estimated by theory. An experimental representation of fall-off curves can therefore be characterized by three quantities, namely k0, k1, and Fc. At higher temperatures, the width as well as the asymmetry of the fall-off curves is increased, and additional approximations for asymmetry effects must be introduced to the fall-off curves.

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The above empirical equations have proven to have considerable advantages for use in modeling combustion reactions, as they are simple and realistic. Moreover, they are derived from the best available theories by empirical fitting [5]. Additional details to the Jroe formalism and the F-center treatment can be obtained in Refs [6–10]. As shown in the example (Figure 2.2), it is evident that the fall-off curves are different for temperatures T1 and T2 (where T2 > T1), and depend on the temperatures at different pressure. The quality of the “fall-off” analysis depends on the availability of experimental data at least for a limited set of conditions. Determining fall-off curves is never an easy task experimentally, because it can involve pressure ranges that may stretch over orders of magnitude and, indeed, present a major experimental challenge. However, by using experimental data, uncertain theoretical parameters can be fitted, and full density- [M] as well as temperature-dependent rate coefficients can be determined. Nonetheless, with such unavailability of reliable experimental data, the quality of ab intio predictions will depend on the accuracy of thermochemical data and structural parameters of the activated complex. Only recently have experiments been conducted over a very wide range of pressures close to 1000 bar and temperatures nearing 1000 K for important elementary reactions (e. g., H þ O2 þ M), and this has allowed the construction of fall-off curves by combining measurements made at both low and high pressures [11]. Even though the high pressure limit for this reaction has not been reached experimentally (even at 1000 bar), detailed theoretical and high-level calculations have shown very good agreement with the measurements, thereby allowing the construction of fall-off curves under wider ranges. Fall-off curves constructed over wider temperature ranges allow the successful modeling of combustion processes for a variety of applications of combustors operating under different conditions. Experiments performed under extreme pressures (as in Ref. [11]) help in overcoming the problem of the accurate determination of high-pressure-limiting rate constants, which otherwise would require extrapolation that often involves theory. The determination of the rate coefficients of elementary reactions continues to be a challenge, from both theoretical and experimental aspects, although significant progress has been made in this area over the past few years [12, 13]. Some of these challenges will be discussed in the following subsections of this chapter, together with details of the recent progress that has been made in understanding the chemistry in two important areas of combustion, namely auto-ignition chemistry and the chemistry of soot formation. An overview will be provided of the experimental techniques involved in understanding elementary chemical kinetics, and the modern tools available to probe this chemistry.

2.4 Experimental Techniques in Elementary Gas-Phase Kinetics

Various experimental techniques have been used to understand the kinetics of combustion reactions. From previous discussions, it is known how important

2.5 Shock Tubes

experimental results are in elementary reaction kinetics; developing a detailed reaction mechanism, and the subsequent forward numerical integration of this mechanism, is of assistance when determining the progress of a combustion process. From recent reviews [12, 13] on this subject, it appears that the recommended rate expressions have been derived predominantly from experiments, and that precise ab initio calculations of rate parameters achieve experimental accuracy only in exceptional cases. However, theory has identified major advancements over the years, especially with the availability of advanced computational resources. In fact, theory has proved capable of intrapolating and extrapolating rate data after parametric fitting, with the assistance of experimental data [12]. On the experimental side, the major experimental techniques involved in probing elementary gas-phase kinetics are laser flash photolysis (LFP) in static or flow reactors and shock tubes. A typical static reactor would be an isothermal vessel, filled with reactants at time zero, where the time behavior of the concentrations of the species is measured. In the case of flow reactors, a constant flow velocity allows the transformation of spatial profiles of the concentrations of species to temporal profiles [14]. In a flow reactor, the gaseous reactants may be flown in the reactor separately through controlled flows, or else a premixed mixture is prepared (usually in high-pressure experiments), allowed to homogenize, and finally passed into a reactor through controlled flows. In most cases, the reaction must be initiated by radical generation for which laser flash photolysis is a commonly used technique, with the choice of laser wavelength depending on the choice of radical precursor in the reaction. In some cases, free radicals are generated by using a microwave discharge [15] (e.g., H2, O2 to form H and O atoms). In the case of laser flash photolysis, radiation from a pulsed laser operating in the ultraviolet (UV) range is used to photodissociate a precursor so as to form the radical. For example, H atoms can be produced by the laser photolysis of NH3 at 193 nm [11] or, in some cases, a combination of both photolysis and reaction may be employed. An example of this is the use of photolysis to generate Cl atoms from Cl2 or (COCl)2, with the subsequent reaction of Cl with C2H6 to form C2H5 radicals [16]. Although the laser photolysis technique for radical generation is used at temperatures below 1000 K, it has a great advantage over single-shot experiments (such as shock tubes), due to the possibility of averaging on multiple shots and allowing a higher sensitivity in terms of the radical concentrations that can be used.

2.5 Shock Tubes

Kineticists use the shock tube as a wave reactor for obtaining kinetic data for high temperatures. The advantages of shock waves in the investigations of elementary reactions in combustion processes are based on their very rapid increases in temperature (<107 s), homogeneous heating, and the temperature and pressure variations that can be achieved over a wide range.

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A shock tube consists of a high-pressure section and a low-pressure section, with the two parts separated by a diaphragm. The driven or low-pressure section consists of the test gas under study. The high-pressure or driver section is filled with a gas (e.g., hydrogen) which, at a certain pressure, will abruptly rupture the diaphragm. The driver gas, which can be considered as a frictionless piston, moves into the lowpressure section in an accelerated motion, thereby compressing the test gas adiabatically. Due to the successive compression wavelets moving at higher velocities, the temperature of the gas is increased. Subsequently, a succession of these accelerations in the heated gas produces a series of increasingly rapid wavelets which overtake each other, producing finally a single abrupt change where all the changes coalesce. This so-called shock wave then moves into the undisturbed gas at a speed greater than the speed of sound in the initial unheated gas. When the shock wave reflects from a plane stationary endplate of the tube, the reflected wave heats the previously heated test gas to a higher temperature. The shock tube technique serves as an important experimental tool in reaction kinetics for investigating reactions under varying pressures and temperatures. Further details of the technique, and its use in chemical kinetics, are available in Refs [17, 18]. One important aspect when studying elementary reactions experimentally is that the particular reaction of interest must be isolated by minimizing the role of the secondary reactions (e.g., radical þ radical). In this way, the temporal profile of concentration of the species will be less affected by such reactions. Modern diagnostics provide a detailed picture of the chemical structure of combustion systems, and allow for the quantitative validation of combustion modeling that is essential when testing the reliability of combustion optimizations. Notably, these diagnostic techniques are led by a variety of performance requirements, such as the optimum use of fuel and reduction of emissions.

2.6 Detection Techniques in Combustion Kinetics

A variety of detection techniques are coupled with laser photolysis experiments, such as laser-induced fluorescence (LIF) (see Vol. 2 Ch. 7), which is commonly used for OH and other radical detection. This detection technique has proven successful even under extreme conditions of pressure ranging from a few bar to thousands of bar [19–21]. Many of the combustion species of interest possess a fluorescence spectrum in the UV range, which makes this technique highly feasible for studying elementary kinetics. Absorption-based measurements are also widely employed, ranging from UV to infrared (IR) absorption spectroscopy [11, 22–24]. Absorption spectroscopy has been improved over the past years by the introduction of cavity-enhanced and ring-down spectroscopy, in which the absorption signal is significantly enhanced by multiple reflections of a selected single-frequency laser radiation in an optical cavity, and

2.7 Low-Temperature Chemistry and Auto-Ignition

improving the signal-to-noise ratio (SNR) by multiple averaging of the signal [13]. The principles and details of these spectroscopic techniques are provided in Vol. 2 Ch. 5 and Ch. 7. Gas chromatography–mass spectrometry (GC-MS) is another frequently used technique [25] to understand overall mechanisms by following product detection and analysis. GC-MS also complements other spectroscopic measurements involved in elementary gas-phase kinetic studies, and aids in the development of detailed mechanisms. The GC-MS technique as used in combustion is described extensively in Vol. 2 Ch. 8. Recently, time-of-flight mass spectrometry (TOF-MS) has been employed in shock tubes [18], along with various absorption-based measurements [22–24]. One of the most powerful techniques developed recently to investigate elementary reaction kinetics and provide an understanding of the chemistry of flames by imaging combustion intermediates, is the combination of multiplexed mass spectrometry with photoionization by tunable-synchrotron radiation. This method has been used by research teams at the Sandia Laboratories, and by collaborators at the Advanced Light Source (ALS) of the Lawrence Berkeley National Laboratory [26, 27]. In these applications, multiple-mass detection and tunability of the synchrotron radiation allows experimental data to be acquired as a function of mass, photon energy, time after reaction initiation, and distance from the burner. The application of multiplexed synchrotron photoionization to elementary reaction kinetics permits the identification of time-resolved isomers (species with the same mass but different chemical structures), and their composition in reacting systems. Some applications of the above-described detection techniques in combustion chemistry are discussed in the following subsections.

2.7 Low-Temperature Chemistry and Auto-Ignition

Today, low-temperature oxidation chemistry is becoming increasingly important in combustion due to its links with auto-ignition. Moreover, research in this area has gained even more prominence due to the emergence of advanced engine technologies that rely on the compression-ignition of a premixed fuel–air charge. These new engine technologies target higher fuel efficiencies and lower emissions [28]. One important example of such a technology has been the development of the homogeneous charge compression-ignition (HCCI) engine concept, which operates on the principle of combining gasoline and diesel engines (see Vol. 5, Ch.1 for details of HCCI combustion). As with a spark-ignition engine, the HCCI engine operates with a premixed intake charge and like a diesel engine, it operates at a very lean fuel–air ratio, relatively lower temperatures, and higher pressures, where combustion occurs solely on compression-ignition. This technology aims at diesel-like efficiency and very low NOx due to lower combustion temperatures; however, under low load conditions the levels of both unburned hydrocarbons as well as carbon monoxide can rise significantly due to incomplete combustion processes [28]. Thus, one of the

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significant challenges facing HCCI engine technology is that of combustion control and ignition timing, and this is where fuel chemical kinetics has a central role to play. When a premixed charge is compressed to a suitable temperature it will spontaneously ignite; however, the ignition timing might not always coincide with optimal efficiency and controlled emissions. As the fuel kinetics controls the ignition, the fuel structure effects and subsequent “ignitability” can be very significant. Although a significant amount of research has been conducted in this field in an effort to optimize engine technology and design, newer challenges have been encountered due to changing fuels entering the fuel stream. Although engine technologies and the use of conventional fuels (e.g., gasoline, diesel) have been in concert for almost a century, this situation might be changed drastically due to the use of renewables, biofuels, and other alternative fuels (e.g., tar sands, heavy petroleum) entering the fuel stream. Therefore, details of chemical kinetics, and its dependence on fuel structure, will play a critical role in future engine research [30]. In this part of the chapter, attention will be focused on the “rich” chemistry of peroxy (RO2) radicals in low-temperature hydrocarbon oxidation, and their dominant role played in the auto-ignition system needed in engine technologies that rely on compression-ignition. The scheme shown in Figure 2.3 represents the general mechanism for lowtemperature hydrocarbon oxidation. The combustion processes at low temperatures present a much greater complexity when compared to the high-temperature combustion of hydrocarbons, which is generally dominated by atoms and small radicals produced by the pyrolysis and homolysis reactions of fuel molecules. Although, the heat release and temperature of the flame are controlled by the thermodynamics of the hydrocarbon, the kinetics and its dependence on fuel structure becomes less important.

Figure 2.3 General mechanism for low-temperature hydrocarbon oxidation.

2.7 Low-Temperature Chemistry and Auto-Ignition

A low-temperature oxidation mechanism, on the other hand, presents many interesting characteristics, and the radical generation and kinetics are closely linked to the chemical structure of the fuel or the parent hydrocarbon (this is in contrast to high-temperature kinetics) [29]. This mechanism involves both slow reactions that occur through straight-chain processes, and rapid runaway reactions due to the chain branching that leads to auto-ignition (as desired in HCCI engines). The scheme shown in Figure 2.3 provides a broader view of this mechanism. Here, the parent alkane (RH) reacts with radicals such as OH to form an alkyl radical (R) through an H-atom loss. The alkyl radical can react in different ways: (i) O2 addition to the alkyl leads to a peroxy radical (RO2); (ii) its decomposition leads either to the formation of smaller radicals (R1) and an alkene (A1) or to its self-reaction to form stable products (R2) or a reaction to produce HO2 and a conjugate alkene (A2) with the same number of C atoms as in the parent alkane. The HO2 species is relatively inert, and therefore the production of HO2 is termed as a “termination” step at low temperatures. The peroxy radical, formed through the reaction R þ O2, can undergo further internal hydrogen abstraction to form an alkylhydroperoxide (QOOH) with a new radical center. The QOOH may then cyclize to form ethers with ring sizes from three to six atoms, or form oxygenated products such as aldehydes and OH. As the hydroxyl radical (OH) is highly reactive it can propagate the chain further. On the other hand the QOOH, as also a radical species, can undergo further O2 addition and form an unstable intermediate called a peroxyalkylhydroperoxide (O2QOOH) which, on subsequent isomerization, can produce a nonradicalic R0 OOH and an OH radical. The dissociation of R0 OOH leads to the formation of two radicals (R0 O and OH), which largely contributes to chain branching. A hydroperoxide, R0 OOH can also be formed by H-abstraction by RO2 from the alkane (RH), thus providing another route for branching. The termination steps which either slow down or prevent this reaction are those which are located early in the mechanism, and which produce inert radicals such as HO2 and small alkyl radicals such as CH3. Any competition between the reactions of alkyl and peroxy will, therefore, have important consequences in that branching will be preferred and auto-ignition enhanced when peroxy radical isomerization and decomposition (PRID) is straightforward, since the route to branching will involve two such reactions, while that for termination will involve no reaction [12]. As discussed above, because the alkyl radicals lead to termination, while the peroxy radicals cause branching, at relatively higher temperatures the equilibrium of reaction (R þ O2 $ RO2) will be shifted to the left, and the termination reactions through alkyl radicals will dominate the branching reactions of RO2. In this way, the reaction will become slower and result in a region of negative temperature coefficient (where the rate of the reaction decreases as the temperature is increased) [13]. Hydrocarbon oxidation chemistry can be broadly classified into three important regimes, as depicted in Figure 2.4. Each of these dominated by small radical chemistry, HO2/H2O2 chemistry, and peroxy radical chemistry [12]. Hence, the mechanism for any hydrocarbon oxidation is very sensitive to both temperature and pressure. At high temperatures, branching is dominated by the H þ O2 reaction (above the dotted line in Figure 2.4), with the collisional stabilization and termination

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Figure 2.4 Temperature versus pressure plot for regimes dominated by chain-branching reactions in hydrocarbon oxidation.

step H þ O2 þ M (below the dotted line) being effective at low temperature. The boundary of this regime is therefore determined by the competition of these two reaction steps. Many experimental and theoretical studies have been conducted over the past few years on the reaction step (H þ O2 þ M), at various temperatures and pressures, with the most recent of these [11] reporting both the stand of theory and an agreement with experiments performed on the termination step over a wide range of temperatures (T ¼ 300 to 950 K) and pressures (P ¼ 2 to 950 bar). As this is one of the important reaction systems in hydrocarbon oxidation, it is essential that accurate rate data are acquired for validation in combustion models. As discussed above, the more complex regime is the low-temperature oxidation chemistry region, centered on peroxy chemistry. The boundary shown (the solid line in Figure 2.4) is for iso-octane as a reference alkane, and indicates the conditions when the rates of branching (via RO2) and of termination (via R) are equal. Below the line, the alkyl peroxy chemistry is dominant (this is of less significance above the line). However, the conditions might differ based on the fuel type; for example, with heptane the limiting line would lie above that of iso-octane. However, in the intermediate temperature regime, HO2 chemistry would dominate to form H2O2 (via the reactions HO2 þ H and HO2 þ HO2) that, eventually, would dissociate to form reactive OH radicals. The low-temperature oxidation mechanism involving the complex characteristics featuring peroxy-dominant chemistry has been well supported by indirect experimental investigations conducted at an early stage by Walker et al. [31]. Recent progress in this field has been demonstrated by the extensive studies of Taatjes and coworkers, who have carried out several key experiments involving laser photolysis,

2.8 Chemistry of Pollutant Formation in Combustion

coupled with UV- and IR-absorption detection for OH and HO2 species at low pressures [32], in addition to OH-laser-induced florescence techniques for high pressures [19] so as to acquire a deeper insight of the mechanisms. These experiments were supported by a detailed theory, which used high-level electronic structure calculations coupled with detailed master equation analysis [33]. Although it is beyond the scope of this chapter to describe the details of theoretical studies in the field of combustion kinetics, it would be worthwhile consulting the articles of Miller and coworkers in their investigations of some important combustion problems [33–35]. Kinetic measurements have also been performed by Taatjes and colleagues at the ALS, who have developed – and currently employ – photoionization MS to probe isomer-resolved kinetics and the mass spectrometric detection of combustion intermediates in flames, which together represent a very important tool for developing reaction models. Clearly, an integrated approach of theory, modeling, and experiments using advanced instrumentation, techniques and high-level theory and computational kinetics, is required to unravel the details of these chemical mechanisms. In addition to OH and HO2 studies of hydrocarbon mechanisms, it would also be important to probe QOOH secondary chemistry (which leads to branching), as present understanding in this area is very limited. Nonetheless, the present fundamental understanding of these important reactions has been greatly improved with advanced experiments and theory over the decades, although much remains unresolved. In future, state-of-the-art experiments will be required to target the isomerization and dissociation reactions, and the further addition to O2 to QOOH [36]. A related challenge lies in understanding energy-transfer processes in chemical kinetics, especially with regards to the increasing importance of high-pressure phenomena in advanced engine design. At present, pressure effects in gas-phase kinetics are represented via empirical parameterizations based on the direct measurement of inelastic processes of mostly stable species. At sufficiently high pressures, bimolecular reactions can interrupt the unimolecular decay of reaction products and adducts that have a relatively low importance at low pressures. Consequently, new approaches will be required to measure and detect the variety of species present under combustion conditions, along with high level theory that will enable fundamental investigations to be undertaken in this very important field of low-temperature oxidation chemistry. Clearly, this will open up new research opportunities, and these are discussed in the concluding part of the chapter.

2.8 Chemistry of Pollutant Formation in Combustion

As noted in the Introduction, under ideal combustion conditions the major products are CO2 and H2O. However in practical combustors – such as engines, gas turbines, and industrial furnaces – the conditions deviate from ideality, and additional byproducts such as NO and NO2 (collectively called NOx), carbon monoxide (see Vol. 2 Ch. 13), unburnt hydrocarbons and soot will appear. NOx is a major contributor of photochemical smog and ozone in the troposphere. Smog occurs as a result

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of branched-chain reactions in which the main carrier is the OH radical, and where NOx is an active participant that removes ozone from the stratosphere, resulting in an increased UV radiation reaching the Earth’s surface [14]. In addition, NO is responsible for the formation of tropospheric ozone, due to its reaction with peroxy radicals (e.g., HO2) that oxidize it to NO2. On photolysis, the NO2 forms NO and Oatoms, this being a critical step that leads to ozone formation. Recently, even CO2 has become a significant player in the “atmospheric balance,” there being increasing concerns regarding the global “greenhouse effect” (see Vol. 2 Ch. 14). Although nitrous oxide (N2O) also contributes to the greenhouse gas effect, there are no current regulations on N2O, as combustion is not believed to be a major source of its emission [14]. Recently, soot has become an unwanted byproduct of combustion, despite its various applications following changes to its properties. For example, soot was once valued for its heat- and light-producing properties, as well as for the manufacture of carbon-black, as toners for photocopiers, and as fillers in automobile tires. Yet, today soot is regarded as a major pollutant, due to adverse health effects associated with it and its precursors, such as polycyclic aromatic hydrocarbons (PAHs) [45] (see Vol. 2 Ch. 16). Whilst small soot particles may be dangerous if inhaled deeply into the lungs, the PAHs have found to be both carcinogenic and mutagenic [37]. Thus, the chemistry of rich flames – and especially those which have involved hydrocarbon growth into PAHs and soot – represents one of the most active research areas in combustion chemistry. The chemistry of both soot and NOx formation will be discussed briefly in the following section.

2.9 Formation of Soot from Aliphatic Fuels

The mechanism of soot and PAH formation from an aliphatic fuel, based on current knowledge, is shown schematically in Figure 2.5. When an aliphatic fuel is broken up due to pyrolysis or oxidative pyrolysis reactions, smaller hydrocarbon radicals such as acetylene (C2H2) and propargyl (C3H3) are formed. These radicals add to other hydrocarbons, or with themselves, to form higher hydrocarbon radicals, such that the growing radicalic hydrocarbons form aromatic rings that contain a sufficiently large number of carbon atoms. For example, the propargyl radical will self-recombine

Figure 2.5 Basic schematic for soot processes from gas-phase to particle agglomeration.

2.9 Formation of Soot from Aliphatic Fuels

to form C6H6 species that eventually lead to formation of the first aromatic ring [38, 39]. When the first aromatic ring has formed, a variety of routes has been proposed as to how the ring growth can progress, one example being via the further addition of acetylene C2H2 to the ring. In order to promote further growth, a hydrogen abstraction must occur so as to create a free radical; this takes place by an H-atom attack, followed by acetylene addition, as described by Frenklach and coworkers and referred to as the HACA (H-atom Abstraction and C2H2-Addition) mechanism [40, 41]. The formation of larger aromatic rings is believed to take place mainly through the addition of acetylene. While all of these events take place at the molecular scale, the third dimension originates via the coagulation of larger aromatics, forming primary soot particles that rapidly coagulate with molecules from the gas phase for surface growth. While such surface growth serves as the major pathway to the final soot concentration in sooting flames, it is the coagulation phase (transition from molecular scale to particle dimensions) that determines the final size of the soot particles [14]. The remedy to the appearance of soot and smoke is considered to relate to the temperature, time and turbulence; in other words, by allowing a longer time at a higher temperature, with good mixing, the oxidation of soot can be guaranteed [14]. Unfortunately, these conditions may also lead to a higher NOx formation. Consequently, it is essential that urgent research be undertaken to provide an understanding of both the NOx- and soot-formation processes. Indeed, extensive research is ongoing in an effort to optimize conditions in engines so as to reduce both of these pollutants, notably because the conditions that favor NOx control are undesirable for soot control, and vice versa. In understanding the formation of PAHs under combustion, a detailed knowledge of the elementary reactions forming the first aromatic ring is important, and an excellent description of the reaction pathways leading to PAH and soot has been provided [37]. At this point, the discussion is centered on the role of resonantly stabilized free radicals (RSFRs) in the first aromatic ring formation, as emphasized by Miller and Melius [39]. The propargyl radical is one such simple RSFR that differs from ordinary free radicals in the sense that one (or more) unpaired electron in a RSFR is delocalized, resulting in a higher stability than an ordinary free radical. This allows the RSFRs to survive within combustion environments, and their concentrations to be greatly increased as a result. The resonance structures of the propargyl radicals are shown in Figure 2.6. The higher stability of RSFRs manifests itself as reduced reactivity by forming weaker bonds with stable molecules such as O2 (which is a major oxidative pathway for most radicals). Due to their slower reactions with other molecules and their high concentrations in flames, the RSFRs tend to self-react, as in the case of propargyl

Figure 2.6 Resonance structures of the propargyl radical.

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radical, to form C6H6 species and this eventually leads to the first aromatic ring formation. Miller and Klippenstein have conducted extensive theoretical studies on the recombination reactions of RSFRs, such as allyl and propargyl radicals and the role that they play in soot formation [42].

2.10 Formation of NOx

The formation and control of NOx has long been an important topic of combustion research which requires an understanding of the interaction between chemical kinetics and fluid dynamics. To date, four different routes have been discussed in the formation of NOx [14, 43], namely the thermal NOx route, the prompt route, the N2O route, and the fuel-bound nitrogen route. 2.10.1 Thermal NO Route

Thermal NO, which is also known as Zeldovich-NO (after Zeldovich, who postulated this mechanism), is formed by the elementary reactions of N- and O-atoms, such as: (i) O þ N2 ! NO þ O; (ii) N þ O2 ! NO þ N; and (iii) N þ OH ! NO þ H. In this case, “thermal” refers to the high activation energy (319 kJ mol1) that is required in the first reaction, due to the strong triple bond in N2. This reaction can be sufficiently effective only at very high temperatures; moreover, due to its low rate it is also the rate-limiting step of thermal NO-formation. 2.10.2 Prompt NO

Prompt NO is also known as Fenimore NO, as postulated by C.P. Fenimore [44]. Based on Fenimore’s observations with several hydrocarbon flames, the NO profiles measured in the “post-flame” gases had nonzero intercepts, which led to the NO that appeared promptly being named as “prompt NO”. The mechanism of thermal NO (or Zeldovich NO) was too slow to explain these observations. It was suggested that the prompt NO was due to the CH radical, which previously was considered to be an unimportant transient species. Because C2H2 is accumulated under fuel-rich conditions (due to the CH3 recombination), this becomes a precursor for the CH radicals, where prompt NO is greatly favored in rich flames. Previously, it was suggested that the CH radical would react with N2 from the air, forming HCN þ N. However, there have been significant disagreements among theory and experiments (as reviewed in Ref. [12]), and it appears that prompt NO does indeed occur due to the CH þ N2 reaction, as suggested initially by Fenimore. However, the reaction produces NCN þ H rather than HCN þ N (as was imagined earlier), and the NCN formed further reacts to form NO. In contrast to thermal NO, prompt NO is formed at relatively low temperatures (ca. 1000 K).

2.11 Outlook

2.10.3 The N2O Route to NOx

The N2O route to NO formation is similar to that of the thermal NOx, wherein the N2 reacts with O in the presence of a third body, M, to form N2O as: N2 þ O þ M ! N2 O þ M

The N2O subsequently reacts with O atoms to form NO: N2 O þ O ! 2NO

This mechanism is not widely discussed as its does not make any significant contribution to the total NO. However, it could become important especially at low temperatures and lean conditions (which suppress CH formation), where the Zeldovich NOx and Fennimore NOx become insignificant. The N2O route is enhanced at high pressures, as it requires the third body for the stabilization and can become a major source of NO under lean premixed combustion in turbines [14]. 2.10.4 Fuel Nitrogen Route to NO

This route is mainly observed in coal combustion, since even “clean coal” contains a small percentage of chemically bound nitrogen. As this nitrogen is fuel-bound, the route is also known as the “fuel nitrogen” or “fuel-bound nitrogen” conversion to NO. The nitrogen-containing compounds evaporate during the gasification process, and this leads to NO formation in the gas phase. The mechanism involves a sequence of reactions that involve the conversion of fuel (nitrogen-containing) to NH3 and HCN, and eventually to N atoms which react with O2 and OH to form NOx. It is important to note that the reactions forming NH3 and HCN are usually very fast and therefore not rate-determining. However, the rate-limiting steps are those with N-atoms such as N þ OH ! NO þ H and N þ NO ! N2 þ O, which compete for the N-atoms. Currently extensive research investigations are being undertaken in the field of pollutant formation and the control of NOx and soot, as well as of other combustionrelated pollutants and byproducts. Since it is beyond the scope of this chapter to identify all of the different mechanisms of pollutant formation, measures and control, it is important to refer to other chapters in the book, as well as to references listed in this chapter. As combustion kinetics modeling is described in Chapter 8, it will not be discussed any further at this point.

2.11 Outlook

It is evident that chemical kinetics and combustion are closely interlinked and share a strong relationship, with chemical kinetics providing insight into

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unraveling the combustion mechanisms. Jill date, there has been a continuing challenge to control pollutant emissions while maintaining the high efficiency of combustion engines. Thus, there is a broad scope of research opportunities and needs for understanding combustion processes, with detailed insights into combustion chemistry. Presently in the western world, there is a continued effort and a strong motivation to develop new energy sources and renewable fuels, not only to reduce the dependence on foreign oil but also to develop an increasing energy security and mitigate climate changes. The current “newer” fuels include biologically derived ethanol and biodiesel, as well as fossil fuels such as tar sands, all of which are chemically different from conventional transportation fuels such as gasoline and diesel. These new fuels will also contain a diverse range of hydrocarbon classes, such as olefins, naphthenes, and oxygenates. At present, the kinetics of these such classes is largely unknown, especially under the elevated pressures that mimic the conditions encountered in advanced engines. Although fuels derived from biomass have the potential to reduce CO2 emissions, there is a trade-off on higher NOx formation, and in this respect combustion chemistry plays an important role in understanding these processes, and their control. Today, combustion technology is passing through a period of “dual transition,” with the development of advanced engines that depend on compressionignition (e.g., the HCCI, which has different operating conditions from conventional engines) and newer fuels (that have different chemical compositions compared to traditional, fossil fuels) entering the fuel stream. In such cases, a successful strategy must be developed aimed at the experimental characterization of these newer fuel types with regards to their ignition properties and reactivities. Chemical kinetics has a superior role to play, where theoretical and experimental findings must be in concert, such that mechanisms for new fuel types can be developed and validated for existing combustion models. It is clear that the rates of combustion and pollutant emission, and the ignition behavior of compression-based engines, will depend on the chemical details of the fuel. Future engines will be expected to operate at higher pressures, at lower temperatures, and under leaner conditions than conventional engines. Although the initial steps of hydrocarbon oxidation of these classes of fuel are as yet unexplored, “new fuels” means “new chemistry.” Consequently, there will be much to accomplish in order to understand this new chemistry that continues to open new research opportunities in the chemical kinetics of combustion processes, thus acquiring scientific insight into phenomena such as auto-ignition, as well as characterizing the in-cylinder formation of soot and other pollutants, and their destruction processes. The development of predictive models of combustion remains a future challenge which can only be met by providing a detailed understanding of the chemistry involved. This can be achieved through optimized validation experiments and advanced diagnostic tools, which must be developed on all scales, from individual molecular events to the combustion chamber [30, 46]. Clearly, research into the evolution of engine technologies and fuel changes should go hand-in-hand, in order to develop the right fuel for the right engine, as well as providing engine design optimization. This places combustion chemistry – and especially chemical kinetics – in a central role for research opportunities aimed at meeting the “grand challenge” to design future fuels for future engines.

References

2.12 Summary

In this chapter, the fundamentals of chemical kinetics and combustion chemistry in general and its role in understanding details of combustion processes have been discussed. In addition, two important areas of present-day research, such as lowtemperature hydrocarbon oxidation and the formation of pollutants such as NOx and soot, have been detailed, while the basic mechanisms associated with not only lowtemperature oxidation processes but also soot and NOx formation, have been unveiled. A further aim of the chapter was to focus on how chemical kinetics can help to unravel the mechanisms involved, and why accurate rate data are required in order to model combustion processes that will assist in developing predictive capabilities for combustion systems under a variety of operating conditions. Combustion modeling, which forms an important part of combustion chemistry, is more fully discussed in Chapter 8. The discussions have also centered on the progress of chemical kinetics with regards to certain key issues over the years, and proposals made as to which route the path forward would take in this “energy area,” where the ample research needs face major future challenges. In meeting these needs, the chemical kinetics of combustion will have a major role to play in the development of combustion technologies among an ever-changing fuel stream. Clearly, the future research opportunities in this field of combustion kinetics are immense.

Acknowledgments

The author would like to thank Sandia National Laboratories, under the LaboratoryDirected Research and Development (LDRD) program. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94-AL85000. The author also thanks the Cluster of Excellence “Tailor-Made Fuels from Biomass”, funded by the Excellence Initiative by the German Federal and state Governments to promote science and research at German universities.

References 1 Laidler, K.J. (1965) Chemical Kinetics, 2nd 2

3 4 5

edn, McGraw-Hill Book Co., New York. Gilbert, R.G. and Smith, S.C. (1990) Theory of Unimolecular and Recombination Reactions, Blackwell, Oxford. Arrhenius, S. (1889) Phys. Chem., 4, 226. Lindemann, F.A. (1922) Trans. Faraday Soc., 17, 598. Gardiner, W.C. Jr (ed.) (1984) Combustion Chemistry, Springer-Verlag, New York.

6 Troe, J. (1979) Predictive possibilities of

unimolecular rate theory. J. Phys. Chem., 83 (1), 114. 7 Troe, J. (1983) Theory of thermal unimolecular reactions in the fall-off range. Ber. Bunsen-Ges. Phys. Chem., 87 (2), 161. 8 Troe, J. (1974) Fall-off curves of unimolecular reactions. Ber. Bunsen-Ges. Phys. Chem., 78 (5), 478.

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9 Troe, J. (1977) Theory of thermal

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11

12

13 14 15

16

17 18

19

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unimolecular reactions at low-pressures. J. Chem. Phys., 66 (11), 4758. Gilbert, R.G., Luther, K., and Troe, J. (1983) Theory of thermal unimolecular reactions in the fall-off range. Ber. BunsenGes. Phys. Chem., 87 (2), 169. Fernandes, R.X., Luther, K., Troe, J., and Ushakov, V.G. (2008) Experimental and modeling study of the recombination reaction H þ O2( þ M) ! HO2( þ M) between 300 and 900 K, 1.5 and 950 Bar, and in the bath gases M ¼ He, Ar, and N2. Phys. Chem. Chem. Phys., 10 (29), 4313. Miller, J.A., Pilling, M.J., and Troe, J. (2005) Unravelling combustion mechanisms through a quantitative understanding of elementary reactions. Proc. Combust. Inst., 30 (1), 43. Pilling, M.J. (2009) Proc. Combust. Inst., 32, 27–44. Warnatz, J., Maas, U., and Dibble, R.W. (1996) Combustion, Springer. Schwanebeck, W. and Warnatz, J. (1975) Reactions of butadiene 1. Reaction with hydrogen-atoms. Ber. Bunsen. Phys. Chem., 79 (6), 530. DeSain, J.D., Klippenstein, S.J., Miller, J.A., and Taatjes, C.A. (2003) Measurements, theory, and modeling of OH formation in ethyl plus O2 and propyl plus O2 reactions. J. Phys. Chem. A, 107 (22), 4415. Lifshitz, A. (ed.) (1981) Shock Waves in Chemistry, Marcel Dekker, New York. Tranter, R.S., Giri, B.R., and Kiefer, J.H. (2007) Shock tube/time-of-flight mass spectrometer for high temperature kinetic studies. Rev. Sci. Instrum., 78 (3), 11. Fernandes, R.X., Zador, J., Jusinski, L.E., Miller, J.A., and Taatjes, C.A. (2009) Phys. Chem. Chem. Phys., 11, 1320. Forster, R., Frost, M., Fulle, D., Hamann, H.F., Hippler, H., Schlepegrell, A., and Troe, J. (1995) High-pressure range of the addition of HO to HO, NO, NO2, and CO. 1. Saturated laser-induced fluorescence measurements at 298 K. Journal of Chemical Physics, 103 (8), 2949. Fulle, D., Hamann, H.F., Hippler, H., and Troe, J. (1998) Temperature and Pressure dependence of the addition reactions of HO to NO and to NO2. IV. Saturated laser-induced fluorescence

22

23

24

25

26

27

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measurements up to 1400 bar. J. Chem. Phys., 108 (13), 5391. Fernandes, R.X., Luther, K., and Troe, J. (2006) Falloff curves for the reaction CH3 þ O2 ( þ M) ! CH3O2 ( þ M) in the pressure range 2–1000 bar and the temperature range 300–700 K. J. Phys. Chem. A, 110 (13), 4442. Fernandes, R.X., Gebert, A., and Hippler, H. (2002) The pyrolysis of 2-, 3-, and 4methylbenzyl radicals behind shock waves. Proc. Combust. Inst., 29, 1337. Fernandes, R.X., Hippler, H., and Olzmann, M. (2005) Determination of the rate coefficient for the C3h3 þ C3h3 reaction at high temperatures by shocktube investigations. Proc. Combust. Inst., 30, 1033. Culbertson, B., Sivaramakrishnan, R., and Brezinsky, K. (2008) Elevated pressure thermal experiments and modeling studies on the water-gas shift reaction. J. Propul. Power, 24 (5), 1085. Taatjes, C.A., Hansen, N., McIlroy, A., Miller, J.A., Senosiain, J.P., Klippenstein, S.J., Qi, F., Sheng, L.S., Zhang, Y.W., Cool, T.A., Wang, J., Westmoreland, P.R., Law, M.E., Kasper, T., and Kohse-Hoinghaus, K. (2005) Enols are common intermediates in hydrocarbon oxidation. Science, 308 (5730), 1887. Osborn, D.L., Zou, P., Johnsen, H., Hayden, C.C., Taatjes, C.A., Knyazev, V.D., North, S.W., Petreka, D.S., Ahmed, M., and Leone, S. (2008) The multiplexed chemical kinetic photoionization mass spectrometer: a new approach for isomer resolved chemical kinetics. Rev. Sci. Instrum., 79, 1. Wallington, T.J., Kaiser, E.W., and Farrell, J.T. (2006) Automotive fuels and internal combustion engines: a chemical perspective. Chem. Soc. Rev, 35 (4), 335. Pilling, M.J., Robertson, S.H., and Seakins, P.W. (1995) Elementary radical reactions and autoignition. J. Chem. Soc., Faraday Trans., 91 (23), 4179. Wcorkshop Report of the Department of Energy’s Office of Basic energy Sciences, Basic Research Needs for Clean and Efficient Combustion of 21st Century Transportation Fuels Available at:

References

31

32

33

34

35

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http://www.sc.doe.gov/bes/reports/files/ CTF_rpt_.pdf. Walker, R.W. and Morley, C. (1997) Basic chemistry of combustion, LowTemperature Combustion and Autoignition (ed. M.J. Pilling), Elsevier, Amsterdam. Knepp, A.M., Meloni, G., Jusinski, L.E., Taatjes, C.A., Cavallotti, C., and Klippenstein, S.J. (2007) Theory, measurements, and modeling of OH and HO2 formation in the reaction of cyclohexyl radicals with O2. Phys. Chem. Chem. Phys., 9, 4315. Miller, J.A. and Klippenstein, S.J. (2006) Master equation methods in gas-phase chemical kinetics. J. Phys. Chem. A, 110 (36), 10528. Klippenstein, S.J. and Miller, J.A. (2002) From the time-dependent, multiple-well master equation to phenomenological rate coefficients. J. Phys. Chem. A, 106 (40), 9267. Miller, J.A. (1996) Theory and modeling in combustion chemistry. Proc. Combust. Inst., 26, 461. Taatjes, C.A., Hansen, N., Osborn, D.L., Kohse-Hoinghaus, K., Cool, T.A., and Westmoreland, P.R. (2008) “Imaging” combustion chemistry via multiplexed synchrotron-photoionization mass spectrometry. Phys. Chem. Chem. Phys., 10 (1), 20. Richter, H. and Howard, J.B. (2000) Prog. Energy Combust., 26 (4-6), 565–608.

38 Miller, J.A. and Klippenstein, S.J. (2003)

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The recombination of propargyl radicals and other reactions on a C6H6 potential. J. Phys. Chem. A, 107, 7783. Miller, J.A. and Melius, C.F. (1992) Kinetic andthermodynamicissuesintheformation of aromatic-compounds in flames of aliphatic fuels. Combust. Flame, 91, 21. Frenklach, M. and Wang, H. (1991) Detailed modeling of soot particle nucleation and growth. Proc. Combust. Inst., 23, 1559. Frenklach, M. and Clary, D. (1983) Aspects of autocatalytic reaction-kinetics. Ind. Eng. Chem. Fund., 22 (4), 433. Georgievskii, Y., Miller, J.A., and Klippenstein, S.J. (2007) Association rate constants for reactions between resonance-stabilized radicals: C3H3 þ C3H3, C3H3 þ C3H5, and C3H5 þ C3H5. Phys. Chem. Chem. Phys., 9 (31), 4259. Miller, J.A. and Bowman, C.T. (1989) Mechanism and modeling of nitrogen chemistry in combustion. Prog. Energy Combust., 15, 287. Fenimore, C.P. (1971) Proc. Combust. Inst., 13, 373–380. Bockhorn, H. (ed.) (1994) Soot Formation in Combustion, Springer-Verlag, Berlin. Manley, D.K., McIlroy, A., and Taatjes, C.A. (2008) Research needs for future internal combustion engines. Phys. Today, 61 (11), 47–52.

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3 Combustion Physics Alexey Burluka 3.1 Introduction

Combustion is an exothermic chemical transformation that usually proceeds in a very large number of elementary chemical reactions steps that may individually be either exothermic or endothermic, but together have an overall effect that the energy of chemical bonds of the original reactant – the fuel – is released as heat. Combustion physics is the particular branch of physics that aims to determine the rates of various individual physico-chemical processes such as heat transfer or the formation of a particular substance. For many utilitarian purposes, it is the rate of heat release which is of the main interest. It should be noted that a process where the main useful outcome of combustion is its products rather than heat is termed a high-temperature synthesis, and a detailed description of such processes may be found in Vol. 5 Ch. 18 of this Handbook. Traditionally, a distinction is drawn between chemical and physical factors in combustion. The former include the exact sequence and rates of individual elementary reactions specific for any given fuel (for details, see Chapter 2 of this Volume). The aim of the present chapter is to provide an introduction into the physical effects that contribute to the rate of combustion. Whilst a detailed description of combustion physics may be too lengthy at this point, an earnest attempt will be made to provide a route to reference material providing a more exhaustive description. The important role played by combustion physics can be easily seen from the fact that combustion of the same amount of fuel with the same amount of air under the same temperature and pressure conditions will proceed at different rates and ultimately release different amounts of usable heat in different burners. Clearly, the chemistry of combustion can only be a rate-determining process if any physical factor is much faster than any elementary chemical reaction. In all but a few (mostly artificial) laboratory flames this is not the case; more often than not, it is the chemistry which is the fastest factor while the combustion features are determined by the interaction of chemistry with only a few basic physical phenomena that include:

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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. . . .

Flow hydrodynamics, especially turbulence. Molecular transfer of heat and mass in multicomponent gas mixtures. Heat transfer by radiation (for details, see Chapter 5 of this Volume). Phase transitions in heterogeneous (e.g., coal or liquid fuel) combustion. These include rates of vaporization, patterns of the mixing between combustible vapor and air, and the rate of coal devolatilization.

Each of the above-listed factors has its own characteristic time, and the characteristic times ratios determine a particular combustion regime [1]. The rate of combustion per unit volume of the reacting medium depends on this regime. Before discussing the rate of combustion in various flame configurations, it must be mentioned that, for a properly arranged combustion process, the final state – that is, the composition, temperature and pressure – of the combustion products and the total magnitude of heat release are determined by the thermodynamic equilibrium. It is expedient to begin the exposition at this point, providing the summary of combustion thermodynamics.

3.2 Equilibrium Thermodynamics 3.2.1 Extensive and Intensive Variables

Extensive variables are the characteristics of a system which depend upon (i.e., are linearly proportional to) the mass or the volume of the system; these include the system’s mass m, entropy s, and internal energy U. Intensive variables are the characteristics of the system which do not depend upon either its mass or its dimensions; these include the temperature T, specific internal energy u, and pressure p. For any thermodynamic system, any of its intensive variables may be expressed as a function depending solely on its extensive variables [2]. For example, an equation of state expresses the pressure – an intensive variable – in terms of its volume, mass, and internal energy, all of which are extensive variables. Note, however, that often the temperature is used instead of the internal energy. The property of a system is any single-valued function of all its extensive variables; for example, if a system is characterized exclusively with only two extensive variables – perhaps its volume and internal energy – then a property is any function which does not change its value when the volume and internal energy of that system remain constant. 3.2.2 The First Law of Thermodynamics

The First Law of thermodynamics states that any extensive variables X of a system, the volume of which is V, possesses its own balance equation expressed as an

3.2 Equilibrium Thermodynamics

integral equation: ððð ðð ððð q ~j ~ XdV ¼
n dS þ WX dV X qt V S V

ð3:1Þ

where S denotes the system boundary, ~ n is its unit normal vector pointed outwards, and the integration is performed over the entire the volume and the surface of the system. The first term on the right-hand side represents exchange of the quantity X with the system surroundings, expressed in terms of its flux~j X , here taken as negative when directed into the system. The second term describes the production of X in internal processes. Note that while Equation 3.1 is written for an extensive variable, the First Law does not imply that an intensive variable cannot possess its own balance equation; however, it is not possible to formulate a generic balance equation for an intensive variable. The solution consists in use of a corresponding “density”; for example, instead of a temperature it may be preferred to consider the enthalpy of the system. A very simple corollary of the First Law, Equation 3.1, is that the conservation of energy for an isentropic steady-state flow of a compressible gas of variable density means that the specific enthalpy h ¼ u þ pV remains constant along a streamline. 3.2.3 Equilibrium

Complete equilibrium means that, for any given point inside the system as well as on its boundary all fluxes,~j X and source terms WX are zero for any X. This ultimate state of the thermal death is seldom (if ever) achieved in reality; instead, in practice the situation is often encountered where the individual fluxes and sources are not zero, but rather compensate each other at any given point of a system, for example: dX ¼ WXþ
WX
¼ 0 dt

ð3:2Þ

where the production WXþ and destruction WX
rates of X may depend upon the external constraints imposed on the system. The situation described with Equation 3.2 is often called “dynamical equilibrium.” 3.2.4 The Second Law of Thermodynamics

The Second Law may be formulated as follows [2]. Let a system, characterized by a set of extensive variables ðXi ; i ¼ 1; 2; . . . ; nÞ, be at equilibrium. The internal energy U and volume V of the system are among Xi . Then, there exists one more extensive variable s called entropy, which is a function of ðXi ; i ¼ 1; 2; . . . ; nÞ such that: 1)

_ Its change caused by the exchange of heat with the surroundings is Wext ¼ T1 Q, where Q_ is the heat flux rate into the system.

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2)

3)

Its change caused by any spontaneous internal process is positive, regardless of whether the system is isolated (i.e., it experiences neither heat nor mass exchange), or closed (it may exchange heat but not matter), or open (it may exchange both energy and matter): Wsint  0 At the thermodynamic equilibrium Ws ¼ Wsint þ Wsext ¼ 0.

3.2.5 Entropy

In terms of entropy, other extensive variables can be defined as, for example: 1) 2) 3)

qs jV;Xi The temperature T may be defined as: T ¼ qU qs The pressure p may be defined as: p ¼ T qV jU;Xi If Xi is the mass fraction of the i-th species in a multicomponent mixture, then its qs chemical potential mi may be defined as: mi ¼
T  qU jV;Xj;j6¼i .

All the above definitions may be rewritten as one single equation [2]: 1 p 1 X ds ¼ dext s þ dint s ¼  dU þ  dV
 mi dXi T T T i

ð3:3Þ

This equation – termed the Gibbs equation – forms the basis for any analysis of a thermodynamic equilibrium, as it reflects both the First and Second Laws of thermodynamics. 3.2.6 Thermochemical Equilibrium and the Second Law

According to the Second Law of thermodynamics, the evolution of a system proceeds in the direction of increasing internal entropy, dint s  0, until an equilibrium is attained. Thus, from the thermodynamics point of view, combustion is a transition of nonequilibrium reactants, fuel and oxidant, into equilibrium products. The equilibrium state – that is, temperature, pressure and composition of the products – depends upon the external constraints; for example, combustion in a gas turbine takes place at a constant pressure. The final state of evolution – that is, the state of the combustion products – is therefore characterized with a minimum of the thermodynamic potential determined by the constraints. For example, for combustion at constant given temperature and pressure the products composition will be such that the Gibbs energy of products is at a minimum. A clear and concise exposition of the fundamental principles of the equilibrium thermodynamics can be found elsewhere [2], and also in Chapters 1 and 6 of this Volume. As a matter of fact, external constraints imposed on the state of a combustible mixture vary from one location to another, and from one instant of time to another, within the same burner. The cases where those constraints are known in advance are rare; the solution therefore requires the transport equations expressing the so-called “first principles” – that is, the conservation of mass, momentum, energy, and number of atoms – to be solved. From the solution of these transport equations one would

3.2 Equilibrium Thermodynamics

typically find a limited number of thermodynamic variables, and assumptions of complete or partial equilibrium may subsequently be used to identify any remaining quantities of interest. Consider a system consisting of N chemical species Ai ; let ni ; i ¼ 1; 2; . . . ; N be the number of moles of i-th species in that system. For such a system, Equation 3.3 can be written as: Tds ¼ dU þ pdV


N X

ð3:4Þ

mi dni

i¼1

where the total change of entropy ds is the sum of two contributions, one, dext s, is caused P by exchange of heat dQ ¼ dU þ pdV and mass i dext ni with the surroundings: Tdext s ¼ dU þ pdV


N X

ð3:5Þ

mi dext ni

i¼1

The second contribution dint s comes from changes in composition dint ni caused by chemical reactions: Tdint s ¼


N X

ð3:6Þ

mi dint ni

i¼1

Consider the following chemical reaction with reactants A1 . . . Ak and products Ak þ 1 . . . AN : n1 A1 þ n2 A2 þ    þ nk Ak >nk þ 1 Ak þ 1 þ nk þ 2 Ak þ 2 þ    þ nk Ak

ð3:7Þ

Changes of concentrations caused by this reaction will obey the following relationship:


dn1 dn2 dnN ¼
¼ ... ¼ ¼ dj n1 n2 nN

ð3:8Þ

where j is called the progress variable, or reaction extent, for this given reaction. A small increment, dj, of the reaction extent corresponds to the following increase of the entropy of the system: 2 3 N k N X X X ð3:9Þ Tdint s ¼
mi dint ni ¼ dj4 ni mi
nj mj 5 i¼1

i¼1

j¼k þ 1

so that the affinity A of the reaction can be introduced: A¼

k X i¼1

ni mi


N X

nj mj

ð3:10Þ

j¼k þ 1

The Second Law prescribes that dint s  0; therefore, the direction of the spontaneous evolution of chemical reactions corresponds to the positive affinity. Indeed: dint s A dj ¼ 0 dt T dt

ð3:11Þ

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Henceforth, if A  0 the reaction will spontaneously proceed to the right dj dt  0,  0. A ¼ 0 at a thermowhereas, if A  0 the reaction will proceed to the left dj dt chemical equilibrium. It is important to understand that the above definitions are valid for any arbitrary chemical transformation, and not necessarily for an elementary chemical reaction which may occur as a result of a single molecular collision (see Chapter 2 of this Volume). Furthermore, concurrently with any given elementary reaction, another reaction usually proceeds in the reverse direction. The usual notation in this case is: Wþ

nA A þ nB B ,
nC C þ nD D W

ð3:12Þ

where W þ and W
are the rates for the forward and reverse reactions, respectively. When the forward and reverse rates are the same – that is, when the concentrations of the constituents are not changed by this reaction – then the reaction is said to be in a state of “chemical equilibrium.” The rates of the forward and backward reactions are: 
Eþ W þ ¼ k þ ½AnA ½BnB exp
act RT ð3:13Þ 
E
W
¼ k
½CnC ½DnD exp
act RT respectively; here, the square brackets denote the molar concentrations, the k are the pre-exponential factors, and Eact is the activation energy, [3]. At the chemical equilibrium, W þ ¼ W
, and therefore:

 þ ½CnC ½DnD k þ Eact
Eact ð3:14Þ ¼ Keq ðTÞ nA nB ¼
 exp k RT ½A ½B In this way, the so-called equilibrium “constant” Keq is introduced. As can be easily seen, the equilibrium constant depends strongly on the temperature and is not really a constant; furthermore, if the reaction changes the total number of moles, nA þ nB 6¼ nC þ nD , then the value of K will also depend on the pressure. The equilibrium constant determines the reaction extent, so that when Keq !
1 j ! 0. The equilibrium constant Keq is defined in terms of molar concentration; often (especially for engineering combustion problems), another equilibrium constant, Kp , which is defined in terms of the partial pressures pi of the components, is more convenient: Kp ðTÞ ¼

pnCC pnDD ¼ Keq ðTÞ  ðRTÞnC þ nD
nA
nB pnAA pnBB

ð3:15Þ

Both, Keq and Kp as defined above depend not only upon the temperature but also on the pressure; hence, in order to avoid such pressure-dependency yet another definition of the equilibrium constant is often used. This new definition introduces a dimensionless equilibrium constant, K, which uses a reference pressure p0 , which normally is the standard atmospheric pressure (p0 ¼ 1 atm or p0 ¼ 1 bar).

3.2 Equilibrium Thermodynamics

For the reaction given by Equation 3.12, the dimension of Kp is atmnC þ nD
nA
nB , and a new dimensionless “constant” K may therefore defined as: K¼

Kp pn0C þ nD
nA
nB

¼

 nC þ nD
nA
nB xCnC xDnD p  p0 xAnA xBnB

ð3:16Þ

and is a function of temperature only. Here, xi is the molar fraction of the species i. One particular advantage of the dimensionless equilibrium constant is that it allows an easy analysis of how pressure change will influence the equilibrium. 3.2.7 Equilibrium and the Gibbs Energy

It is well known [2] that the thermochemical equilibrium is described with a minimum of the corresponding thermodynamic potential. For example, a system which is maintained at constant temperature and pressure evolves towards the minimum of its Gibbs energy. For modern, computer-based calculations of equilibrium it proves more convenient to determine the equilibrium concentrations through minimizing Gibbs energy under constraints of atom conservation, rather than to employ equilibrium constants. Several algorithms can be used to determine the extremum of a function of many variables; the method described below was used by Morley in his freely available GASEQ code [4]. Consider a system composed of N species, at temperature T and pressure p; the number of moles of i-th species is denoted as xi , and without loss of generality it can P be assumed that i xi ¼ 1. The Gibbs energy of this system is: Gðp; TÞ ¼

 N  X xi xi gi ðp0 ; TÞ þ RTxi ln P þ RTxi lnp xi i¼1

ð3:17Þ

If there are K different elements in the system, then the conservation of elements is expressed with a linear algebraic system of K equations: N X

aij xi
bj ¼ 0

ð3:18Þ

i¼1

where j ¼ 1; 2 . . . K, bj is the number of moles of atoms of element j in this system, and aij is the number of atoms of element j in species i. The standard technique of finding a constrained extremum consists in introduction of the Lagrange multipliers lj and finding minimum of a new function W: K N X X W ¼ Gðp; TÞ
lj ðaij xi
bj Þ

¼

N  X i¼1

j¼1

i¼1

 X K N X xi xi gi ðp0 ; TÞ þ RTxi ln P x þ RTxi ln p
lj ðaij xi
bj Þ xi j¼1 i¼1 ð3:19Þ

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The extremum of the Gibbs energy under the constraints Eq. 3.18 corresponds to: qW ¼0 qxi

ð3:20Þ

for all i ¼ 1; 2 . . . N. Differentiating Equation 3.19, one has N equations:   X K qW xi ¼ gi ðp0 ; TÞ þ RT 1 þ ln P þ ln p
lj aij xi ¼ 0 qxi xi j¼1

ð3:21Þ

Simultaneous solution of the system of N þ K equations formed by the Equations 3.18 and 3.21 allows the unknown xi and lj to be determined. Because these equations are nonlinear in xi , typically, an iterative procedure is necessary; iterations are also needed when either temperature or pressure is not known and must be identified under some additional constraints. Finally, it should be noted that the establishment of a thermochemical equilibrium is a rapid process with characteristic time of the same order of magnitude as the time between a molecular collision. For any practical purpose, the combustion products behind the flame zone are at equilibrium, but the residence time of the gas within the flame zone is comparable to the intermolecular collision time; thus, it cannot be assumed that the medium within the flame zone is at equilibrium. It is a wellestablished fact, that the concentration of radicals inside the flame may exceed the equilibrium value by a factor of ten [5, 6]. Another principal limitation of the equilibrium thermodynamics is that it does not provide any information about the rate of combustion.

3.3 Rate of Combustion 3.3.1 Conservation Laws

From a mathematical point of view, all fluid flow phenomena – including combustion – are described in terms of the so-called “transport equations.” These are nothing other than mathematical expressions of various conservation laws; for example, the conservation of mass is described in terms of the continuity equation which is a transport equation for the flow density. Similarly, the conservation of momentum is expressed by transport equations for the velocity vector components, or the Navier– Stokes equations. It is remarkable that the same set of governing equations would describe a large variety of different flows, ranging from the movement of a fine layer of treacle on a spoon, to flame of a candle to a tornado, or to a forest fire covering an area of tens of square miles. The difference in the mathematical expression of all these cases would only come from the boundary and initial conditions and particular expressions of the source terms.

3.3 Rate of Combustion

The derivation of the transport equations is available in many textbooks, with perhaps one of the most comprehensive, for different coordinate systems and different quantities, in Ref. [7]. As a general rule, a transport equation for any scalar quantity is obtained considering the integral balance of this quantity within a volume. For a volume element V of any arbitrary (but fixed), shape, denoting its boundary as S and the normal unit vector~ n to its boundary as positive when pointing outwards, it can be written that the accumulation rate equals the sum of the divergence of flux jX of this quantity, plus the rate of production or destruction within this volume element: ððð ðð ððð q ~j ~ rX dV ¼
n dS þ WX dV ð3:22Þ X qt V S V where X is the amount of the quantity in question per unit mass, r is the mass density of the medium, and WX is the intensity of the source term per unit volume. This is essentially identical to Equation 3.1, if the latter is formulated for the product of rX. Convection is the flux of the quantity X caused by the mass flow with the volume-averaged velocity ~ u and passing through an area element dS is:
rð~ u ~ n ÞXdS. The flux of X caused by any factor other than convection is denoted u X . The surface integral in Equation 3.22 may be transformed into an as ~ t X ¼ _jX
r~ integral over the volume, so that this equation becomes: ððð qrX þ divðr~ u X þ~ tX Þ ð3:23Þ V qt This equation must hold good for any fixed volume V; hence, the expression under the integral must be zero at every point: qrX þ divðr~ u X þ~ t X Þ
WX ¼ 0 qt

ð3:24Þ

Equation 3.24 is very general, and essentially it is valid for any extensive variable. For example, when written for X ¼ ui, the i-th component of momentum of a unit mass it will express Newton’s second law for the i-th coordinate direction; it will then be nothing but one of the Navier–Stokes equations. When written for X ¼ Yi, the mass fraction of the i-th species, it will express the balance of the number of molecules of that species in a unit volume. In any case, an appropriate expression for fluxes ~ t X must be provided. In the particular case when X in the Equation 3.1 is chosen constant, X ¼ 1, the flux t and the source term W turns to zero, and this equation will express the conservation of mass. The flow is termed incompressible if the density variations at different locations in a flow caused by variations of pressure and flow velocity can be neglected. In a nonreacting flow of gases, and a flow of most liquids without boiling or condensation, an incompressible flow is synonymous with the constant density rðx; tÞ ¼ Const; in this case it follows from Equation 3.1 that the velocity divergence is zero everywhere div ~ u ¼ 0. However, for a typical combustion problem, an unsteady flame requires use of Equation 3.1 in its general form. A steady-state situation implies div r~ u ¼ 0; there is no reason to neglect density gradients induced

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by the thermal expansion. It should be emphasized that in a typical combustion problem the density variation is induced by the heat release in the flame zone, while the fresh gas ahead of the flame and the combustion products behind may be considered as incompressible to a large degree of accuracy [8]. 3.3.2 Transport Equation for Concentrations of Species

It must first be said that, in a multicomponent system, there are numerous ways to describe the concentrations of the various species [7], the most often-used definitions being: . . . .

Mass concentration ri , which is the mass of the i-th species per unit volume of medium; this may also be termed the partial density of the i-th species. Molar concentration, ci ¼ ri =Wi , which is the number of moles of the i-th species per unit volume of medium; here, Wi is the molecular weight of the i-th species. Mass fraction Yi ¼ rri , which is the mass of the i-th species per unit mass of the medium. Molar fraction xi , which is the molar concentration of the i-th species divided by the total molar density of the solution.

The relationships between these concentrations may easily be obtained, if it is P P P P taken into account that i ri ¼ i ci Wi ¼ r and i xi ¼ i Yi ¼ 1. For example: P r ci  Wi ci ¼ xi Wi P i Yi ¼ i ¼ ð3:25Þ r r c  i i Wi It is quite obvious that the exact form of the transport equation will depend on the specific definition of “concentration.” However, the general principles for its derivation are exactly the same as outlined before for Equation 3.1. For example, when the mass fractions are used: qrYi þ div ðr~ u Yi þ~ t i Þ
Wi ¼ 0 qt

ð3:26Þ

where ti is the mass flux caused by the molecular diffusion, and Wi (expressed in g cm
3 s) is the net rate of formation of the i-th species by the chemical reactions. Specific expressions are available in Ref. [7]. 3.3.3 Molecular Transport

As will be seen later, the overall combustion rate is often very sensitive to the rate of the molecular transport, denoted as ti in Equation 3.26. The simplest (and perhaps most often-used because of its simplicity) expression for mass transport is Fick’s law of diffusion, which states that the diffusional flux of mass of i-th species is linearly proportional to this species concentration gradient:

3.3 Rate of Combustion

~ t i ¼
Di grad ri

ð3:27Þ

Similarly, Fourier’s law states that the thermal flux ~ q is proportional to the temperature gradient: ~ q ¼
l grad T

ð3:28Þ

where l is the thermal conductivity, related to the thermal diffusivity k as l ¼ rcp k. The definition of the “effective” diffusivities Di or k is by no means a trivial task, especially in a multicomponent mixture. Besides, these laws, Eqs. 3.27 and 3.28, may be quite inaccurate for combustion applications where there is a need for an accurate calculation of the effective mass diffusivities Di which depend on the medium composition, pressure, and temperature [9–11]. The molecular transfer of heat and mass occurs when there exists a gradient of the chemical potential within the flow [2]. The chemical potential of one mole of a multicomponent mixture of gases maintained at temperature T and the pressure p is numerically equal to its Gibbs energy: mðp; TÞ ¼

N X

xi ðgi ðp0 ; TÞ þ RT ln pxi Þ

ð3:29Þ

i¼1

From this equation, it can be seen that in a general case the diffusional flux of the ith species may be caused by the difference in any molar fraction xk , temperature, or pressure. The diffusional mass flux is conveniently expressed as ti ¼ rVid Yi in terms of the diffusional velocity Vid for which the following expression may be written [12]: Vid ¼


N X

Dij dj
Hi

j¼1

grad T T

ð3:30Þ

where the diffusion driving force dj is caused by gradients of molar fraction of the j-th species and the pressure gradient: dj ¼ grad xj þ ðxi
Yi Þ

grad p p

ð3:31Þ

The diffusion matrix Dij must be symmetric positively defined; furthermore, it P must also be such that the total diffusive flux of mass is zero: Vid Yi ¼ 0. An iterative procedure satisfying these requirements can be found in Ref. [11]; the same source should be consulted for the algorithm to determine the heat flux vector ~ q, including determination of the thermal diffusivity factors Hi . The procedures outlined in Refs [11, 12] require as an input the so-called “binary mass diffusivities” Dij ; that is, the coefficients of diffusion of the i-th species in its mixture with j-th species. Commonly used expressions for Dij [7] may be derived from the kinetic theory of gases [13, 14], assuming a particular potential for intermolecular force. In combustion problems, for this purpose the Lennard–Jones “12–6” potential is used very widely, and its parameters have been tabulated for a large number of substances [15]. It is worth noting that, whilst this potential is theoretically justified for nonpolar molecules only, its use in combustion problems provides the transport coefficients

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within an accuracy of only a small percentage point. The use of Stockmeyer potential for nonpolar molecules has been shown to result in a somewhat modified expression for molecular collision parameters [16]. At the same time, an estimation of the thermal conductivity in combustion problems requires an account of various modes of heat transfer, including the transfer of energy between the translational and rotational energies of the colliding molecules. As such, these various modes of energy transfer result in inelastic collisions [17]; the determination of thermal conductivity in multicomponent gas mixtures is strongly affected by these modes. An iterative procedure that allows the accurate calculation of thermal conductivity is presented in Ref. [12]. The importance of accurately calculating molecular transport coefficients has been highlighted in several studies [9–11] for laminar flames. A particularly strong influence for laminar flames has been noted in calculations of hydrogen–air flames, where the mass diffusive flux caused by the temperature gradient is important. It should be noted that there is today an extensive experimental evidence [18, 19] that the molecular transport coefficients have a great effect on the rate of combustion in premixed turbulent flames. However, at present no adequate theoretical framework has been proposed which allows molecular transport within turbulent flames to be taken into account. An attempt to develop an approach to allow this is presented later in the chapter. The rôle of molecular transport can, however, easily be illustrated by using the two simple examples of laminar flames, and this has laid the foundations for the science of combustion during the past half-century. A wide range of specific examples of Equation 3.1, as applied to various flow scenarios, may be found in Ref. [7], which provided also specific expressions for the transport coefficients. Here, an illustration of the use of the transport equations will be made with the two limiting regimes of gaseous combustion: (i) premixed flame propagation in reactants which are perfectly mixed; and (ii) diffusion flame established on the mixing surface of perfectly segregated reactants. 3.3.4 Propagation of Premixed Planar Laminar Flame: The Theory of Zeldovich, Semenov and Frank-Kamenetsky (ZSFK)

When a fuel and an oxidant (e.g., air) are perfectly well mixed before combustion is initiated, the combustion occurs within a self-sustained propagating flame wave, the speed of which, under certain conditions [20], is constant. One of the most important notions in the theory of combustion is the so-called “normal flame speed” un ; this is a velocity of propagation of combustion wave in a perfect mixture of fuel and oxidant. The following example deals with its definition under several simplifying assumptions. It is assumed that: . .

The combustible mixture is perfect and occupies an unbounded space. The density r is constant, that is, there is no motion induced by the thermal expansion u ¼ 0.

3.3 Rate of Combustion . . . .

The combustion wave is planar and one-dimensional; x denotes the direction of the flame propagation. The molecular mass diffusivities for different species and the thermal diffusivity are equal DF ¼ Dox ¼ k ¼ rclp ; they do not depend on temperature. The molecular transport is described with the simplest Fourier and Fick’s laws (see Equations 3.27 and 3.28). Chemical reactions occur in one step and are irreversible; the fuel is the deficient reactant.

Under these assumptions, the equation for temperature and concentrations can be written as: rcp r

qT qT q qT þ rucp ¼ rcp k þ rQWðYF ; Yox ; TÞ qt qx qx qx

qYF qYF q qYF þ ru ¼ rDF þ rnWðYF ; Yox ; TÞ qx qt qx qx

with the boundary conditions 8 Tð
1Þ ¼ Tad ; > > > > > YF ð
1Þ ¼ 0 > > > > < qT ð
1Þ ¼ qx > > > > > > qY > > F ð
1Þ ¼ > : qx

Tð1Þ

ð3:32Þ

¼ T0

YF ð
1Þ ¼ YF0 qT ð1Þ ¼ 0 qx

ð3:33Þ

qYF ð1Þ ¼ 0 qx

It is easy to see that these equations are similar, hence the resulting fields of temperature and concentration are also similar: c¼

T
T0 Y 0
YF ðx; tÞ ¼ F Tad
T0 YF0

ð3:34Þ

where the variable cðx; tÞ is nothing else but a suitably expressed reaction progress variable j, for example, as given by Equation 3.8. c ¼ 1 in the combustion products and c ¼ 0 in the fresh mixture. Thus plane flame propagation may be described solely in terms of cðx; tÞ, the transport equation for which is: qc qc q2 c þ u ¼ k 2 þ WðcÞ qt qt qx

ð3:35Þ

The next step is to introduce a new variable, in terms of which cðx; tÞ would no longer depend on time. Because the solution is sought as a traveling wave, propagating rightwards ðx ! 1Þ (see Figure 3.1) with a constant speed un , a variable substitution z ¼ x
un t may be used in order to reveal the flame structure. Upon this substitution, the Equation 3.35 will become
un

dc d2 c ¼ k 2 þ wðcðzÞÞ dz dz

ð3:36Þ

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Y F(x)

W(c) reaction zone

combustion products

c(x)

fresh gas pre–heat zone

dc/dx 2

dc/dx2

Figure 3.1 Sketch of the structure of a premixed laminar flame.

However, the chemical source term WðcÞ is nonlinear, and direct integration of Equation 3.35 is impossible. It has been shown [20] that the solution of Equation 3.35 is not possible for any un, and the task is to find the value of un which makes it possible. The way to obtain a solution is based on the practical observation that the reaction zone is very thin in a real flame, and the first (i.e., convection) term in Equation 3.35 is negligible in this zone. In contrast, in a thicker preheat zone (see Figure 3.1) the chemical reaction rate is very low. Departing from this observation, all the flame thickness can be divided into two zones; separated at the flame location z ¼ zF : 1)

The reaction zone, the structure of which is determined by the balance between the reaction and the molecular diffusion: k

2)

d2 c þ wðcÞ ¼ 0 dz2

ð3:37Þ

The preheat zone, where the reaction rate is neglected and where the molecular transfer is compensated by the convection:
un

dc d2 c ¼k 2 dz dz

ð3:38Þ

These equations are complemented with the boundary conditions far from flame:

3.3 Rate of Combustion

8 cðz !
1Þ ¼ 1 > > < dc ðz !
1Þ ¼ 0 > > : dz

cðz ! 1Þ

¼ 0

dc ðz ! 1Þ ¼ 0 dz

ð3:39Þ

Equation 3.38 may be integrated one time with the outcome that: "

#   dc  dc  k
¼
un ½cðz ¼ 1Þ
cðz ¼ zF Þ dz z¼1 dz z¼zF

ð3:40Þ

The next step is to integrate Equation 3.37, and to do this c ðzÞ must be dc is the new considered as a new independent variable instead of z, while dz unknown function. Integration of the Equation 3.37 results in:  2   2  ð dc  dc  2 1 j ¼ j ¼
Wðc0 Þ dc 0  j ¼
1
F dz  dz  k 0

ð3:41Þ

By using the boundary conditions, a combination of the above equations will allow the determination of the thought flame speed un as: 
ð1 1=2 dc  un 2 0 0 ¼
¼
Wðc Þdc dz z¼zF k 0 k h Ð i1=2 1 un ¼ 2k 0 Wðc 0 Þdc 0

ð3:42Þ

This is a major result of the ZSFK theory, since it demonstrates that un depends solely on k and the integral of the reaction rate dependency. It is remarkable that the flame speed does not depend upon a particular character of the reaction rate dependency, nor upon the temperature and concentration; rather, it depends only hÐ i
1 1 as on the chemical times scale defined as tc ¼ 0 Wðc 0 Þdc 0 un ¼ ð2ktc Þ1=2

ð3:43Þ

Further analysis of laminar premixed flame structure, stability, and propagation speed may be found in Ref. [20]. It should be noted that the flame speed depends strongly on the molecular transport coefficients. In particular, it having been shown [20] that when the diffusion of the deficient reactant D is greater than the thermal diffusivity k, the flame speed is proportional to the ratio k=D. However, when D  k, then the propagation of flame at a constant speed is unstable and the flame will exhibit oscillations of its speed. Further extensions of the ZSFK theory ideas of distinguishing several distinct zones within a flame are brought by the method of matched asymptotic developments, see for example, [21–23].

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3.3.5 Nonpremixed (Diffusion) Flame

In a situation where the fuel and oxidizer are not premixed it is clear that, in order for the chemical reactions to proceed, the reactants have first to mix at the molecular level. Then, if the chemical reaction is fast enough – which is very often the case – it will be such molecular mixing–diffusion that controls the rate of combustion. In this case, the flame will separate the mixture of fuel and products on the one side, and the air (oxidizer) on the other side. The examples of this regime include: the flame of a candle, a fire in a domestic stove, or combustion in a diesel engine. It is also very often thought that the diffusion flame becomes established around ablating solid or vaporizing liquid fuels; all common fuels, except pure carbon in its various forms (e.g., char or graphite), will burn in the gaseous phase. For a simplified analysis of a diffusion flame, it can be supposed that the chemical reactions may be represented in a simplified form: n1 A1 þ n2 A2 ! n3 A3

ð3:44Þ

where ni are the stoichiometric coefficients. Assuming a constant specific heat cp and molecular transport coefficients, and the simplest expressions for the molecular fluxes (Equations 3.27 and 3.28), then the transport equations for the concentrations ci of species Ai , and the temperature T can be written as: qci þ~ u  grad ci ¼ Di div ðgrad ci Þ
ni W qt ð3:45Þ

qT Q þ~ u  grad T ¼ k div ðgrad TÞ þ W qt cp

These Equations are analogous to Eqs. 3.32 but are written for two or more dimensions. It is obvious that there cannot exist a one-dimensional diffusion flame: Here, W is the rate of reaction, the exact expression of which is not essential for what follows. If it is assumed that all the molecular transfer coefficients are equal, Di ¼ D ¼ k, then it is easy to see that:
   q c3 ¼ W ¼ þ~ u  grad
Di div ðgradÞ n3 qt  ¼

½. . . . . .

¼

½. . . . . .

¼

½. . . . . .




c2 n2




c1 n1



 ¼ ð3:46Þ

 ¼

  cp T
Q

3.3 Rate of Combustion

Although all of the equations turn out to be similar, this does not mean that the fields of temperature and concentrations are themselves similar, because the boundary conditions are different! Perhaps the most simple but most insightful and fundamental observations in the entire combustion theory was made by Burke and Schumann in 1928 [20]. This stated     that, if the equation for
nc11 were to be subtracted from that for
nc22 , then the reaction rate would be eliminated: 

q c1 c2 þ~ u  grad
Di div ðgradÞ ¼0 ð3:47Þ
qt n1 n2 Because its transport equation does h i not have a source term caused by chemical reactions, the combination ncFF
ncoxox is called a “passive” – that is, a chemically inert, scalar (although quite often it is also termed the mixture fraction). In addition to the mixture fraction, it is possible to construct another combination of concentrations, for example, c1 and c2 , and temperature which will also be a passive scalar, for example:
 cp c1 c2 g ¼ Const1 Const2 þ ð1
Const2 Þ þ ðT
TO Þ ð3:48Þ n1 n2 Q This, or any other similar combination, is known as a Schwab–Zeldovich variable [1]. In order to commence the analysis of a diffusion flame structure, it is expedient to assume that the chemical reactions are very fast. In this case, it may be concluded that the concentration of either fuel or oxidizer should be zero at any given point in the flow – that is, they cannot coexist. It will also mean that the surface where g ¼ 0 will represent the surface of the flame where both fuel and oxidizer disappear. Such a structure is illustrated in Figure 3.2.

mixture fraction

oxidizer

e ur rat pe tem

Fuel

fuel

Oxidizer

Figure 3.2 Sketch of the structure of a nonpremixed laminar flame.

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If, in order to consider the diffusion flame flow where, far away from the flame, the boundary conditions are: cF ¼ c10 ; cox ¼ 0

T ¼ T0

on the fuel side

cF ¼ 0;

T ¼ T0

on the oxidiser side

cox ¼ c20

ð3:49Þ

then it is possible to choose the constants in the definition of the Schwab–Zeldovich variable (Equation 3.48), so that g will take the same value away from the flame, say g ¼ 1. The constants satisfying this requirement will be: Const1 ¼

n1 n2 þ c10 c20

Const2 ¼

n1 c20 n2 c10 þ n1 c20

and the corresponding Schwab–Zeldovich variable will be:   c1 c2 n1 n2 cp ðT
T0 Þ g¼ þ þ þ c10 c20 c10 c20 Q

ð3:50Þ

ð3:51Þ

It can easily be seen that this is simply a suitably normalized enthalpy in that it satisfies the transport equations (Eq. 3.45) and the boundary conditions (Equation 3.49); therefore, it is a solution describing the structure of a nonpremixed flame. By substituting g ¼ 1 into Equation 3.51, the temperature can be expressed in terms of c1 and c2 : T ¼ T0 þ

Q c10 c20
c1 c20
c2 c10 cp n1 c20 þ n2 c10

ð3:52Þ

By substituting in the equation above the values of c1 ¼ 0 and c2 ¼ 0, the flame temperature can then be determined: Tfl ¼ T0 þ

Q c10 c20 cp n1 c20 þ n2 c10

ð3:53Þ

and the concentration of the products c3 can be found in similar way. Before completing this brief review of the properties of laminar diffusion flame, it should be mentioned that the diffusion flame is established where the diffusional fluxes of fuel and oxidizer are met at the stoichiometric proportions [1]:


D1 grad c1 D2 grad c2 ¼
n1 n2

ð3:54Þ

These two considered regimes of perfectly premixed and nonpremixed combustion illustrate that molecular transport is equally as important as the chemical reactions. However, in virtually every “practical” combustion appliance – be it an automobile engine or a power plant boiler – the combustion is invariably turbulent, such that the question arises as to whether the same physical factors play an equally important rôle in turbulent flames as they do in laminar flames. The answer to this question remains the subject of considerable debate, and revised mathematical techniques must be introduced before such discussions can proceed any further.

3.4 Turbulent Combustion

3.4 Turbulent Combustion

It is widely known that when the flow velocities exceed a certain threshold, the flow pattern becomes irregular and all flow parameters begin to fluctuate. In most combustion situations, fluctuations in the velocity and temperature are of comparable magnitude to the average values. The equations of motion, continuity and energy conservation place constraints on the fluctuations resulting in nonzero correlation length- and time-scales [24]. Although the problem is old, perhaps the first to attempt a description of this phenomenon was Leonardo da Vinci, until now this turbulence problem remains the only unsolved question in classical physics. It is fair to say that there is no totally satisfactory definition of turbulence in existence, although in practice a clear distinction can be made between turbulent and laminar flows. Virtually any modern description of turbulence is based on, or employs the splitting of, all the flow variables into two parts: (i) the mean (which is denoted in the following text with angular brackets); and (ii) the fluctuation (which is denoted by a prime). For example: ~ x ; tÞ u ð~ x ; tÞ ¼ h~ u ð~ x ; tÞi þ u0 ð~

ð3:55Þ

Fluctuations will have a very strong effect on the rate of combustion, and this is illustrated in Figure 3.3, which shows how the flow root-mean-square (rms) velocity 7 φ=0.7, p=5 atm φ=0.8, p=10 atm φ=0.8, p=5 atm φ=1.0, p=10 atm φ=1.0, p=5 atm

Turbulent burning rate, Ut [m s–1]

6 5

u′

3/4

fit curve

4 3 2 1 0 0

1

2

3

4

6 5 RMS velocity, u′ [m s–1]

7

8

9

Figure 3.3 Mass burning rate in CH4 –air spherical explosions for different rms velocities at the flame radii rfl ¼ 9 cm at different equivalence ratios and initial pressures. The initial temperature is T0 ¼ 20  C.

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u0 ¼ ðhu0 2 iÞ1=2 affects the rate of combustion in a turbulent premixed flame. In this case, the burning rate and the speed of the turbulent flame Ut are approximated by a power dependency Ut  u0 3=4, which is justified theoretically in Ref. [25]. Currently, there exists several ways to achieve such a decomposition because the mean value may be obtained from various averaging procedures. For example, it may be a time average, suitable for statistically steady flows (i.e., when hui does not depend upon time), or it may be a space average, suitable for homogeneous and isotropic flows (i.e., when hui does not depend upon the spatial location). Most often, and especially in analytical investigations, use is made of the ensemble average which will be taken over a number of repeated measurements performed under identical conditions. There are also some weighted averages; for example, the very popular Favre average uses density as the weighting function. At this point it is relevant to note that, in an experiment using hot-wire anemometry, the velocity signal would undergo some double time- and space-averaging. The time averaging arises from the time lag (i.e., inertia) of the probe and its circuit; the space averaging occurs because the size of the hot wire probe is not zero. At the same time, laser-Doppler velocimetry would yield a signal which is space- and ensemble-averaged. A modern computational fluid dynamics (CFD) software would produce results averaged with the density as a weighting function. A “real” turbulent flow is not ergodic; hence, there is absolutely no guarantee that all these averaging procedures will yield the same results! 3.4.1 Averaging in a Turbulent Flow

From an applied point of view, in the theory of stochastic processes an average of a function is formed with respect to a stochastic variable, the statistical properties of which are known a priori. The dependency of this function on the latter variable may be explicit, or implicit, for example, if given by a differential equation. For a description of a turbulent flow there are no random terms in the equations of motion, and the stochastic fluctuations should be introduced into the boundary and initial conditions. The fundamental difficulty of applying the results of the theory of stochastic processes to the description of turbulence is that there is no obvious way of expressing the dependency of the flow velocity (or any other quantity which depends on the velocity) on a perturbation in initial conditions [26]. Because the transport equations are nonlinear partial differential equations of the second order, it should be expected that the averaging operation would be nonlocal; this consideration is illustrated with the example below, which originally was derived in Ref. [26]. For a particular type of equation which does not contain explicitly the spatial coordinate ~ x , for example,   qf ð~ x ; tÞ ~ qf ð~ x ; tÞ qf ð~ x ; tÞ q2 f ð~ x ; tÞ þ jðtÞ ¼ F t; f ; ; ; . . . qt q~ x q~ x q~ x2

ð3:56Þ

3.4 Turbulent Combustion

where ~ jðtÞ is a vector stochastic process, and F is a deterministic function, the solution may be formally written as:  ðt  jðt0 Þ dt0 ; t f ð~ x ; tÞ ¼ G ~ x
~ ð3:57Þ 0

where the function G satisfies a deterministic equation   qGð~ x ; tÞ qGð~ x ; tÞ q2 Gð~ x ; tÞ ; . . . ¼ F t; G; ; qt q~ x q~ x2

ð3:58Þ

In this particular case, it proves possible to construct a closed equation for hf i which, however, may contain an infinite number of terms. Following Ref. [26], this can be illustrated using a one-dimensional Burgers equation with a stochastic addition to the velocity uðx; tÞ: quðx; tÞ quðx; tÞ q2 f ð~ x ; tÞ þ ðuðx; tÞ þ jðtÞÞ ¼n qt qx q~ x2

ð3:59Þ

with a nonrandom initial and boundary conditions, for example, uðt ¼ 0; xÞ ¼ u0 ðxÞ. Averaging Equation 3.59 with respect to realizations of j – that is, by taking an ensemble average, a transport equation is obtained for hui: qhuiðx; tÞ 1 qhu2 iðx; tÞ qhuðx; tÞ  jðtÞi q2 huiðx; tÞ þ  þ ¼n qt 2 qx qx qx 2

ð3:60Þ

In drawing an analogy with the usual state of affairs in the so-called “turbulence modeling,” an application of Equation 3.60 would entrain some ad hoc expression, “a closure” for the two correlations, hu2 i and huji, coming from the convective-like term. However, for this simple problem and for some particular statistics of j, it is possible to obtain rigorously a “closed” form of the Equation 3.60, without any additional hypothesis. This derivation makes use of the variational derivatives [27], assuming that the solution uðx; tÞ at the point x and time t depend upon the random perturbation at a previous moment t. By using Equation 3.57, it is possible to express the variational derivative of uðx; tÞ with respect to jðtÞ as [27]:   ðt duðt; xÞ d quðt; xÞ ¼ G t; x 0
jðt0 Þ dt0 ¼
qðt
tÞ djðtÞ djðtÞ qx 0

ð3:61Þ

Here, q(t) is the Heaviside step function. Let jðtÞ be a Gaussian process with the zero mean and the correlation function BðtÞ; for such a process the Novikov–Furutsu formula [27] would read: hjðtÞuðt; xÞi ¼

ðt 0

 dtB ðt
tÞ

duðt; xÞ djðtÞ

ð3:62Þ

This determines one of the correlations; to express the mean square hu2 i a following auxiliary relationship should be used [27]:

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huðt; x; jðt1 Þ þ g1 ðt1 Þ; uðt; x; jðt2 Þ þ g2 ðt2 ÞÞi ¼ 2 3 ð ðt 2 d 5 dt1 dt2 Bðt1
t2 Þ exp4 dg1 ðt1 Þdg2 ðt2 Þ 0

ð3:63Þ

 huðt; jðt1 Þ þ g1 ðt1 ÞÞihuðt; jðt2 Þ þ g2 ðt2 ÞÞi

from which a mean square can be determined, setting g1 ¼ g2 ¼ 0, as: # " ð  t  q2 2 huðt; x þ g1 Þihuðt; x þ g2 Þi g1 ¼g2 ¼0 hu i ¼ exp 2 dtðt
tÞBðtÞ Þ qg1 qg2 0 ¼

1 k
ð t X 2 k¼0

k!

0

k
dtðt
tÞBðtÞ

qk hui qx k

2 ð3:64Þ

Finally, upon the substitution of the above expressions into Equation 3.60, one obtains its closed form: k k 1 k
ð t qhuiðx; tÞ X 2 q hui qk þ 1 hui þ dtðt
tÞBðtÞ  ¼ qt k! 0 qxk qx k þ 1 k¼0   2 ðt q huiðx; tÞ n þ BðtÞ dt qx2 0

ð3:65Þ

Very remarkable here is the fact that the nonlinear, convective term produces upon averaging an infinite series of terms. It is also Ð t very easy to see that the commonly used introduction of the “eddy viscosity” nt ¼ 0 BðtÞ dt corresponds to the zeroth-order truncation of the series in the left-hand side of Equation 3.65. Finally, it should be noted that in combustion problems the scalar (i.e., concentrations and temperature or enthalpy) transport by the fluctuating velocity field is described with a linear term. The sources of nonlinearity arising in the averaging of the energy transport equations derive from the dependency of the molecular transport coefficients (e.g., Equation 3.30), from the strongly nonlinear reaction rate (e.g., Arrhenius) and, where relevant, from the fourth-power dependency of the radiant energy flux on temperature. The latter two nonlinearities may be taken into account, for example, by presuming the probability density function (PDF) of the temperature [28]. However, the nonlinearity originating in the molecular transport coefficients remains a largely unexplored phenomenon, although, as it will be seen from the analysis of the experimental data on the burning rates of premixed flames, it may have a drastic effect even on the average reaction rates. The extensive analysis of experimental data in Refs [25, 29, 30] has shown that the average reaction rates in turbulent premixed combustion depend – in addition to the commonly considered turbulence rms velocity u0, length-scale lt and laminar flame speed un – also on the molecular transport coefficients, such as heat diffusivity k and the fuel and oxidant diffusivities DF and Dox (or the ratios known as the Lewis number(s), for example, LeF ¼ k=LeF . The arguments developed in Refs [25, 29–31]

3.4 Turbulent Combustion

have shown that, when u0 exceeds a certain value u0 m , then in addition to these factors the adiabatic flame temperature and the details of chemical kinetics mechanism become important. Because un itself is a function of elementary reactions rates and the molecular transport properties, a model of turbulent premixed combustion should take these into account in order to be applicable across the entire range of u0 , from weak turbulence to extinction. Dependency on the molecular transport coefficients does not seem likely to be expressed solely in terms of un and Le, as indicated by the available experimental data. An attempt to construct an approach, together with an explicit account of the molecular transport in turbulent combustion, is provided in the following section. 3.4.2 Reference Scalar Field (RSF) Model

In a general case, a statistical description of a turbulent flame containing N
1 species requires N (N
1 concentrations and the temperature or the enthalpy) individual scalar fields and their correlations, thus requiring an N-dimensional PDF [32]. The basic idea of the RSF model [33–35] is to represent the scalar statistics in terms x ; tÞ possessing an extra probaof a number of the so-called reference fields Yj ðX ;~ bilistic dimension X 2 ½0; 1. In other words, in this model, the computation of one N-dimensional PDF is replaced with the computation of N one-dimensional reference fields. The original formulation [34, 35] was limited to a single scalar case, where the extra probabilityÐ dimension X was defined as the cumulative probability distri^ dY. ^ In the case of many reactive scalars a question arises ^ ¼ 1
^Y PðYÞ bution: XðYÞ 0 as to whether the combustion process can be represented as a motion of point in the configuration space of ~ Y along a one-dimensional trajectory, especially in a case of complex chemistry. This is a necessary condition for an accurate representation of chemical reaction rates in the RSF model [36]. This question was considered in Ref. [37], where it was argued that whilst variations in reaction rates and mixing time alter the shape of those trajectories, they remain one-dimensional and the RSF model is an attempt to exploit this fact. When the trajectories in the configuration space, connecting the fresh gas state ~ Y0 with the burnt gas state ~ Y b , are assumed to be one-dimensional, the state of the burning mixture can be represented in terms on one parameter only. However, the parameter describing the completeness of combustion, such as the progress variable in a single scalar case, cannot be specified in a general case of Le 6¼ 1 and for different combustion regimes [1]. Any attempt to use any one of the species concentration, or the temperature, as such a parameter is doomed to failure as can be seen from measurements (e.g., Refs [38, 39]), where such parameterization led to a widely scattered set of points. The same problem of a lack of simple ordering for several scalar configuration space was discussed in Ref. [40]. However, because the one-point statistical description cannot be self-sufficient as a matter of principle (such that, for example, an approximation for the small-scale mixing is required), it proves quite possible to extend the RSF governing equations for a multiscalar case, without

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making any recourse to an exact definition of X but imposing instead some conditions that it should satisfy. For example, if the motion of the point representing the mixture state were to be parameterized in terms of a parameter x with PDF PðxÞ, for example, x might be a type of residence time and there might exist a single-valued vector function ~ Y ðjÞ. X may be then be naturally defined in either of two alternative ways: XðxÞ ¼

ð ^x

PðxÞ dx or X ðxÞ ¼ 1


xmin

ð ^x PðxÞ dx

ð3:66Þ

xmin

and the reference fields would then be defined as YðX Þ ¼ YðXð^ xÞÞ ¼ Yð^ xÞ. The first requirement for the definition of a reference scalar field is that all singlepoint statistics could be found as: ð1 ^ ¼ dX 0  Fð~ hFðYÞi Y ðX 0 ;~ x ; tÞÞ ð3:67Þ 0

The second requirement is that, exactly because X may have two, or more, alternative definitions (see Equation 3.66), the transport equations for YðX Þ should have coefficients symmetrical about the point X ¼ 1=2. Similarly, employed smallscale mixing model for the RSFs should preserve the average scalar values in the absence of any chemical reactions, produce correct rates of scalar variance decay, and also preserve the boundedness of scalars and possess a number of other properties as enumerated in, for example, Refs [21, 32]. The original [35] formulation of the RSF’s transport equations employed two terms that described the small-scale mixing, one of which was taken following the interaction-by-exchange-with-the-mean (IEM) model [32]: qYi ðX ;~ x ; tÞ ~ i ðX Þ þ h~ u j~ Y ð~ x ; tÞ ¼ ~ Y ðX Þi  rY qt Di q2 Yi ðX Þ hYi ið~ x ; tÞ
Yi ðX Þ Y ðX ÞÞ þ 2  þ ¼ Wi ð~ qX 2 tt lt

ð3:68Þ

where lt and tt are the integral length and time scales of turbulent scalar fields. The boundary and initial conditions for Equation 3.68 are discussed in Ref. [35]. A subsequent study [41] revealed that the deficiencies in the IEM mixing model [32] are inherited by the solutions of Equation 3.68; moreover, this equation predicts very little sensitivity of the results upon the molecular diffusivity Di at Ret 1, in contrast to the experimentally observed trends [30]. In order to cure these deficiencies, a modification of the small-scale mixing description is proposed here as: qYi ðX ;~ x ; tÞ ~ i ðX Þ ¼ Wi ðYðX ^ ÞÞ þ q Di ðX Þ  qYi ðX Þ þ ð~ u ðX Þ  rÞY qt qX qX

ð3:69Þ

where the small-scale mixing is described as a diffusion in X direction with a variable diffusion coefficient Di ðXÞ. Although, the definition of the conditional

3.4 Turbulent Combustion

velocity ~ u ðXÞ and the derivation of its transport equation are provided in Ref. [33], only an application of Equation 3.69 to a homogeneous case is considered at this point. The small-scale mixing in Equation 3.69 is described as a “diffusion process.” Provided that Di ðX ¼ 0Þ ¼ Di ðX ¼ 1Þ ¼ 0 and Di ðX Þ > 0 for X 2 ð0; 1Þ, regardless of a particular shape of Di ðXÞ, satisfy these conditions, then the diffusion process representation preserves the mean scalar values and guarantees that the scalar values remain within the bounds they had at t ¼ 0. At the same time, the correct rate of scalar variance decay in nonreacting homogeneous case is determined by the integral: ð ð q 1 1 1 q qYi ðX Þ Di ðX Þ  dX ðYi ðX Þ
hYi iÞ2 dX ¼ ðYi ðX Þ
hYi iÞ qt 0 2 0 qX qX ð3:70Þ   ð 1 1 qYi ðX Þ 2 ¼ Di ðX Þ  dX 2 0 qX

2 i ðX Þ from which it can be seen that the combination 12Di ðX Þ  qYqX is the conditional scalar dissipation, and that this is fully consistent with Equation 3.67. A specific shape of Di ðX Þ should depend upon the (effective) molecular diffusivity Di for the scalar Yi, but the same time it should produce the rate of decay in Equation 3.70, as determined by the integral time scale tt with only a weak sensitivity upon Di . Further, considering that Equation 3.69 can be adimensionalized using the u0 , lt and Di as the scaling variables, the most general form for Di ðXÞ Di Y ðX should be Di ðX ; ~ Ð 1 Þ; Ret  Sci Þ, where Sci ¼ n , while the integral of adimensionalized diffusivity 0 Di ðX Þ dX must be a constant, denoted as CD . The independence of this constant of Ret , molecular transport, or flow geometry would then Y ðX Þ; Ret  Sci Þ. At be an indicator of how realistic is the chosen formula for Di ðX ; ~ present, it is necessary to resort to some guesswork to presume Y ðXÞ; Ret  Sci Þ and to assess this presumed dependency, following the Di ðX ; ~ guidelines outlined above. The initial simulations of a small-scale mixing, following the reasoning outlined above, employs one such presumed dependency: CD  ðRet  Sci Þ  ðX ð1
X ÞÞaðRet  Sci Þ tt pffiffiffi pCða þ 1Þ ¼z  BðaðzÞ þ 1; aðzÞ þ 1Þ ¼ 3 22a þ 1 C a þ 2 Di ðX Þ ¼

ð3:71Þ

where Bðx;Ð yÞ is the b-function and the final equation expresses the normalization 1 condition 0 Di ðXÞ dX ¼ CD . The exponent a is found as a solution to the last equation, and it changes from 2 at Ret  Sci ¼ 30 [values typical for direct numerical simulation (DNS) research] to approximately 5 for a more realistic case of Ret  Sci ¼ 2  104 . Certainly, Equation 3.71 is only tentative and serves, in this case, solely for assessing the proposed RSF model framework (see Equation 3.69).

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The assessment of Equation 3.69 remains ongoing, and the first results presented here concern its application to, first, a nonreactive single scalar mixing using the DNS data [42] as the reference; and second, to determine whether this model is capable of predicting the influence of molecular transport coefficients at Ret 1 on the average reaction rate, by using a hypothetic homogeneous reactor with the chemical kinetics parameters describing the di-tert-butylperoxide (DTBP) decomposition flame used in Ref. [31]. One study [42] has presented the results of DNS in homogeneous and isotropic turbulence at Ret Sc 28 for the scalar Y 2 ½
1; 1, with the zero mean and initial PDF close to the bimodal shape. It was found that the value CD ¼ 0:18 allows the reproduction of both the rms scalar fluctuation decay (see Figure 3.4) and the corresponding PDF shapes (see Figure 3.5). Moreover, the exponential decrease to zero of the third- and fourth-order cumulants of the scalar PDF (see Figure 3.4b) is entirely consistent with the observations in Ref. [42] (see their Figure 21) of the PDF collapse to a universal shape parameterized solely in terms of the rms fluctuation. The profiles of the conditional scalar dissipation are shown in Figure 3.6, and are also in a very good agreement with the DNS [42] (see their Figure 19). However, the influence of molecular transport cannot be explored in this test case, because it has not been varied in the DNS.

(b) Cumulants

Scalar rms, 1/2

(a) 1 RSF prediction DNS

0.1

1 rd

3 order th 4 order

0.1

0.01

0.001

0.0001

0.01

1e-05

0.001

0

0.5

1

1.5

2

2.5

Time, t/τ t

3

3.5

4

4.5

1e-06

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Time, t/τ t

Figure 3.4 Scalar field moments versus time for the initially bimodal PDF for the conditions of DNS [42]. (a) Comparison of the progress variable rms with the DNS; (b) The third and fourth cumulants.

3.4 Turbulent Combustion

(a)

1.5 RSF, c′=0.55 DNS, c′=0.55

Scalar pdf, P(c)

Gaussian pdf, c′=0.55

1

0.5

0 (b)

-1

0 scalar, c

0.5

1

0.5

1

2.5 RSF, c’=0.27 Gaussian pdf, c′=0.27 DNS [Eswaran & Pope (1988), Fig. 14], c′=0.27

2 scalar pdf P(c)

-0.5

1.5

1

0.5

0

-1

-0.5

0 scalar, c

Figure 3.5 Scalar PDF for the initially bimodal PDF for the conditions of DNS [42], at the instants when the progress variable rms equals 0.55 and 0.27, respectively.

An assessment of the effects of molecular transport as predicted by the present form of the RSF model was therefore performed in a hypothetical case of a homogeneous constant-pressure reactor fed with a premixture of 25 vol% of DTBP vapor with nitrogen at the initial temperature T0 ¼ 350 K. The combustion rate corresponded to the atmospheric pressure. This particular reacting system was selected on the basis of its very simple chemical kinetics [31] which necessitates the use of two scalars only, namely the fuel mass fraction and the temperature. For this flame, the mass diffusivity DDTBP ¼ 0.057 cm2 s
1, and the thermal diffusivity k ¼ 0.07 cm2 s
1 [31]. The turbulence integral length-scale was kept

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Conditional scalar dissipation, <χ >c /<χ >

2 c′=0.27 c′=0.38 c′=0.55

1.5

1

0.5

0

-1

-0.5

0 scalar,c

0.5

1

Figure 3.6 Conditional scalar dissipation for the initially bimodal PDF for the conditions of DNS [42]. The curves shown correspond to the PDF shapes shown in Figure 3.5.

constant at lt ¼ 2.0 cm, and the rms velocity u0 was varied. The principal value of interest was the maximum reaction rate Wmax , achieved in the transient regime when the rate of supply was superior to the blow-off limit. Clearly, the average rate of combustion equals the rate of supply, and may therefore be made completely arbitrary. There is a certain analogy between the conditions inside a turbulent flame brush and this hypothetical reactor. At the same time, the entrainment of a fresh mixture into the brush is governed by the turbulent diffusion, and is dependent on flow geometry and the local turbulence properties. In this simplified case, the rate of fresh mixture supply is an independent variable, a straightforward link between it and the turbulent burning rate could not be established. At the same time, it might be expected that the influence of various factors on Wmax and Ut is qualitatively similar, which in turn justifies the use of the former variable as an indicator of the average reaction rates in the turbulent flame brush. Figure 3.7 presents the dependency of the maximum average reaction rate on u0 . Variation in the Lewis number was achieved by changing k simultaneously with the pre-exponent in the reaction rate, in such a way that the laminar flame speed un calculated for the first-order reaction [20] (see Chapter 4 and Equation 3.29) remains constant. The results thus obtained clearly demonstrate a very appreciable increase in Wmax ðu0 Þ with a reduction in k, in agreement with the conclusions of Refs [25, 30]. At the same time, varying k only while keeping the constant value of the pre-exponential factor corresponds to maintaining the product un k constant while changing the Lewis number. It can be seen that such variation produces very little effect on Wmax . Finally, it may be remarked that for the case with the large Lewis

3.5 Conclusions

10 κ =0.07,k=k0 κ =0.035,k=4k0 κ =0.014,k=k0/4 κ =0.07,k=4k0

<W >max, 102 1 s–1

8

Fitting curve: y=a+bx

3/4

6

4

2

0

0

0.5

1

1.5

2

2.5

3

3.5

4

rms velocity, u′, m s–1 Figure 3.7 Maximum average reaction rate in a homogeneous reactor versus rms turbulence velocity for different parameters.

number Le ¼ 2:48, the increase in Wmax with increase in u0 is much slower, both absolutely and relatively, than for cases with smaller Le. All of these trends support the analogy between Wmax in a homogeneous reactor and the mass burning rate Ut in a turbulent flame.

3.5 Conclusions

In virtually every combustion occurrence the physical factors have an important – and often determining – influence. The state of the combustion products in most practical cases is determined by the thermodynamic equilibrium. At the same time, the rate at which these products are formed is affected by the rates at which the molecular collisions transfer the mass and energy. These rates are expressed with the help of the so-called “diffusivities,” the definition of which in multicomponent reacting mixtures require quite elaborate procedures. One major aspect of combustion physics is that of turbulence. Indeed, the fluctuating nature of a turbulent flow makes it necessary to use methods which explicitly consider the flow statistics. Consequently, the definition of average transport equations becomes an equally important topic. Finally, it must be said – unfortunately – that the development of a mathematical framework that would enable an explicit description of molecular transport within a turbulent flame remains unresolved, despite its undoubted practical importance.

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References 1 Borghi, R., Destriau, M.G., and de Soete,

15 Reid, R.C., Prausnitz, J.M., and Sherwood,

G. (1998) Combustion and Flames: Chemical and Physical Principles, Ed. Technip, Paris. Kondepudi, D. and Prigogine, I. (1998) Modern Thermodynamics, John Wiley & Sons, Chichester. Atkins, P.W. and de Paula, J. (2006) Atkins’ Physical Chemistry, 8th edn, Oxford University Press, Oxford. Morley, C. (2010) GASEQ computer program. Available at: http:// www.arcl02.dsl.pipex.com/. Gaydon, A.G. and Wolfhardt, H.G. (1953) Flames, their Structure, Radiation and Temperature, Chapman & Hall, London. Sokolik, A.S. (1963) Self-ignition, flame, and detonation in gases. Israel Program for Scientific Translations, Jerusalem. Bird, R.B., Stewart, W.E., and Lightfoot, E.N. (1960) Transport Phenomena, John Wiley & Sons, Inc., New York. Lipatnikov, A.N. (2008) Conditionally averaged balance equations for modeling premixed turbulent combustion in flamelet regime. Combust. Flame, 152, 529–547. de Swart, J.A.M., Groot, C.R.A., van Oijen, J.A., ten Thije Boonkkamp, J.H.M., and de Goey, L.P.H. (2006) Detailed analysis of the mass burning rate of stretched flames including preferential diffusion effects. Combust. Flame, 145, 245–258. Ern, A. and Giovangigli, V. (1998) Thermal diffusion effects in hydrogen-air and methane-air flames. Combust. Theory Modelling, 2, 349–372. Ern, A. and Giovangigli, V. (1999) Impact of detailed multicomponent transport on planar and counterflow hydrogen/air and methane/air flames. Combust. Sci. Technol., 149, 157–181. Ern, A. (1995) Fast and accurate multicomponent transport property evaluation. J. Comp. Phys., 120, 105–116. Bird, G.A. (1976) Molecular Gas Dynamics, Clarendon Press, Oxford. Lifshitz, E.M. and Pitaevskii, L.P. (1981) Physical Kinetics, Pergamon Press, Oxford.

T.K. (1977) The Properties of Gases and Liquids, 4th edn, McGraw-Hill Book Corp., New York. Monchick, L. and Mason, E.A. (1961) Transport properties of polar gases. J. Chem. Phys., 35, 1676–1697. Mason, E.A. and Monchick, L. (1963) Theory of transport properties of gases, IXth Symposium (International) on Combustion, New York, The Combustion Institute. Nakahara, M. and Kido, H. (1998) A study of the premixed turbulent combustion mechanism taking the preferential diffusion effect into consideration. M
emoires of the Faculty of Engineering Kyushu University, 58 (2), 55–82. Karpov, V.P. and Severin, E.S. (1980) Effects of molecular-transport coefficients on the rate of turbulent combustion. Combust. Expl. Shock gWaves, 16, 41–46. Zel’dovich, Ya.B., Barenblatt, G.I., Librovich, V.B., and Makhviladze, G.M. (1980) Mathematical Theory of Combustion and Explosion, Nauka, Moscow. Clavin, P. (1985) Dynamic behaviour of premixed flame fronts in laminar and turbulent flows. Prog. Energy Combust. Sci., 11, 1–59. Matalon, M., Cui, C., and Bechtold, J.K. (2003) Hydrodynamic theory of premixed flames: effects of stoichiometry, variable transport coefficients and arbitrary reaction orders. J. Fluid Mech., 487, 179–210. Sivashinsky, G.I. (2002) Some developments in premixed combustion modelling. Proc. Combust. Inst., 29, 1737–1761. Monin, A.S. and Yaglom, A.M. (1965) Statistical Hydromechanics, Izd-vo Nauka, Moscow. Lipatnikov, A.N. and Chomiak, J. (2002) Turbulent flame speed and thickness: phenomenology, evaluation and application in multi-dimensional simulations. Prog. Energy Combust. Sci., 28, 1–74.

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5

6

7

8

9

10

11

12

13 14

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17

18

19

20

21

22

23

24

25

References 26 Klyatskin, V.I. (1980) Stochastic Equations

27 28

29

30

31

32

33

34

and Waves in Randomly Inhomogeneous Media, Nauka, Moscow, in Russian. Klyatskin, V.I. (2005) Dynamics of Stochastic Systems, Elsevier, Amsterdam. Landenfeld, T., Sadiki, A., and Janicka, J. (2002) A turbulence-chemistry interaction model based on a multivariate presumed beta-pdf method for turbulent flames. Flow, Turbulence Combust., 68, 111–135. Burluka, A.A. (2006) Raison d’^etre and general formulation of two-point statistical description of turbulent premixed combustion. C. R. M
ecanique, 334, 474–482. Lipatnikov, A.N. and Chomiak, J. (2005) Molecular transport effects on turbulent flame propagation and structure. Prog. Energy Combust. Sci., 31, 1–73. Burluka, A.A., Griffiths, J.F., Liu, K., and Ormsby, M. (2009) Experimental studies of the rôle of chemical kinetics in turbulent flames. Combustion, Explosion, Shock Waves, 45, 383–391. Dopazo, C. (1994) Recent developments in pdf methods, in Turbulent Reacting Flows (eds P.A. Libby and F.A. Williams), Academic Press, London, pp. 375–473. Burluka, A.A. (2006) Probabilistic methods and physical factors in turbulent premixed combustion, in New Developments in Combustion Research (ed. W.J. Carey), Nova Science Publ., pp. 163–185. Burluka, A.A. and Borghi, R. (1995) Studies of new model for small scale processes in turbulent premixed flames. Archivum Combustionis, 15 (3–4), 229–238.

35 Burluka, A.A., Gorokhovski, M.A., and

36

37

38

39

40

41

42

Borghi, R. (1997) Statistical model of turbulent premixed combustion with interacting flamelets. Combust. Flame, 109, 173–187. Burluka, A.A. (1996) Am
eliorations des modeles de combustion turbulente en milieu pr
em
elang
e. PhD thesis, University of Rouen, France. Borghi, R. and Pourbaix, E. (1981) On the coupling of complex chemistry with a turbulent combustion model. Physico-Chem. Hydrodyn., 2, 65–77. Chen, Y.-Ch. and Bilger, R.W. (2002) Experimental investigation of three-dimensional flame-front structure in premixed turbulent combustion – i: Hydrocarbon/air bunsen flames. Combust. Flame, 131, 400–435. Chen, Y.-Ch. and Bilger, R.W. (2004) Experimental investigation of threedimensional flame-front structure in premixed turbulent combustion – ii: Lean hydrogen/air bunsen flames. Combust. Flame, 138, 155–174. Subramaniam, S. and Pope, S.B. (1998) A mixing model for turbulent reactive flows based on euclidean minimum spanning trees. Combust. Flame, 115, 487–514. Liu, K. (2002) Statistical description of turbulent reacting media. PhD Thesis, School of Mechanical Engineering, The University of Leeds. Eswaran, V. and Pope, S.B. (1988) Direct numerical simulations of the turbulent mixing of a passive scalar. Phys. Fluids, 31, 506–520.

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4 Ignition: New Applications to Combustion Studies Valeri I. Golovitchev and Fabian P. K€arrholm

4.1 Introduction

Ignition processes can be divided conventionally in two groups: . .

Autoignition, which takes place when the combustible mixture is preheated (e.g., by compression) on a large scale to the temperature above the autoignition level. Forced ignition, when only a small part of the combustion volume is preheated (e.g., by a spark).

The processes following ignition in combustible mixtures can result in a variety of different combustion regimes, ranging from flame deflagration (slow propagation) to different types of detonations. Some of these combustion regimes, if realized, can be extremely destructive [1]. The determination of initial and boundary conditions under which these combustion regimes can be realized remains a difficult problem in modeling as it requires that the interaction of complex chemical kinetics, molecular transport, turbulence, and heat transfer be accounted for. In the case of multiphase combustion, when the fuel, oxidizer or products are in different phases, the modeling problem becomes even more complex. Without these complications, the ignition process may be only the initial stage of the combustion process of a relatively short duration characterized by the ignition delay times. The modeling philosophy in this case is well established [2], and has proved to be effective for a wide variety of applications. Moreover, many of the software tools required to implement this methodology are now available (see Ref. [3]). One common element of the modeling strategy is a coupling of the large reactive chemical systems described by complex kinetic mechanisms with computational fluid dynamics (CFD) models realized in different geometries, for example in various types of reactors and combustion chambers. In this chapter, this methodology will be followed so as to illustrate the ignition modeling of different fuels under particular conditions: .

The hot spot ignition of gaseous hydrogen/air/steam mixtures occupying compartments connected by narrow passages.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

j 4 Ignition: New Applications to Combustion Studies

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.

. .

The autoignition of liquid automotive fuels (diesel oil, n-heptane, biodiesel fuel) in a constant-volume vessel under conditions typical for internal combustion engines (ICEs). Evaluation of the impact of the direct injection of a small amount of ethanol into a combustion chamber of a gasoline spark-ignition (SI) engine. Solid aluminum combustion in water/steam that releases heat and pure hydrogen for advanced energy-generating systems.

Starting from the mathematical and physical model formulations, the construction and validation of chemical mechanisms for different fuels, the tools and the results of their applications will be described. The simulation results will then be compared, in some cases with experimental data, and the model applicability for the real cases discussed.

4.2 The CFD Model Formulations

The numerical simulation of combustion phenomena is based on four separate – yet coupled – topics involving multiphase combustion systems: (i) the fluid mechanics, as described by the conservation equations of continuum mechanics; (ii) the chemical reactions between species that comprise the fluid; (iii) spray and droplet dynamics, which is the consequence of interaction between liquid fuel and gas; and (iv) the systems referred to as a set of turbulence modeling equations. The main conservation equations are taken in a form as they are implemented in the three-dimensional (3-D) CFD KIVA-3V code [4]. KIVA, a transient, 3-D, multiphase, multicomponent code for the analysis of chemically reacting flows with sprays has been developed at the Los Alamos National Laboratory. The code uses an Arbitrary Lagrangian Eulerian (ALE) methodology on a staggered grid, which discretizes a space using the finite-volume technique. The code uses an implicit time-advancement, with the exception of the convection terms that are cast in an explicit, second-order monotone scheme. The convection calculations are also sub-cycled in the desired regions to avoid the time step restriction due to Courant conditions. Arbitrary numbers of species and chemical reactions are allowed. A stochastic particle method is used to calculate evaporating liquid sprays, including the effects of droplet collisions and aerodynamic break-ups. Although designed specifically for performing ICE calculations, the modularity of the code allows it to be easily modified for solving a variety of combustion problems. An alternative CFD code used for the comparative study was the C þ þ FOAM [5], which has options for spray combustion modeling. Despite the FOAM code being applied to only one problem, attempts have been made for its use which are consistent, in general, with the numerical and physical models implemented in the KIVA-3V code. Identical combustion mechanisms (n-heptane oxidation) were implemented in both codes.

4.2 The CFD Model Formulations

4.2.1 The Model Formulation: Main Conservation Laws

The conservation equations listed below are given in “advective form,” rather than in “conservative form,” which requires approximations of both the conservative and nonconservative terms. Conservation of mass :

qr þ r
ðruÞ ¼ r_ s qt

ð4:1Þ

Conservation of momentum (a vector equation with three components):
 qðruÞ 1 2
Ao r rk þ r
ðruuÞ ¼ rg þ Fs  2 rp þ r
s qt a 3

ð4:2Þ

Conservation of energy: qðrIÞ c s
: ru þ A0 re þ r
ðruIÞ ¼ r
J þ Q_ þ Q_ pr
u þ ð1A0 Þs qt

ð4:3Þ

Conservation of mass for chemical species m (one equation for each of the Ns species) 
 qrm r þ r
ðrm uÞ ¼ r
rDr m þ r_ cm þ r_ sm dm1 qt r

ð4:4Þ

where m is species index, Ns is the species numbers, the superscripts “c” and “s” denote the terms associated with the chemical reaction and spray, respectively, and dm1 is the Kronecker delta function; that is, species 1 is the species of which the sprays are composed, A0 is zero for laminar flows and unity for turbulent flows. The system of conservation laws (Equations 4.1–4.4) are supplemented by equations of state and the algebraic equations specifying the models of fluid (see Ref. [6]). The turbulence was described by the k-e model, with the velocity dilatation and spray interaction terms. 4.2.2 Turbulent Combustion Modeling

In order to simulate turbulent combustion, the partially stirred reactor (PaSR) method [7] has been employed. To outline the main features of the approach, it is beneficial to consider the average gas-phase equations (Equation 4.4) for a chemically reacting species. As the KIVA-3V code is based on the operation-splitting procedure applied to the mass conservation equations for species participating in any multistep reaction mechanism, the third step of the computational procedure accounts for chemical kinetics coupled with species micro-mixing. This step can be interpreted as representing combustion in a constant-volume PaSR of a computational cell size,

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where reactions occur in a fraction of its volume, as described in terms of the Ordinary Differential Equation (ODE) system: dc 1 ¼ fr ð. . . ; c; . . .Þ; dt

ð4:5Þ

where fr ð. . . ; c; . . .Þ  rc is the chemical source term calculated at some unknown (virtual) concentrations, c, the parameters of a sub-grid scale reaction zone. The species indices are omitted for simplicity. To close the model, the additional equation for the reaction volume can be engaged; that is, dc cc0 cc1 ¼ ¼ þ fr ð. . . ; c; . . .Þ; dt t tmix

ð4:6Þ

where t and tmix are the time integration step and the micro-mixing time, respectively. The model distinguishes between the concentration (in mean molar density) at the reactor exit, c1, the concentrations in the reaction zone, c, and in the feed, c0. When time proceeds, c1 trades place for c0. The difference between Equation 4.6 and that from the perfectly stirred reactor (PSR) [8] model is that the residence time in the reactor equation of the PSR model is replaced by the micro-mixing time. Taking this equation in a steady-state form, it is possible to obtain the basic equations of the PaSR model (Equation 4.6) as follows: dc cc1 ¼ fr ðcÞ ¼ dt tmix

ð4:7Þ

A number of micro-mixing models exists which are based on different principles (for a review, see Ref. [9]). One of the simplest and widely used micro-mixing model is the “Interaction by Exchange with the Mean” (IEM) approach [9], whereby the scalar variable c relaxes to its mean c1 value according to the linear term in Equation 4.7. Then, by rewriting the reaction rate in Equation 4.7 in terms of reactor exit parameters, the following is obtained: fr ðc1 Þ þ ðqfr =qcÞjc¼c1 ðcc1 Þ ¼ fr ðc1 Þ

cc1 tc

ð4:8Þ

using the Taylor’s expansion at the value c1, assuming that the reaction times can be estimated as reciprocal values of the Jacobian matrix elements evaluated at the grid resolved values c ¼ c1; that is, tc  ½qfr =qc1 and accounting for that ðqfr =qcÞjc¼c1 < 0. Algebraic manipulation with the second pair of Equation 4.4 leads to the relationship fr ðc1 Þ

cc1 cc1 ¼ tc tmix

and, finally, to the main relation of the PaSR model. fr ðcÞ ¼ fr ðc1 Þ

tc tc þ tmix

ð4:9Þ

4.2 The CFD Model Formulations

This means the chemical source terms can be calculated using the averaged species concentrations, if multiplied by the model rate parameters ratio tc =ðtc þ tmix Þ. The application of Equation 4.9 is feasible for the chemical mechanisms of an arbitrary complexity. The model parameters such as tmix and tc are calculated as described in Refs [10, 11]; for example: tmix ¼



0:621 cm k
e Ret

ð4:10Þ

where Ret is the turbulence Reynolds number, and cm is the parameter of the k-e model. It is instructive to note that the rate expression Equation 4.9 treats the reactions in a full complexity, in contrast to the expression used in the “characteristic time” model of combustion [12]: fr;i ðcÞ ¼ 

Yi Yi tc;i þ f tmix

ð4:11Þ

where Yi and Yi are current and equilibrium species, f is a multiplier ranging from 0 to 1 switching from kinetic to turbulent regime of combustion, and the characteristic chemical time is taken as the ignition delay time calculated using detailed chemical mechanism. Sometimes, on the ignition stage of combustion, the Shell ignition model – which operates with artificial species representing low-temperature chemistry – is used. A review of turbulence combustion models is available in Ref. [13], but if the ignition is formally considered as the process preceding the combustion stage, such models are not used in the ignition description. A most effective separation of ignition and combustion stages is realized in Ref. [14], based on the use of an ignition integral calculated with the help of an artificial neural network (ANN) and KIVA-3V (or KIVA4) code. The ignition integral: ðt 1 dt ð4:12Þ IðtÞ ¼ t 0 ign ðT; p; wÞ predicts the ignition onset, as I(t) ¼ 1, using a time history of ignition delays during the process development. The ignition delays are calculated and stored in a special library using the detailed chemical mechanisms for particular fuels. 4.2.3 Finite-Rate Formulation for Reaction Model

If the effects of turbulent fluctuations are ignored, then the reaction rates can be determined by the Arrhenius rate expressions. Consider the r-reaction written in a form as follows: Nr X i¼1

kf ;r

Nr X

kb;r

i¼1

0 ni;r Mi ,

n00i;r Mi

ð4:13Þ

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The molar rate of creation/destruction of i-species in to r-reaction is given by: ! Nr Nr a a g0j;r g00j;r 00 0 ^ Ri;r ¼ Cðn i;r ni;r Þ kf ;r ½Cj;r  kb;r ½Cj;r  ð4:14Þ j¼1

j¼1

In the above, ' represents the net effect of inert collision partners in the third body reactions on the reaction rate. This term is read as: C¼

Nr X

cj;r Cj

ð4:15Þ

j

where n0 i;r are the stoichiometric coefficients for reactants in the r-reaction, n00i;r are stoichiometric coefficients for the product in the r-reaction, Mi denotes i-species, kf ;r is a rate parameter for the forward stage of the r-reaction, kb;r is a rate parameter for the backward stage of reaction r, Cj, are molar concentrations of reactants and products in the r-reaction. g0j;r are forward rate exponents for reactant and product species, g00j;r are backward rate exponents for reactant and product species, and cj;r are the third-body efficiencies of the r-reaction For elementary reactions, gj;r are equal to nj;r ; for global reactions, these values are specified based on the experiment rate data, Nr is the number of species participating in the r-reaction. The formulation of chemical kinetics problems must be supplemented by the equations of state for ideal and thermally perfect gas. The thermodynamic properties required for the calculations are available from the database [15]. 4.2.4 Construction and Validation of Chemical Mechanisms

An important part of the development is a construction and validation of complex chemical mechanisms required to simulate ignition and post-ignition processes. The tools to do this are already available in the Chemkin-3 (or more recent versions) [3] package. For purposes of this chapter, the Senkin [16] and Premix [17] codes of the Chemkin-2 package and XsenkPlot [18] code for data post-processing were used. Since the first-discussed ignition problem was the enhanced flame propagation in the hydrogen/air mixture simulating an accidental explosion, the H2/air mechanism [19] was analyzed. However, when the combustion mechanism consisting of nine species participating in 28 reactions was applied to the high-pressure shock-tube autoignition data [20], the agreement between predictions and measurements was found to be unsatisfactory (see Figure 4.1). As the temperature/reaction rate sensitivity coefficients are known to take maximum values at the moment of autoignition, this makes the ignition delays useful data for “tuning” the reaction mechanisms. Thus, both the path (see Figure 4.2) and sensitivity reaction analysis (see Figures 4.3 and 4.4) were performed. The interpretation of data in Figure 4.2 is straightforward: reactions with the highest rates are depicted by the largest arrows. From Figures 4.3 and 4.4, a huge difference is apparent between the values of temperature/reaction rate sensitivity coefficients, illustrating the most important stages of the reaction mechanism which could be “tuned” to achieve the agreement between predictions

4.2 The CFD Model Formulations

Figure 4.1 Ignition delay in H2/air mixture: predictions and measurements.

and measurements. The mechanism modification included the parameters variation of the key third-body reaction: H þ O2 þ M ! HO2 þ M

since the third body M ¼ Ar must be replaced by N2 in the illustrative example. For other illustrative problems, the combustion mechanisms for automotive fuels (diesel oil, n-heptane, gasoline, etc.) were constructed and, although the mechanisms

Figure 4.2 Reaction pathways of H2/air reactions produced by the XsenkPlot code.

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Figure 4.3 Reaction sensitivity test for the low-pressure conditions.

will not be described in detail, the examples of mechanism performance are presented below. Here, as the mechanisms for practical fuels are too complex for CFD applications, the concept of surrogate fuel [21] has been applied. The composition make-up for real diesel oil is presented in Figure 4.5a, while the ignition properties of the diesel oil surrogate (DOS) are shown in Figure 4.5b, where they are compared with the shock-tube data for its constituent components, nheptane (70%) and toluene (30%). Later on, an n-heptane combustion submechanism has been used in the modeling of the fuel spray injection into constant volume. The autoignition and flame propagation properties for dimethyl ether (DME), ethanol, gasoline and gasoline/ethanol blends are presented in Figures 4.6–4.9, as illustrations of the tools used for ignition simulations. The gasoline surrogate has been considered as a ternary blend consisting of 55% iso-octane, 35% toluene, and 10% n-heptane. The chemical mechanism developed included 127 species participating in 674 reactions, and clearly predicted the ignition delay times (see Figure 4.9).

Figure 4.4 Reaction sensitivity test for the high-pressure condition.

4.2 The CFD Model Formulations

Figure 4.5 (a)Diesel oil composition blend (illustration courtesy of Dr. Ph. Dagaut). (b) Ignition delays versus inverse temperature histories for the diesel oil surrogate and its constituent components compared with shock-tube experimental data of the Stanford group [22].

Finally, the ignition properties of biodiesel fuel (rapeseed methyl ester; RME) were studied. RME is specified as methyl oleate (C19H36O2), and comprises 60% of the “real” RME. The oxidation mechanism has been compiled based on a methyl butanoate ester (mb, C5H10O2) oxidation model [24], supplemented by the submechanisms for two proposed fuel constituent components, methyl decanoate (md, C11H22O2) and n-heptane (C7H16), with the soot and NOx formations being reduced

Figure 4.6 (a) Calculated flame propagation speeds versus equivalence ratios, and (b) calculated ignition delays versus inverse temperature histories for DME/air mixtures compared with shocktube experimental data of the Adomeit group [23], LLNL is Lawrence Livermore National Laboratory.

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Figure 4.7 (a) Predicted flame propagation speeds, and (b) predicted ignition delay times. Po ¼ 4.5 bar for the ethanol/air stoichiometric mixtures.

Figure 4.8 Predicted ignition delays versus inverse temperature histories for: (a) a ternary (63% iso-octane, 20% toluene, 17% n-heptane) mixture; and (b) a gasoline surrogate. Comparison with shock-tube data of the Stanford group [22].

4.3 Ignition CFD Modeling Examples

Figure 4.9 Predicted ignition delays versus inverse temperature histories for (a) the gasoline surrogate (55% iso-octane, 35% toluene, 10% n-heptane) and (b) the gasoline surrogate/ethanol blends, E15 and E85, in comparison with shock-tube data [22].

and “tuned” by using the sensitivity analysis. A special global reaction was introduced to “crack” the main fuel into the constituent components, md, mb, and propyne (C3H4), so as to accurately reproduce the proposed RME chemical formula. The submechanisms were collected in the general mechanism, which consisted of 99 species participating in 411 reactions. Since the experimental data on RME autoignition in air under engine-like conditions are not yet available, the validation is based on the comparison (see Figure 4.10a) of a reduced combustion mechanism constructed with the detailed mechanism available for methyl butanoate. From the present authors’ point of view, all of the results presented illustrate the efficiency of the tools currently available for the construction and analysis of reduced chemical mechanisms that can be used to effect CFD simulations of complex ignition problems.

4.3 Ignition CFD Modeling Examples 4.3.1 Simulation of Enhanced Turbulent Deflagration in the Closed Volume

This model illustrates the possibility to simulate hydrogen/air/steam distributions and flame propagation in the closed connecting compartment, with the inclusion of hydrogen mitigation by igniters using the CFD KIVA-3V code coupled with hydrogen

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Figure 4.10 Autoignition properties. (a) For methyl butanoate ester (mb) calculated using detailed LLNL and reduced chemical mechanisms; (b) For RME and its constituent components, n-heptane, mb, and DOS.

combustion kinetics, as discussed above. The general problem is formulated in Refs [1, 25]. The ignition initiation is the process triggered by an energy source capable of producing flame thermal runaway. The spark ignition was simulated as the increase in specific internal energy within the ignition “window” in the specified ignition cells. If the temperature in the ignition cells reached the prescribed limit before the end of the ignition window, then the energy deposition was terminated at that moment. The classic concept of flame initiation [26] is based on the analysis of a competition between the reaction heat release and conductive heat losses, without accounting for the effects of turbulence and any possible pressure growth. Based on this fact, the ability to predict the external energy deposit sufficiently to drive the initial flame kernel outwards of the initiation region is very restricted. The computer results presented in Figure 4.11, by the temperature distributions, illustrate the classic experiments on accelerating flame propagation in a duct consisting of two sections, one of which contains the orifice plates that reduce the duct cross-section in several locations. With the ignition region placed at the center of the duct, the flame propagation pattern illustrates the fact that flame propagates much faster along the duct section that contains the orifice plates, due to the effect of turbulence produced by flow deformation. The flame speed depends on the passage size and, after passing the duct area constriction, the flame continues to accelerate. Other than the growth of turbulence intensity, an additional effect is related to the existence of a so-called “extended” second limit for hydrogen explosion at subatmospheric pressures, and due to this the flame accelerates when the pressure drops. These modeling results

4.3 Ignition CFD Modeling Examples

Figure 4.11 Temperature plots at different instants, illustrating enhanced flame propagation initiated by hot spots in the duct with the orifice plates.

Figure 4.12 Theoretical and experimental estimates [1] for a possibility of the accidental explosion (flame acceleration regimes) of hydrogen/air/steam mixtures.

can be summarized on the parametric maps similar to that presented in Figure 4.12 (from Ref. [1]). 4.3.2 Modeling of the Flame Lift-Off for Liquid Sprays in the Constant Volume: Comparative Study of KIVA-3V and FOAM Codes

In this modeling, the autoignition of a liquid n-heptane spray injected into a constant volume was described in terms of the Lagrangian–Eulerian approach [27]. This involves several spray models including fuel (n-heptane) injection, spray break-up and atomization, droplet formation, interaction (collision), and evaporation. The fuel

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Figure 4.13 Computation meshes used in the modeling by: (a) KIVA-3V; and (b) FOAM codes.

vapor/air mixture formed due to the effects of turbulent mixing then ignites such that the flame front develops and results in a so-called “flame lift-off phenomenon,” whereby the flame is separated from the injection location. Spray atomization was simulated using the hybrid KH-RT (Kelvin–Helmholtz/Rayleigh–Taylor) model coupled with the “blob” injection model. The meshes used in the simulations are presented in Figure 4.13. Here, the KIVA-3V mesh consisted of 300 000 cells, while the OpenFOAM mesh consisted of only 180 000 cells due the use of a central square region with the fine resolution of a spray injection region. The simulations were performed under the following conditions: the oxygen content in the ambient gas was varied within a range of 8% to 21%, and the ambient gas temperature over a range of 750 to 1300 K; the ambient gas density was equal to 14.8 kg m3. The injector orifice diameter (dinj) was 100 mm, and the injection pressure 1500 bar. Predictions of the two codes were compared to the measurements data [28], as well as to each other. From the temperature distributions presented in Figure 4.14, it follows that a full flame length is over-predicted by the FOAM code, and a simple model was constructed to explain this numerical artifact. By assuming no gradients and constant density conditions, the equations of the k-e turbulence model were reduced to: qrk _ s; ¼ re þ W qt

qre e _ sÞ ¼ ðCe2 re þ Cs W qt k

ð4:16Þ

After algebraic manipulation, the equation for the turbulent length scale can be written as: e qrlt _s ¼ ðCe2 aÞre þ ðaCs ÞW ka1 qt

ð4:17Þ

where the turbulent length scale is defined as lt  ka =e, with a ¼ 1.5. As it is not possible to draw any conclusion about the value of Cs (the spray/ turbulence interaction constant) and its influence on the turbulent length scale,

4.3 Ignition CFD Modeling Examples

Figure 4.14 Flame lengths predicted by the OpenFOAM code. (a) At the ignition onset; (b) For the stabilized flame, comparison with measurements; (c) At 0.80 ms; (d) at 1.30 ms.

Cs ¼ 1.5 was taken to eliminate any unknown effect of droplet turbulence interaction. Thus, by setting Cs ¼ 1.5, Equation 4.17 is reduced to e qrlt ¼ ðCe2 aÞre > 0 ka1 qt

ð4:18Þ

As Ct2 ¼ 1.92 in the k-e model, the turbulent length scale following Equation 4.18 infinitely increases with time. Thus, in order to limit its growth, a constraint on the e-value similar to that used in the KIVA-3V code was introduced into the FOAM code in the injection region:   e  

1 3 2 k2 cm  Pre ðce2 ce1 Þ Ls

that with Ls ¼ dinj improves predictions in Figure 4.15, where the temperature distributions for different oxygen contents in the ambient gas are compared with OH-radiation (chemical radical) snapshots, and in Figure 4.16, where a comparison of predicted and measured global ignition characteristics, ignition delays and flame lift-off lengths, is presented. As can be seen, the model is capable of reproducing the trends in variation of the spray ignition delays and lift-off lengths with changes of the vessel parameters. Both codes used in the modeling produce consistent data, regardless of any differences in the numerical and physical models. Yet, further comparisons with the experimental data are presented in Figure 4.17, showing that n-heptane vapor distributions at different instants after the start of injection (SOI) were predicted with a visible discrepancy which reduces with time. Finally, in Figure 4.18, the spray ignition and combustion development patterns for two instants for different fuels used in the automotive industry are presented. These flame patterns are very different for each fuel, and may require careful selection of the spray model parameters. Apart for the

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Figure 4.15 OH chemiluminescence snapshots from experiments compared to temperature plots from numerical simulations: Panels (a–d) correspond to different times after the start of injection (SOI).

non-sooting fuels such as RME, the flame lift-off length cannot be correctly detected by these measurements. 4.3.3 Modeling of Spark Ignition in SI Gasoline Engine Boosted by Direct Injection of Ethanol

As increases in the efficiency of SI gasoline engines by using supercharging and higher compression ratios is greatly limited by engine knocking, the concept of ethanol-boosted gasoline engine (proposed by the MITgroup) was validated by using

Figure 4.16 Comparison of the OpenFoam predicted and measured (a) ignition delays, and (b) lift-off lengths. The predictions of the KIVA-3V code are also shown.

4.3 Ignition CFD Modeling Examples

Figure 4.17 Fuel vapor distributions at different time after start of injection. (a) 0.49 ms; (b) 0.68 ms; (c) 0.9 ms; (d) 1.1 ms.

CFD modeling. The concept here was to inject ethanol directly into the engine, with the amount of fuel injected being varied according to the need for knock suppression as a result of a high ethanol octane number (>100) and the substantial charge cooling caused by evaporation of the ethanol. Simulations were performed using KIVA-3V code with the chemical mechanism of a gasoline/ethanol blend (129 species, 700 reactions) for the model engine geometry presented in Figure 4.19a. The schematic of the concept is shown in Figure 4.19b. The results presented in Figure 4.20 illustrate the engine performance for different amounts of ethanol injected. If the injection and spark parameters are optimized, the engine performance can be improved with savings of gasoline, as noted in Ref. [29].

Figure 4.18 Autoignition of fuel sprays injected into the constant volume. RME ¼ rapeseed ester; MK1, MK3 are different diesel oils; fuels A–C are diesel oil/water emulsions. Illustration courtesy Mr. R. Lima Ochoterena.

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Figure 4.19 (a) The computational mesh with 100 000 cells used in the engine modeling; (b) A schematic of ethanol direct injection into the engine cylinder.

4.3.4 Modeling of Solid Aluminum Ignition in Steam and Carbon Dioxide

Whilst the use of aluminum as a solid fuel has been recognized since the early 1950s, less recognized is the fact that it can react with the combustion products of energetic

Figure 4.20 Illustration of the flame kernel development in the gasoline SI engine in the absence and presence of ethanol direct injection.

4.3 Ignition CFD Modeling Examples

fuels and propellants (e.g., water and CO2 [30, 31]), and that this has proved to be a vital breakthrough in “green” propulsion technology. Aluminum reacts exothermically with steam following the reaction: 2AlðsÞ þ 3H2 OðgÞ $ Al2 O3 ðsÞ þ 3H2 ;

ðDH ð298 KÞ ¼ 230 kcalmol1 Þ

Later, it is possible, in principle, to burn the H2 produced in the above reaction in O2: 3H2 ðgÞ þ 3=2O2 ðgÞ $ 3H2 OðgÞ;

ðDH ð298 KÞ ¼ 174 kcalmol1 Þ

In summarizing the heat releasing effects of both reactions, the same energy as that released during the solid aluminum combustion in oxygen can be obtained, but under much less restrictive ignition conditions. In order to initiate a solid Al reaction in water or steam, however, it is necessary first to remove the surface oxide film that renders solid Al inert in an oxidizing atmosphere. The most effective way to do this is to machine an aluminum rod so as to produce tiny fragments (chord), as shown in Figure 4.21a. If the aluminum fragments are of an optimal size, then the resultant high combustion temperature will prevent any oxide film formation on the aluminum surface. The ignition delay time, in this case, will be of a purely chemical nature, and exclude the time required to heat and melt the oxide film. This ignition delay time (10 ms) can be seen in Figure 4.21b, where the main species time histories calculated using the Senkin code for the chemical mechanism described globally by the above two reactions are plotted. The mechanism, which consists of 30 species participating in 85 elementary reactions, is detailed in Ref. [33]. If the fragment sizes are sufficiently small, the mechanism can be implemented in the CFD code with the spray/particle models.

Figure 4.21 (a) The schematic of Al/H2O combustor based on a mechanical removal of the oxide layer; (b) Species–time histories during the course of aluminum combustion in water.

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Figure 4.22 (a) Predicted main reactant species and (b) aluminum oxide time histories during the course of aluminum combustion in CO2.

The aluminum combustion in carbon dioxide, CO2, has also a substantial heat release effect (as described in Ref. [32]) as follows: AlðgÞ þ CO2 $ AlO þ CO 2AlO þ CO2 $ Al2 O3 ðsÞ þ CO

The chemical mechanism mentioned above was used to calculate aluminum ignition under the conditions of the Martian atmosphere, which consists of 95% CO2. Removal of the oxide film can be achieved in the same way as described above, which means that the CO2-breathing jet engine may in time become a valid engineering option for trans-atmospheric flights in the Martian atmosphere. The time histories of the reactant and product (different aluminum oxides in different phases) are presented in Figure 4.22. The ignition delay time of Al combustion in CO2 was predicted to be shorter by an order of magnitude than for its combustion in water/ steam.

4.4 Conclusions

The software systems that are currently available to construct, validate, and reduce the detailed chemical mechanism of oxidation for complex fuels represent effective tools for the generation of reduced mechanisms for 3-D CFD simulations of ignition and combustion problems. By using these tools, a number of chemical mechanisms for different fuels (e.g., hydrogen, diesel oil, n-heptane, gasoline and gasoline/ethanol blends, DME and RME) reduced for CFD applications have been constructed. Details of the mechanisms described in this chapter may be downloaded by request [33].

References

In this chapter, mechanisms were implemented into the prototype CFD codes, and applications to ignition problems of a practical interest have been illustrated. Some predictions were compared with experimental data to validate the models used for complicated cases of ignition in multiphase, turbulent environments. The approach developed can be used to integrate the ignition and combustion simulations in the common model. Whilst the term “state of art” depends heavily on individual interpretation, attempts have been made to produce relevant information on ongoing research in this field, and to outline those regions where such modeling requires the greatest improvement. Acknowledgments

These studies were supported by the Engine Research Center, CERC, at Chalmers University of Technology. The authors acknowledge the kind assistance of Mr. Yang in the preparation of this text.

References 1 Breitung, W. (2000) Flame Acceleration

2

3

4

5

6

7

and Deflagration to Detonation Transition to Nuclear Safety. OECD Nuclear Energy Agency Report, NEA/CSNI/R. Kee, R.J. and Miller, J. (1992) A Structured Approach to the Computational Modeling of Chemical Kinetics and Molecular Transport in Flowing Systems. SAND868841, February. Kee, R.J., Ruplay, F.M., Miller, J.A., Coltrin, M.E., Grcar, J.F., Meeks, E. et al. (2000) Chemkin Collection, Release 3.6, Reaction Design, Inc., San Diego, CA. Amsden, A.A. (1997) KIVA-3V: A Blockstructured KIVA Program for Engines with Vertical or Canted Valves. LA-13313MS, Los Alamos, New Mexico 87545. OpenCFD release OpenFOAM, version 1.4 (2007) Available at: http://www. opencfd.co.uk/openfoam/version1.4. html. Holst, M.J. (1992) Notes on The KIVA-II Software and Chemically Reactive Fluid Mechanics. Lawrence Livermore National Laboratory. Golovitchev, V.I., Nordin, N., Jarnicki, R., and Chomiak, J. (2000) 3-D Diesel Spray Simulation Using New Detailed Chemistry Turbulent Combustion Model. SAE Papers 2000-01-1891.

8 Glarborg, P., Kee, R.J., Grcar, J.F., and

9

10 11

12

13

14

Miller, J.A. (1992) PSR: A Fortran Program for Modeling Well-Stirred Reactors, SANDIA Report, SAND868209. Fox, R.O. (1998) On the relation between Lagrangian micromixing models and computational fluid dynamics. Chem. Eng. Process., 37, 521. Frisch, U. (1995) Turbulence, Cambridge University Press. Golovitchev, V.I., Atarashiya, K., Tanaka, K., and Yamada, S. (2003) Towards Universal EDC- Based Combustion Model for Compression Ignition Engine Simulation. SAE Paper 2003-01-1849. Kong, S.C., Marriott, C.D., Rutland, C.J., and Reitz, R.D. (2002) Experiments and CFD Modeling of Direct Injection Gasoline HCCI Engine Combustion. SAE Paper 2002-01-1925. Veynante, D. and Vervisch, L. (2002) Turbulent combustion modelling. J. Prog. Energy Combust. Sci., 28, 193. Aceves, S.M., Flowers, D.L., Cheng, J.-Y., and Babajimopoulos, A. (2006) Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code. SAE Paper 2006-013298.

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15 Bucat, A. (2007) Technion. Available at:

16

17

18

19

20

21

22

23

ftp://ftp.technion.ac.il/pub/supported/ aetdd/thermodyna-mics/. Lutz, A.E., Kee, R.J., and Miller, J.A. (1994) SENKIN: A Fortran Program for Predicting Homogeneous Gas Phase Chemical Kinetics with Sensitivity Analysis. Sandia Report SAND87-8248. Kee, R.J., Grcar, J.F., Smooke, M.D., and Miller, J.A. (1994) A Fortran Program for Modeling Steady Laminar OneDimensional Premixed. Sandia Report SAND85-8240. NIST Xsenkplot (1996) An Interactive Graphics Postprocessor for Numerical Simulation of Chemical Kinetics. Available at: http://www.cstl.nist.gov/ div836/xsenkplot/. O’Connaire, M., Curran, H.J., Simmie, J.M., Pitz, W.J., and Westbrook, C.K. (2004) Comprehensive modeling study of hydrogen oxidation. Int. J. Chem. Kinet., 36, 604–622. Petersen, E.L., Davidson, D.F., R€ohrig, M., and Hanson, R.K. (1995) High-pressure shock tube measurements of ignition times in stoichiometric H2/O2/Ar mixture. Proceedings, 20th International Shock Wave Symposium, July 23–28, 1995, Pasadena, CA. Pitz, W.J., Cernansky, N.P., Dryer, F.L., Egolfopoulos, F.N. et al. (2007) Development of an Experimental Database and Chemical Kinetics models for Surrogate Gasoline Fuels. SAE Papers 2007-01-0175. Gauthier, B.M., Davidson, D.F., and Hanson, R.K. (2004) Shock tube determination of ignition delay times in full-blend and surrogate fuel mixture. Combust. Flame, 139, 300. Pfuhl, U., Fieweger, K., and Adomeit, G. (1996) Self-ignition of diesel relevant hydrocarbon-air mixtures under engine conditions. Proceedings of the 26th Symposium (International) on Combustion, Combustion Institute, p. 781.

24 LLNL (2007) Combustion Chemistry.

25

26

27

28

29

30

31

32

33

Available at: http://www-cmls.llnl.gov/? url¼science_and_technology-chemistrycombustion-mbutanoate. Golovitchev, V.I. and Hansson, J. (1998) Some Trends in Improving Hypersonic Vehicles Aerodynamics and Propulsion. AIAA Paper IS-090. He, L. (2000) Critical conditions for spherical flame initiation in mixtures with high Lewis numbers. Combust. Theor. Model., 4, 152. Peng K€arrholm, F. (2008) Numerical Modeling of Diesel Spray Injection, Turbulence Interaction and Combustion. PhD Thesis, Chalmers University of Technology. Pickett, L.M., Siebers, D.L., and Idicheria, C.A. (2005) Relationship between Ignition Processes and the Lift-off Length of Diesel Fuel Jets. SAE Paper 2005-013843. Golovitchev, V.I. and Rinaldini, C.A. (2008) Development and Application of Gasoline/EtOH Combustion Mechanism: Modeling of Direct Injection Ethanol Boosted Gasoline Engine. Paper SI-15, JSME No. 08-202, COMODIA 2008, Sapporo, Japan. Ingenito, A. and Bruno, C. (2004) Using aluminum for space propulsion. J. Propul. Power, 20 (6), 1056. Milani, M., Montorsi, L., and Golovitchev, V.I. (2008) Combined hydrogen, heat, steam and power generation system. Proceedings of 16th International Conference of the ISTVS, Turin, November 25–28. Yuasa, S., Sogo, S., and Isoda, H. (1992) Ignition and combustion of aluminum in carbon dioxide streams. Proceedings of the 24th Symposium (International) on Combustion, The Combustion Institute, p. 1817. Golovitchev, V.I. (2006) Chalmers University of Technology. Available at: http://www.tfd.chalmers.se/valeri/ MECH.

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5 Heat Transfer in Combustion Systems Jinliang Xu

5.1 Introduction

Expenditures on fossil energy by individuals, commerce, transportation and industry in an industrialized country account for a significant fraction of that country’s gross national product (GNP). A better understanding of combustion systems, using fossil fuels of coal, natural gas and oil, will result in an improved energy utilization efficiency, which might have a significant influence on the country’s economy, and also motivate the development of advanced combustion systems. Hence, one of the most important tasks is to develop mathematical models that describe the complicated processes that occur in combustion systems [1]. Combustion is one of the most difficult processes to deal with in Nature. Generally, it involves a set of simultaneous three-dimensional multiphase fluid dynamics, heat transfer (conduction, convection, and radiation), chemical kinetics, and the turbulent mixing of different species. Although all of these processes or factors are coupled with each other, they must be treated sequentially in order to produce a comprehensive model for a combustion system. Today, state-of-the-art reviews of combustion models are available [1, 2], and significant progress has been made on this topic. However, certain major problems – such as turbulence in reactive flows, particle formation, and some others – remain unresolved. It is well known that three basic heat transfer modes exist, namely conduction, convection, and radiation [3]. Each of these mechanisms can be encountered in industry furnaces or combustors although, depending on the combustor geometry and size, the fuel and the temperature, the contribution of a specific heat transfer mechanism to the total heat transfer will vary from case to case. For instance, conduction heat transfer is very important to decide the heat loss through the furnace or combustor wall surfaces to the environment. The use of a thermal insulation material is often considered to reduce the heat loss. In a supersonic combustion system for military applications, convection heat transfer from the flame or combustion products is important to decide the heat transfer rate to the combustor wall surfaces. Alternatively, in a high-temperature combustor, radiation heat transfer should be

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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paid more attention between the combustion products and solid walls, and among the combustion products. Combined heat transfer modes are frequently encountered in combustion systems [3], one example being the flame impingement studies reported by Chander and Ray [4]. The flame impingement heating of solids has been used for many years, with typical applications including the melting of scrap metal, the shaping of glass, and the heating of metal bars. The same method is also used in metal fabrication and assembly applications, including soldering, brazing, cutting, and welding. Direct flame impingement in industrial furnaces causes a significant enhancement in the convective heat transfer from the combustion products to the load, leading in turn to enhanced chemical reactions between fuel and air. The impinging effect ultimately increases productivity, reduces fuel consumption, and lowers pollutant emissions. Chander and Ray [4] showed that the flow field in impinging jets can be divided into three regimes: the free jet region; the stagnation region; and the wall jet region. The stagnation region is characterized by pressure gradients, which stop the flow in the axial direction and turn it radially outwards. This region is where the static pressure pst exceeds the distant ambient pressure. The boundary of the stagnation region, parallel to the wall, defines the start of the wall region. The wall jet region is free of gradients of mean pressure, and is where the flow decelerates and spreads. As noted by Chander and Ray [4], there are six heat-transfer mechanisms involved in flame impingement studies, namely conduction, convection, thermochemical heat release (TCHR), radiation, condensation, and boiling. Several types of these mechanisms exist; for example, both forced and naturalconvectionwere considered.The relative importanceofa mechanism depends on the experimental conditions; when targets are located inside a furnace, radiation fromthe hot walls isvery important.However, radiation from the environment is negligible for targets located in a large room under ambient conditions. Often, multiple mechanisms are important, although in such cases the relative contribution of each mechanism has not usually been determined. As an example, forced convection is generally coupled with TCHR, whereas in other studies some of the mechanisms have been completely ignored without sufficient justification. In a second example, heat transfer to the surface and inside of ash deposits formed in solid fuel-fired utility boilers was analyzed; Zbogar et al. [5] demonstrated the heat transfer to and through ash deposits. In this case, heat is transferred first from the flue gas to the deposit, and then to the steam inside the tube through the deposit and the tube wall surface. This permits the surface temperature of the deposit to be controlled, and also influences the physical conditions at the deposit surface, for example, if the surface is molten. The deposit surface conditions affect the deposit build-up rate as well as the removal/shedding of deposits; typically, a molten deposit will lead to more efficient particle capture, but it may also flow across the heat transfer surfaces. Heat transfer parameters include: the convective heat transfer coefficient a; the effective thermal conductivity of the deposit keff; and the surface emissivity of the deposit, e. The convective heat transfer coefficient is a function of flow characteristics, and can be calculated using different correlation equations, whereas the other two parameters depend on the deposit properties, and can be calculated using different structure-based models.

5.2 The Three Basic Heat Transfer Modes

The thermal conductivity of porous ash deposits can be assessed by using different models for packed beds; these can be divided into two major groups, depending on the way in which they treat the radiation heat transfer, namely a unit cell model and a pseudohomogeneous model. The most suitable model depends on the deposit structure – that is, whether the deposit is particulate, partly sintered, or completely fused. Therefore, the whole heat transfer system [5] is coupled with the radiation and convective heat transfer from the flue gas to the deposit surface, with conduction heat transfer through the deposit (porous media) and the tube wall surface, and with the convective heat transfer inside the boiler tube. In a combustion chamber, both the convective and radiation heat transfer from the flame and combustion products to the surrounding walls can be evaluated if the flow field, radiative properties, and temperature distributions in the medium and on the walls are available. Usually, the temperature itself is unknown, and as a result of this, the three heat transfer rates are coupled with each other, as in many heat transfer applications. Solution of the thermal energy equation is achieved if the physical and chemical process can be successfully modeled. As noted by Viskanta and Menguc [1], the major processes to be considered in a combustion system may be summarized as: (i) convective and radiation heat transfer; (ii) chemical kinetics; (iii) thermochemistry; (iv) laminar and turbulent fluid dynamics; (v) molecular diffusion; (vi) nucleation; (vii) phase change heat transfer, such as evaporation or condensation; and (viii) surface effects. As the physical and chemical processes are very complicated in combustion systems, and cannot be modeled on the microscale, it is necessary to simulate these processes physically. However, each of these models requires an extensive and separate treatment. In the past, much has been written on the subject of heat transfer, yet no significant discussions have been included on the subject of combustion, generally because the field of heat transfer is very broad. Many of the reports on heat transfer do not specifically consider heat transfer in industrial combustion applications, but rather concentrate on gaseous radiation heat transfer. Heat Transfer in Industry Combustion, written by Charles E. Baukal, deals excellently with heat transfer issues related to combustion [6], whilst Viskanta and Menguc [1] have provided an extensive review on research into radiation heat transfer in combustion systems. The aim of this chapter is to provide a brief review on heat transfer in combustion systems, with the three modes of conduction, convection, and radiation being discussed separately. For this, the basic concept of each heat transfer mechanism is first introduced, after which heat transfer mechanisms as applied to combustion systems are outlined. Finally, the most recent research into heat transfer modes in industrial combustion systems is detailed.

5.2 The Three Basic Heat Transfer Modes

As most heat transfer studies on combustion systems have been conducted from a practical application point of view, it is first necessary to provide a brief discussion of heat transfer physics. Although the research and development of microelectrome-

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chanical systems (MEMS) are less connected with combustion phenomenon (except for microturbine systems used for portable energy output [7]), heat transfer studies on the micro/nanoscale help in understanding the heat transfer physics for combustion. A detailed review on the progress of heat transfer studies at the nanoscale and microscale was recently produced by Carey et al. [8]. Heat transfer physics describes the kinetics of storage, transport, and transformation of energy carriers such as phonon, electron, fluid particle, and photon on the microscale. Sensible heat is stored in the thermal motion of atoms in various phases of matter. The states and their populations of the atomic energy are quantified by the classical and the quantum statistical mechanics (partition function and combinatoric energy distribution probabilities). The transport of thermal energy by energy carriers is fulfilled by their particle, quasi-particle, and wave descriptions; their diffusion, flow, and propagation; and their scattering and transformation encountered as they are traveling. The mechanisms of energy transitions among these energy carriers, and their kinetic rates, are controlled by the match of their energies, their interaction probabilities, and the various limits of the hindering-mechanism rate (kinetics). The conservation of energy describes the interplay among energy storage, transport, and conversion, from the atomic to the continuum scales [8]. In general, an understanding of flow and heat transfer on the nanoscale and microscale relies on the molecular dynamics (MD) and direct simulation Monte Carlo (DSMC). Both, the MD and DSMC models have a basic framework that is relatively simple and physically realistic. The MD method computes each molecule or atom in the computation domain, which requires a large amount of computation resource. In contrast, the DSMC method treats particles, with each particle representing a fixed number of gas molecules; hence, it can be used to compute a relatively large system containing more molecules than can the MD method. The recent development of hybrid models which couple MD and continuum fluid mechanics represents a new trend for investigating the complicated flow and heat transfer, on both small and large scales [9]. The three basic heat transfer mechanisms – conduction, convection, and radiation – are reviewed in the following sections.

5.3 Conduction Heat Transfer 5.3.1 The Basic Concept

Conduction is the process of heat transfer from one part of body at a higher temperature to another part of the same body at a lower temperature, or from one body at a higher temperature to another body at a lower temperature if the two bodies are either in close contact, or the gap between them is very small (on the molecular level) [3]. Physically, conduction heat transfer takes place at the molecular level and involves energy transfer from more-energetic to less-energetic molecules. In the case of a gas, higher-energy molecules collide periodically with lower-energy

5.3 Conduction Heat Transfer

molecules, such that heat is continuously transferred from the higher-temperature regions to lower-temperature regions. In liquids, the molecules are more closely packed together, but the energy transfer mechanism is similar to that of gases. In solids that are nonconductors of electricity, heat is transferred by lattice waves induced by the atomic motion, whereas in solids that are good conductors of electricity the lattice vibration makes a small contribution to the energy transfer. Here, the dominant mechanism is due to the movement of free electrons, which behave in a similar manner to the molecules in gases. From a macroscopic point of view, heat flux due to conduction heat transfer is dT expressed as q ¼
k dT dx , where k is the thermal conductivity of material, and dx is the temperature gradient. A general statement of Fourier’s law of heat conduction gives the heat flux as q ¼
krT [3], where r is the three-dimensional (3-D) del operator, and T is the scalar temperature field. If a one-dimensional system is considered, the heat flux across the temperature difference of (T2
T1) within the corresponding distance of Dx is q ¼ k(T2
T1)/Dx ¼ k/Rth, where Rth is the thermal resistance. If two bodies with thermal conductivities of ka and kb, and distances of La and Lb, are contacted to each other, then the heat flux across the two solid bodies can be easily computed as q ¼ (Th
Tc)/(La/ka þ Lb/Lb) ¼ (Th
Tc)/(Rtha þ Rthb), where Th and Tc are the two temperatures at the outer boundaries, and Rtha and Rthb are the two thermal resistances. Usually, an additional thermal contact resistance of Rc should be incorporated with the two solid conduction thermal resistances. An expression for the 3-D thermal conduction equation in the rectangular coordinate system is written as:


 qT q qT q qT q qT ¼ k þ k þ k þw ð5:1Þ rCp qt qx qx qy qy qz qz where r is the density, Cp is the specific heat at constant pressure, and w is the internal heating source. The conduction heat transfer equation in cylindrical and spheric coordinate systems can be found in Ref. [10]. Usually, there are three types of boundary conditions subjected to Equation 5.1: (i) the temperature at the body boundary (interface) is Tw ¼ f1(t) when t > 0, where t is the time; (ii) the heat flux at the body boundary is
k qT qn jw ¼ f2 ðtÞ; and (iii) the heat transfer coefficient at the

boundary is a to give
k qT qn jw ¼ aðTw
Tf Þ, where Tf is the fluid temperature subjected to the body boundary. The solution of Equation 5.1 depends on many factors, including the body geometries and sizes, boundary conditions, internal heating source, and thermal conductivities (whether constant or temperature-dependent), with limited analytical solutions having been proposed [10]. Engineering applications involve many steady-state conduction heat transfer processes, either within or outside solids, or between two contacting solids, with shape factors and thermal resistances being used to handle such situations. For instance, the heat transfer rate from one body at temperature T1 to another body at temperature T2 can be written as: Q ¼ kSðT1
T2 Þ

ð5:2Þ

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where Q is the heat transfer rate in the unit W, and S is the shape factor. The shape factors of isothermal, 3-D convex bodies with complex shapes and small to large aspect ratios are of considerable interest in furnaces and combustors. The general expressions of shape factors formulated in orthogonal curvilinear coordinates are listed in Ref. [10], and are used to develop numerous general expressions in circular, elliptical, and bicylinder, spherical, oblate spheroidal, and prolate spheroidal coordinates. The integral form of the shape factor for an ellipsoid was given to obtain analytical expressions and numerical values for the shape factors of various isothermal geometries. The dimensionless shape factors have been shown to be weakly dependent on the geometries, if the square-root of the total active surface area is selected as the characteristic body length. General dimensionless shape factors for 3D bodies such as cuboids, and two-dimensional (2-D) systems bonded by isothermal regular polygons, internal circles and outer regular polygons, and internal regular polygons and outer circles, are listed in Ref. [10]. Transient internal and external conduction heat transfer to many bodies subjected to the boundary conditions of the first, second, and third type, are presented in Ref. [10], with analytical solutions given in the form of series or integrals. The dimensionless temperature g ¼ q/qi is a function of three variables of dimensionless position z ¼ x/L, dimensionless time Fo ¼ at/L2, and the Biot number Bi ¼ aL/k, depending on the convective boundary condition, where q is the excess temperature. The characteristic length L is the half-thickness of the plate or the radius of cylinder or the sphere. The basic values for the plate and cylinder are used to obtain solutions within rectangular plates, cuboids, and finite circular cylinders, with the well-known initial and boundary conditions. The Heisler [11] cooling charts for dimensionless temperature ( are written in the series form as: q¼

/ X

An exp ð
d2n FoÞSn ðdn zÞ

ð5:3Þ

n¼1

where dn is the nth eigenvalue. The temperature Fourier coefficients An, space functions, and characteristic equations are listed in Ref. [10]. 5.3.2 Conduction Heat Transfer in Combustion Systems

As noted by Baukal [6], thermal conduction is overlooked when considering heat transfer in combustion systems; indeed, it is not an important heat transfer mechanism within the combustion space. However, it is very important to determine heat loss through the furnace or combustor outer wall surface to the ambient. Conduction is important in the design of microturbine systems for portable energy supplies. Such a system would have a large surface area-to-volume ratio, so that heat loss from the outer wall surface of the microsystem would be quite large [7]. This increased heat loss would, in turn, reduce the thermal efficiency, causing difficulties in igniting the flame in the combustor and also decreasing the flame stability. Thus, a

5.3 Conduction Heat Transfer

careful design of the geometry and dimensions of the microturbine system would be expected. In addition, a thermal insulation material would most likely be used in such a system, with the thermal performance of the system being analyzed by coupling with a heat conduction analysis. A low thermal conduction is also desirable in other applications [6]. For example, in the thermal spallation process a high-intensity flame impinges directly onto a solid so that, immediately below the surface, the solid is close to atmospheric temperature. As a consequence, heat transfer in the solid would be slow, due to its low thermal conductivity, such that a large temperature gradient and thermal stress would be created within the solid itself. Subsequently, a high thermal stress would result in deformation and/or rupture of the solid. Steady-state conduction heat transfer represents an alternative method of measuring heat flux, and may be applied during flame impingement studies. The heat flux is assessed based on the temperature gradient through a piece of metal placed in a gage; whilst a flame is applied to heat one side of the gage, the other side is fixed at a given temperature, using a coolant [6]. Although transient conduction heat transfer is also important in combustion systems, it is often overlooked, and should receive more attention during the start-up of furnaces or combustors, before the steady-state of the thermal performance has been reached [6]. However, an uneven temperature and thermal stress distribution within the solids may cause deformation and rupture of the solid walls. 5.3.3 Reviews of Conduction Heat Transfer

Whilst annual reviews on heat transfer are available in the International Journal of Heat and Mass transfer, research progress on conduction heat transfer during 2003 was detailed in a single publication [12]. The wide variety of topics included: contact conduction/contact heat transfer; micro/nanoscale heat transport and wave propagation; heat conduction in complex geometries; analytical and numerical methods for the solution of various heat transport mechanisms related to conduction heat transfer; experimental and/or comparative studies; thermal stresses; and miscellaneous studies that deal with a variety of applications in conduction [12]: .

Contact thermal resistance: Several studies have dealt with various aspects of contact conduction and contact thermal resistance. These include (in 2003): thermal resistance for applications involving workpiece-die interface for forging [13]; multiconstrictions contact and simplified models [14]; applications to particle-laden polymeric interface materials [15]; and consideration of conforming rough surfaces with grease-filled interstitial gaps [16]. Other studies have included the significance of surface contact effects for multiphase heat transfer [17], heat transfer coefficient function in thermoelastic contact [18], influence of flatness and waviness of rough surfaces [19], and the measurement of contact parameters [20] for spot welding.

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.

.

.

Heat conduction in complex geometries: Studies have included conduction heat transfer in complex geometries, such as transient thermal load in multilayer structure [21]; steady-state conduction in multilayer bodies [22]; annular fins [23]; the effect of gap size and spacing [24]; and composite slabs [25]. Analytical and numerical methods: Studies have included analytic close form solutions for unsteady heat conduction in 2-D slabs [26]; and inverse solution of one-dimensional (1-D) and 2-D heat conduction [27, 28]. Simulations and numerical techniques using a number of approaches of transfer functions, finite elements, meshless methods, and genetic algorithms have also been performed [12]. Experimental and/or comparative studies: An experimental thermal contact conductance of a bead-blasted SS 304 at light loads [29]; bi-metallic heat switch for space applications [30]; determination of neurocontrol of a heat conduction system [31]; measurements of thermal contact conductance [32]; and thermal contact resistance of aluminum honeycombs [33] have been performed.

5.4 Convection Heat Transfer 5.4.1 The Basic Concept

Convection is considered to be related to the transfer of heat from a body surface to a fluid in motion, or to the heat transfer across a flow plane within the interior of the flowing fluid [3]. If the fluid motion is caused by a pump, a fan, a blower, or some similar device, the process is called forced convection. However, if the fluid motion is induced by the density difference due to the temperature gradient, the process is called free or natural convection. A detailed examination of the heat transfer process shows that, although the bulk fluid motion results in heat transfer, the basic heat transfer mechanism is conduction – that is, the energy transfer is in the form of heat transfer by conduction within the moving fluid. In other words, it is not heat that is being converted, but internal energy. The convection heat transfer would also include the latent heat exchange, which is associated with a phase change between the vapor and liquid states of the fluid, or between the liquid and solid states of the phase change material (PCM). Newton’s law of cooling is written as q ¼ a(Tw
Tf), where a is the convection heat transfer coefficient, and Tw and Tf are the surface and fluid temperatures, respectively. At the wall, the fluid velocity is zero (no-slip boundary condition), and heat transfer will occur by conduction. Thus:  qT  q ¼
k  ð5:4Þ qy y¼0

5.4 Convection Heat Transfer

The convection heat transfer coefficient relating to the temperature gradient at the wall surface is: a¼


kðqT=qyÞjy¼0 Tw
Tf

ð5:5Þ

It must be borne in mind that several concepts are very important, and these are described briefly as follows. 5.4.1.1 Boundary Layer Heat transfer between a solid body and a liquid or gas flow involves the fluid motion. For such a problem, the two fields of heat transfer and fluid motion interact, and in order to determine the temperature gradient and heat transfer coefficient it is necessary to couple the equations of motion and energy conservation. The complete solution of a fluid flow about a body poses significant difficulties for all but the most simple flow geometries. However, a breakthrough was made when Prandtl suggested that the influence of viscosity was confined to an extremely thin region very close to the body surface. The remainder of the flow field could then be treated as inviscid; that is, the potential flow theory. The region close to the body surface is known as the boundary layer; this leads to a simple analysis, based on the layer thickness being quite small relative to the body dimensions. An assumption was made that the fluid adjacent to the body surface was resting relative to the body surface; the boundary layer thickness d was then defined as the region in which the velocity was changed from the free stream value to the zero value at the solid surface. Physically, there is no precise “thickness” to a boundary layer defined in this manner; however, a common method is to define the thickness across which the velocity is changed from zero at the wall surface to 99% of the free stream value. The shear stress within the boundary layer can be written as t ¼ mdu/dy, where m is the viscosity, and du/dy is the velocity gradient perpendicular to the solid wall surface. When heat transfer occurs between the solid surface and the fluid, there is also a thermal boundary layer across which the temperature shows a significant change. Beyond the thermal boundary layer region, the flow is nonconducting. 5.4.1.2 Laminar and Turbulent Flows There are two types of fluid motion, named laminar flow and turbulent flow. In the example of flow occurring over a flat plate, the flow near the leading edge will be smooth and streamlined, whilst in the boundary layer the velocity at any location will not change with time. The viscous force dominates the flow, such that any small disturbance is suppressed, and the hydraulic boundary layer is increased in relation to the distance from the leading edge. At a critical distance, the inertia force begins to have an important influence on the flow, compared to the viscous force, such that a small disturbance begins to grow and the flow becomes turbulent. This small disturbance may originate from the free stream, or it may be caused by the wall surface roughness.

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In the turbulent flow region, a very effective mixing takes place; that is, macroscopic chunks of fluid move across the streamlines, and transport mass, momentum, and energy in a vigorous manner. One important aspect of the turbulent flow is that any one parameter (e.g., the three velocity components, pressure, temperature, and species concentration) at a given point is not constant over time. The property behaves in a very irregular way, but with high-frequency fluctuations, such that at any
plus a fluctuation time a property X can be expressed as the time-averaged value X
þ X 0 . A steady turbulent flow is achieved if X
does not component X 0 , that is, X ¼ X change with time. The Reynolds number (Re) characterizes the flow performance, which is defined as the inertia force relative to the viscous force, Re ¼ uL/n, where u is the mean average velocity, L is the characteristic length of the flow, and n is the kinematic viscosity. From an applications point of view, it is necessary to determine the critical Re-value at which the transition from laminar flow to turbulent flow appears. The critical Re-value for a pipe flow is about 2300, below which the flow is laminar; however, when the Re-value exceeds 10 000, then a fully turbulent flow is reached. The situation is referred to a transition flow if the Re-value lies between 2300 and 10 000. For flow over a flat plate, the critical Re-value, based on the distance from the leading edge, will range from about 3:5  105 to 106. The general expressions for the conservation of mass, momentum, and energy equations appear in many heat transfer handbooks (e.g., Ref. [3]), and are written as: Dr ¼
rðr  VÞ Dt

ð5:6Þ

r

DV ¼
rp þ r  t þ rg Dt

ð5:7Þ

r

Du ¼
r  q00
pðr  VÞ þ rV : t þ q000 Dt

ð5:8Þ

where V is the velocity vector, u is the internal energy, t is the shear stress vector, p is the pressure, q00 is the heat flux vector, and q000 is the volumetric heat generation. Detailed expressions of these conservation equations in the three typical coordinates – rectangular, cylindrical, and spherical – can be found in Ref. [3], and are omitted here. Dimensionless equations are welcome both for the fundamental and practical purposes by introducing a set of dimensionless parameters. This is important for laminar flow and heat transfer. Solutions of Equations 5.6–5.8 are subject to many boundary conditions, including [3]: (i) a uniform wall temperature, denoted by T; (ii) a convection boundary condition, denoted by T3; (iii) a radiative boundary condition, denoted by T4; (iv) a uniform wall heat flux axially, but uniform wall temperature circumferentially, denoted by H1; (v) a uniform wall heat flux axially and circumferentially, denoted by H2; (vi) a conductive boundary condition, denoted by H4; and (vii) an exponential wall heat flux, denoted by H5.

5.4 Convection Heat Transfer

5.4.2 Forced Convection Heat Transfer

Depending on the development speed of hydrodynamic and thermal boundary layers, four types of flow will occur in channels [34], namely: (i) fully developed; (ii) hydrodynamically developing; (iii) thermally developing (hydrodynamically developed and thermally developing); and (iv) simultaneously developing (both hydrodynamically and thermally developing). The term “fully developed flow” refers to fluid flow in which neither the velocity nor dimensionless temperature profiles change along the flow direction. As a result, both the friction factor and Nusselt number are constant. Similar to laminar flow in ducts, turbulent flow is divided into the four types, governed by the development of the two boundary layers. However, because the two entrance lengths for the hydrodynamic and thermal boundary layers are much shorter than those for the laminar flow in ducts, a fully developed flow is frequently assumed in engineering calculations. An exception is permitted, however, when the fluid Prandtl number is less than unity, under which condition the entrance effect is very important. In this section, some examples of analytical solutions of laminar flow and heat transfer in circular tubes have been described, as subjected to the seven types of boundary condition described in Section 4.1. For such problems, two dimensionless axial lengths characterizing the development of the hydrodynamic and thermal boundary layer are used. Here, the first term, x þ , denotes hydrodynamic developing flow, and is expressed as x þ ¼ x/(DhRe); the other term is x , which characterizes the thermal developing flow, and is expressed as x ¼ x þ /Pr, where Dh is the hydraulic diameter of the tube, and Pr is the Prandtl number. It might be necessary to refer to Ref. [34] for laminar flow and heat transfer in various ducts other than circular tubes, as well as turbulent flow and heat transfer in ducts. Here, the Nusselt number is defined as Nu ¼ aDh/k. 5.4.2.1 Fully Developed Flow The analytical solution for laminar flow and heat transfer in circular tubes gives the Nusselt number of 3.657 (NuT ¼ 3.657) for uniform wall temperature (boundary condition denoted by T). For circular ducts with symmetrical heating, the same heat transfer results in a fully developed flow and developing flow, obtained for boundary conditions H1 through H4. Thus, the uniform heat flux boundary conditions are denoted as the H boundary condition. Without the viscous dissipation and no thermal energy source, the Nusselt number may be given as 4.364. 5.4.2.2 Heat Transfer on Convection Duct Walls When the wall temperature is constant along the flow direction, and the channel has the convection with environment, such a boundary condition is denoted by T3. The dimensionless Biot number (i.e., Bi ¼ aDh/kw) represents the internal conduction resistance relative to the external convection resistance. Hickman [35] developed the following expression to compute the Nusselt number:

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NuT3 ¼

4:3636 þ Bi 1 þ 0:2682Bi

ð5:9Þ

5.4.2.3 Heat Transfer on Radiative Duct Walls This heat transfer problem is subject to the T4 boundary condition. Kadaner et al. [36] obtained the following equation for the fully developed flow under the T4 boundary condition: NuT4 ¼

8:728 þ 3:66SkðTa =Te Þ3 2 þ SkðTa =Te Þ3

ð5:10Þ

where Sk is the dimensionless Stark number (i.e., Sk ¼ ew sTe3 Dh =k), ew is the emissivity of the duct wall surface, s is the Stefan–Boltzmann constant, and Ta and Te are the absolute temperatures of the external environment and the internal fluid at the location of the impingement of the radiation heat flux, respectively. 5.4.2.4 Thermally Developing Flow Heat transfer in a channel with uniform wall temperature is known as the Graetz or Graetz–Nusselt problem. A fluid with a fully developed velocity profile and a uniform temperature flows into the entrance, while viscous dissipation, fluid axial conduction, and energy resources are neglected. Graetz and Nusselt devised the following equations [34]: q¼

1 r  X Tw
T expð
2l2n x  Þ ¼ Cn Rn Tw
Te n¼0 a

ð5:11Þ

1 X Tw
Tm Gn 2  ¼8 2 expð
2ln x Þ Tw
Te n¼0 ln

ð5:12Þ

qm ¼

1 P

Nux;T ¼ 2

n¼0 1 P

Gn expð
2l2n x Þ

ðGn =l2n Þexpð
2l2n x Þ

ð5:13Þ

n¼0

Num;T ¼


ln qm 4x 

ð5:14Þ

where ln, Rn(r/a), and Cn are the eigenvalues, eigenfunctions, and constants, and Gn ¼
ðCn =2ÞR0 n ð1Þ, R0 n ð1Þ is the derivative of Rn(r/a) evaluated at r/a equals to 1, where a is the radius of a circular duct, and r is the radial coordinate in the cylindrical coordinate system. 5.4.2.5 Simultaneously Developing Flow Simultaneously developing flow usually exhibits a moderate Prandtl number. Under such circumstances, the flow velocity and temperature develop along the flow

5.4 Convection Heat Transfer

direction simultaneously, and consequently the heat transfer rate depends heavily on the Prandtl number. Solutions of the developing flow for the uniform wall temperature, uniform heat flux, and convection heat transfer coefficients can be found in Ref. [34]. 5.4.3 Natural Convection Heat Transfer

There are two situations where natural convection heat transfer is important in combustion systems. The first situation is heat transfer from the flame at low gas velocities, while the second is heat transfer from the outside shell to the environment [6]. The natural convection heat transfer uses the Rayleigh number [37], defined as Ra ¼ gb(Tw
Tf)L3/na, assuming a body temperature of Tw, an air environment temperature of Tf, and a volume expansion coefficient of b (b ¼ 1/T for an ideal gas), where g is acceleration due to gravity, and L is the characteristic length, a ¼ k/(rCp). The Nusselt number is a function of the Rayleigh number and Prandtl number for the natural convection heat transfer: Nu ¼ f(Ra, Pr). Churchill and Usagi [38] recommended that the total heat transfer rate could be computed based on the mean Nu-value, relating to both the laminar flow and turbulent flow component as m 1=m . The appropriate value of m, which generally lies in the range Nu ¼ ðNum l þ Nut Þ between 4 and 20, is chosen so as to best fit the experimental data. A variety of theoretical and/or empirical expressions have been reported for different body shapes and arrangements, some of which are cited below: .

Vertical plate with uniform Tw and T f: The total heat transfer rate can be calculated based on the following equation:
l Ra0:25 NuT ¼ C Nul ¼

2:0 lnð1 þ 2:0=NuT Þ

ð5:15Þ ð5:16Þ

Nut ¼ CtV Ra1=3 ð1 þ 1:4  109 Pr=RaÞ

ð5:17Þ

m 1=m Nu ¼ ðNum l þ Nut Þ

ð5:18Þ


l is the function of Pr, and C V is the function of Pr and z, see where m equals to 6, C t Ref. [37] for the determinations. . Vertical plate with uniform heat flux q and constant temperature Tf: The Rayleigh number is modified as Ra ¼ gbqL4 =ðnakÞ. Values of Nux and Nu are computed to obtain the local temperature difference DT and the average temperature difference DTave [37]:
0:2 Pr pffiffiffiffiffi NuTx ¼ Hl ðRax Þ0:2 ; Hl ¼ ð5:19Þ 4 þ 9 Pr þ 10 Pr

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Nul;x ¼

Nut;x ¼

0:4 lnð1 þ 0:4=NuTx Þ

ð5:20Þ

ðCtV Þ3=4 ðRax Þ1=4

ð5:21Þ

1 þ ðC2 Pr=Rax Þ3

m 1=m Nux ¼ ðNum l;x þ Nut;x Þ

ð5:22Þ

where m is equal to 3. . Long vertical cylinders, circular or noncircular: Long vertical cylinders can be found in furnaces and combustors. The objective is to compute the heat transfer from the lateral surface of the long vertical cylinder or wire. Heat transfer from the ends is not considered. For a vertical cylinder with the length of L and diameter D, first calculate the Nusselt number NuT and Nul for a vertical plate of height L, at the constant wall temperature or the constant heat flux boundary conditions. These Nusselt numbers are based on the length L of the plate and are renamed as NuTplate and Nul;plate . The laminar Nusselt number Nul is computed from the following equation for the vertical cylinder [37]. Nul ¼ .

z Nul;plate ; lnð1 þ zÞ

1:8L=D NuTplate

z¼

ð5:23Þ

Long horizontal circular cylinders: For a long isothermal horizontal circular cylinder within a constant temperature environment, Nu can be attained based on the following equations [37]:
l Ra1=4 NuT ¼ 0:772C

Nul ¼

2f ; lnð1 þ 2f =NuT Þ

ð5:24Þ

f ¼ 1


0:13 ðNuT Þ0:16


t Ra1=3 Nut ¼ C m 1=m ; Nu ¼ ðNum l þ Nut Þ

ð5:25Þ

ð5:26Þ m ¼ 10

ð5:27Þ

Equations 5.24–5.27 follow suggestions from Raithby and Hollands [39], except that m is improved. Because the shape is so frequently used in practice, a large body of datasets is available for this problem. Predictions by Equations 5.24–5.27 matched the experimental data by a relative error of less than 10%. In an extensive review on the natural convection heat transfer, Raithby and Hollands [39] presented some fundamental concepts relating to heat transfer: (i) between a body and an ambient fluid such as plates, cylinders, and open cavities; (ii) in fin arrays or through cooling slots; and (iii) in enclosures, such as in the annulus between cylinders. The review also detailed special topics including transient natural

5.4 Convection Heat Transfer

convection, natural convection with internal heating resource, mixed convection, and natural convection in porous media. To date, many theoretical or empirical heat transfer correlations for natural convection heat transfer have been reported in the literature [37]. Most importantly, strict attention must be paid to accuracy, with no equation or correlation being used beyond its range of experimental validation. 5.4.4 Convection Heat Transfer in Combustion Systems

Forced convection is often a very important heat transfer mode in combustion systems, whereas in other cases natural convection is important due to the high temperature gradients involved. In conventional furnace heating processes, forced convection contributes only a small fraction of the total heat transfer to the product [6], with the dominant heat transfer mechanism being radiation from the refractory walls. In flame impingement studies without the furnace enclosure, however, forced convection may contribute 70–90% of the total heat flux [6]. Forced convection heat transfer is also important in gas-fluidized beds, in treating the bed to wall heat transfer, and solid particles to gas heat transfer [40]. In the case of low-temperature flames in air–fuel combustion systems, forced convection is the only mechanism to be considered [6]. In highly dissociated oxygen–fuel flames, a large fraction of the heat release is from the exothermic reactions, but even for this type of flame forced convection remains an important contributor to the overall heat transfer to the target. Notably, turbulence may have a major effect on forced convection, as quantified by monitoring the Reynolds number. Laminar flames are encountered in many flame impingement studies. For example, Sibulkin [41] provided a theoretical relationship for the heat transfer near the forward stagnation point of a body of revolution, assuming a laminar, incompressible, low-speed flow. In this case, the Nusselt number at the stagnation point of a sphere was given as:
NuD ¼ 0:763  Pr0:4 

3u/ D n

0:5 ð5:28Þ

where D is the sphere diameter, and u/ is the free stream velocity. Although Sibulkin’s equation considered only the convection effect, Fay and Riddell [42] subsequently considered both equilibrium and catalytic TCHR. As a consequence, a factor was added that contained a ratio of rm, evaluated at the wall and at the edge of the boundary layer. Thus, the expression for heat transfer at the stagnation point becomes: q ¼ 0:76ðbs re me Þ0:5 ðPre Þ
0:6


  0:6 rw mw 0:1 hC
hC 1 þ ðLeb
1Þ e T w ðheT
hwT Þ re me he ð5:29Þ

where hC, hT are the chemical and total enthalpies, the value of b is 0.52 for equilibrium TCHR and 0.63 for catalytic TCHR, b is the velocity gradient, and

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Le the Lewis number. The subscript e represents the condition at the outer edge of body, and w represents the wall condition. Turbulent flames are often encountered. An empirical expression, including the effect of the turbulence intensity, Tu, was given by Hustad and Sonju [43] as: (
0:25 ) ke 0:6 0:35 0:15 Pre ðTe
Tw Þ q¼ 0:41 Reb;e Pre Tu ð5:30Þ db Prw Equation 5.30 is suitable for flames produced by jets of CH4 and C3H8, into the atmosphere. The flames may be impinged normal to uncooled steel pipes, thus simulating fires caused by ruptured fuel pipes in the petrochemical industry. Yusuf et al. [40] examined selected mechanistic and empirical models reported in the literature to compute the convection heat and mass transfer in gas-fluidized beds. Here, the role of hydrodynamics in heat and mass transfer was outlined before the correlations were introduced. Both, the bed to wall and interphase heat transfer were considered; for bed to wall transfer the review focused on the modeling of particle convection components, based on the surface renewal theory. Commonly, convective heat transfer between the bed and the wall is written as: Q ¼ ac ðTb
Tw ÞA

ð5:31Þ

where ac is the overall convective heat transfer coefficient consisting of both particle and gas components, and Q, Tb, Tw, and A are the heat transfer rate, bed temperature, wall temperature, and heat transfer area, respectively. Mickley and Fairbanks [44] proposed a mechanism for particle convection heat transfer from the wall to bed in a bubbling bed; this was termed the “packet renewal theory.” In this case, the packet of particles transfers the heat through the unsteady conduction, with the packet assumed to serve as a semi-infinite medium. Consequently, the local transient heat transfer coefficient for the particle convection at the wall could be written as: 1 ht ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffi pt ðkrcÞpacket

ð5:32Þ

where t is the residence of the packet at the heat transfer surface, and k, r, c are the thermal conductivity, density, and thermal capacity of the packet, respectively. Baskakov [45] modified the packet theory of Mickley and Fairbanks [44], based on the idea that there is a zone near the wall where the bed properties differ significantly from those of the packet. An additional contact resistance was defined as Rw ¼ dw/kew, where dw and kew are the thickness and effective thermal conductivity of the resistance zone, respectively. The instantaneous and time averaged heat transfer coefficients were modified to be: ht ¼

Rw þ

1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffi pt ðkrcÞpacket

ð5:33Þ

5.4 Convection Heat Transfer

h
t ¼

Rw þ

1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ð5:34Þ

pt 4ðkrcÞpacket

Mickey and Fairbanks’s model was shown to be suitable for the bubbling bed, and the model has also been applied to circulating fluidized beds (CFBs), due to similarities between cluster renewal in CFBs and packet renewal in bubbling beds. Subbarao and Basu [46] proposed a mechanistic model for heat transfer in CFBs which accounted for the convective heat transfer; in this case, the ultimate overall convective heat transfer coefficient was expressed as: ac ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 !0:5 0 kg rg cg ðe
ec Þ @1 þ A 2=3 2=3 k c cc rm ð1
ec Þ pdb rp ð1
ec Þ

4kc cc U 1=3 W 2=3 rm

ð5:35Þ

where U, W, db, e, and ec are the gas superficial velocity, solid circulation rate, bubble diameter, bed void fraction, and cluster void fraction, respectively. The subscripts b, c, g, m, and p represent bubble, circulating bed, gas, average value, and particle, respectively. With regards to interphase heat transfer in convective fluidized beds, the heat transfer coefficient can be defined in two ways: (i) based on the whole surface area of particles or the whole bed coefficient, agp; and (ii) based on a single particle, ap. The volumetric interphase heat transfer coefficient is obtained by multiplying it with the specific interfacial area. Although many experimental correlations of interphase heat transfer have been reported, mechanistic works are very scarce. Gelperin and Einstein [47] produced a theoretical expression for the total heat transfer coefficient in an air-fluidized bed as: Nugp ¼ 0:24 Rep dp =HM

ð5:36Þ

where Rep is the Reynolds number based on the particle diameter of dp, and HM is the initial bed height. A two-zone interphase heat transfer model of bubbling fluidized bed was proposed by Brodkey et al. [48], where the bed was considered to contain a bottom heat transfer zone, followed by an upper zone or the well-mixed zone. In this scheme, the cool solids are heated by a high-temperature gas in the bottom heat transfer zone, such that the solid recirculation transfers the heated solids to the upper zone. A number of assumptions have been made in order to simplify the model, which is expressed as: wg cg ðTi
To Þ ¼ Msp cp

Msp cp

dTs dt

dTs ¼ eAatz ðTi
Ts Þ þ ð1
eÞAauz ðTo
Ts Þ dt

ð5:37Þ

ð5:38Þ

where atz is the equivalent of interphase heat transfer coefficient in the bottom heat transfer zone, and auz is the value in the upper zone. Msp, wg, e, Ti, To, and Ts are the solid mass, gas mass flow rate, void fraction of solid in the bottom zone, inlet temperature, and outlet temperature, respectively.

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5.5 Radiation Heat Transfer

Thermal radiation is an important phenomenon in both Nature and industry. Radiation heat transfer takes place in an environment with or without any medium involved, the heat being transported by electromagnetic waves, which are characterized by the wavelength or frequency, and have the expression of l ¼ c/f, where l is the wavelength, c is the speed of light, and f is the frequency. The electromagnetic wavelength of thermal radiation can range from zero to infinity, but for industrial applications it lies in the range of 0.38 to 100 mm, corresponding to temperatures below 2000 K. Most of the energy transported by radiation takes place in the infrared region, with wavelengths between 0.76 and 20 mm. The thermal radiation contribution is less in the visible light band, with a range of 0.38 to 0.76 mm [3, 6]. Alternatively, thermal radiation incidents can occur within a medium (solid, liquid, or gas), and it can be absorbed, reflected, or transmitted – or some combination of these three. The basic energy conservation equation yields a þ r þ t ¼ 1, where a is the absorptivity of the medium, r is the reflectivity, and t is the transmissivity. Most solids have a low reflectivity, except when they are highly polished. For most solids, the transmissivity is low except for glass or plastics, while liquids (notably fluids with a too-high water content) have significant transmissivity. Most gases will have a very high transmissivity, with negligible absorbance and reflectance. An understanding of these material properties is helpful when determining how much radiation heat can be transferred to and from a medium [6]. 5.5.1 The Basic Concept of Radiation Heat Transfer

Any object at a finite temperature emits electromagnetic energy in discrete energy quanta by photons, with the energy of hf ¼ hc/l for a single photon, where h is the Planck constant, and f is the frequency of the emitted energy. Radiative heat transfer from one small volume or surface element to another is decided on by considering the photon energies of all wavelengths, emitted in all directions over a certain time interval. The radiant energy exchange between elements is different from case to case, depending on the location of each element and its orientation with respect to others. Fundamentally, radiation intensity is the term used to characterize the contribution of each element to the radiation balance. 5.5.1.1 Radiation Intensity The radiation intensity (Il) is defined as the fractional radiant energy del propagating through (or originating from) an infinitesimally small area dAn in the direction of ^ ^ Vðq; wÞ, in an infinitesimally small solid angle dV around Vðq; wÞ, within a wavelength interval dl, around the wavelength of l, and within a time interval of dt [49]: ^ ¼ Il ðq; wÞ ¼ Il ðVÞ

lim

ðdA;dV;dl;dtÞ ! 0

del ðq; wÞ dAn  dV  dl  dt

ð5:39Þ

5.5 Radiation Heat Transfer

The equation is defined in spherical coordinates based on the zenith angle q and azimuthal angle w. Radiation intensity is a function of seven independent ^ variables: the location of rðx; y; zÞ, the direction of Vðq; wÞ, the wavelength of l, and time t. From a practical viewpoint, it is necessary to know the radiative heat flux through a ^; this can be done by integrating the radiation intensity incident surface with normal n from all directions (i.e., 4p steradians) as: ð ql ðrÞ ¼

^ n  VdV ^ Il ðr; VÞ^

ð5:40Þ

V¼4p

^ is the unit vector normal to the surface. where n 5.5.1.2 Blackbody Radiation The black-body is a standard by which to characterize radiation heat transfer, and has been well defined by both theory and experiment. A black-body has the following attributes [49]: . . . .

A black-body is defined as a surface or volume that absorbs all incident radiation at every wavelength and from every direction. The black-body is the best emitter of radiation at every wavelength and in every direction. Radiation emitted by a black-body increases monotonically at every wavelength with absolute temperature. Radiation within an isothermal enclosure with black-body boundaries is isotropic, uniform in all directions.

The black-body is a convenient standard for comparing the properties of real materials, all of which reflect some incident radiant energy and are not perfect absorbers. Real materials do not absorb as much as the ideal black-body, and emit less than an ideal black-body so as to retain the thermal equilibrium state with their surroundings. Thus, a real surface emits less than the black-body. The intensity leaving a black surface is independent of q and w. For a black-body, the Planck distribution gives the spectral intensity of black-body as: Ilb ¼

2C1 5 C2 =nlT 2 n l ðe
1Þ

ð5:41Þ

where T is the absolute temperature in K and C1 ¼ 0:59552  108 W mm4 m
2 and C2 ¼ 14 388 mmK. The energy leaving a black surface in all directions, el,b, has the following expression: el;b 2pC1 ¼ n3 T 5 ðnlTÞ5 ðeC2 =nlT
1Þ

ð5:42Þ

Values of el,b/n3T5 are tabulated in many sources, which achieve maximum value when nlT equals 2897.8 mmK. The total black-body emissive power at all

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wavelengths is ð1 eb ¼ el;b dl ¼ n2 sT 4

ð5:43Þ

l¼0

Equation 5.43 is called the Stefan–Boltzmann equation, and s the Stefan– Boltzmann constant, having the value of 5:6705  10
8 Wm
2 K4 . 5.5.1.3 Nonblack Surfaces and Materials A real material may deviate from the black-body. It is necessary to define the spectral, directional, and temperature-dependence of real surfaces relative to those of a blackbody. 5.5.1.4 Emissivity The ability of a real surface to emit radiation relative to the ideal emission by a blackbody is defined as the emissivity of the surface, and is expressed on a spectral, directional, or total basis. The directional spectral emissivity is written as el ¼ Il =Il;b , while the directional total emissivity is obtained by integrating the emitted energy over all directions at a specific direction: e ¼ pI=sT 4 . Integrating the emitted energy over all wavelengths and directions, and comparing with the integrated value for a black-body, gives the hemispherical total emissivity as: P1 el el;b dl e ¼ l¼0 4 ð5:44Þ sT 5.5.1.5 Absorptivity and Reflectivity Absorptivity is defined as the fraction of incident energy that is absorbed by a surface, and is dependent on the wavelength and direction of the incident radiation. It is also dependent on the temperature. Similar to emissivity, several definitions have been proposed with regards to absorptivity, namely directional spectral absorptivity, directional total absorptivity, hemispherical-spectral absorptivity, and hemispherical total absorptivity. (These definitions are listed in Ref. [49].) Reflectivity represents the fraction of incident energy that is reflected by the surface. It is not only dependent on the directional characteristics and wavelength, but it also describes the directional distribution of the reflected radiation. Many terms relating to reflectivity have been defined, but only some are useful practically, namely bidirectional spectral reflectivity, directional-hemispherical spectral reflectivity, hemispherical spectral reflectivity, bidirectional total reflectivity, directional-hemispherical total reflectivity, and hemispherical total reflectivity (see Ref. [49]). 5.5.2 Radiation Heat Transfer in Combustion Systems

Viskanta and Menguc [1] have produced an extensive review on radiation heat transfer in combustion systems. Radiation heat transfer in a furnace or combustor is complex because: (i) the surface properties are changed during operation of the system; (ii) there is a complicated mixture of gases or particles; and (iii) there are

5.5 Radiation Heat Transfer

nonuniform concentration and temperature distributions present. Notably, each of these factors is coupled with another. Heat transfer in many combustion processes is governed by radiation from the hot refractory walls. The emissivity of the refractory is important to determine the surface radiation heat transfer between the walls, the load, and the flame. When Docherty and Tucker [50] investigated the influence of wall emissivity on furnace performance, they showed that fuel consumption was decreased when the furnace wall emissivity increased. A transient operation or poor wall insulation also reduced the beneficial effects of a high wall emissivity. Elliston et al. [51] noted that the wall surface emissivity had only a minimal impact on heat transfer to a load in a furnace. In this case, when the emissivity of supposedly high-emittance coatings was measured, few of the coatings were found actually to have high emissivities [6], although they did extend the range of refractory emittance from 0.3 to 0.9 at 1300 K. The results of another study showed that high-emissivity coatings, when applied to hundreds of furnaces in metallurgical, petrochemical, ceramic, mechanical, and other industries in China, showed energy savings of 5–10%, with a maximum of 28%. The reported emissivity of these coatings ranged from 0.80 to 0.92, depending on the surface temperature and wavelength [52]. Surface absorptivity can change with respect to time, thus affecting the system performance, and this is especially true in the case of coal-fired furnaces where the ash is deposited on tube surfaces. Wall et al. [53] showed the ash emittance to be heavily dependent on the particle size and surface temperature. Later, Wall et al. [54] studied the effects of ash deposits on the heat transfer in a coal-fired furnace. In this case, heat transfer from the combustion products to the tubes was mainly by radiation, with a lesser amount via convection. Conduction heat transfer occurred through the ash deposits and the tube wall, before the fluid inside the tubes was heated by convection. Because the ash deposit melted to form a slag, the absorbance was increased significantly, to values approaching 0.9. In addition, the thermal conductivity of the deposit was seen to be highly dependent on its physical state, and especially on its porosity. The important conclusion of these studies was that the radiative and conductive properties of ash deposits were found to depend on the physical and chemical characteristics of the deposits [6]. Radiation heat transfer in an enclosure is important in industries, notably because furnace walls usually have high temperatures. Radiation heat transfer with a nonparticipating medium refers to the space inside an enclosure being either a vacuum or to contain a gas (such as air), which is essentially transparent to radiation. This means that the combustion space will not absorb any of the radiation passing through it. However, if a radiatively absorbing gas such as CO2, H2O, or CO is contained within the combustion space, then the medium is referred to as participating, because it will absorb some of the radiation passing through it. During the combustion of fossil fuels, the combustion products normally contain significant quantities of CO2 and H2O, so that the participating media is contained in the combustion space. A simple assumption is often made that the concentrations of participating gases are low enough due to dilution by N2, that the combustion space can be treated as nonparticipating in order to simplify the analysis [6].

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5.5.2.1 Nonluminous Gaseous Radiation The complete combustion of hydrocarbon fuels produces CO2 and H2O, generating nonluminous radiation, which has been studied extensively [6]. The heat transfer depends on the gas temperature level, the partial pressure and concentration of each species, and the molecular path length through the gas. Ludwig et al. [55] have extensively reviewed the theory of infrared radiation from combustion gases, including the terms and their definitions, models for specific molecules (both diatomic and polyatomic), calculation techniques for both homogeneous and inhomogeneous gases, actual computed radiation data, the accuracy of the models, and some predictive techniques for calculating rocket exhausts. The total emissivity is given by Leckner [56]. The individual emissivity of either CO2 or H2O is written as: "
j
 #
 M X N X Tg pa L i e ðpa L; p; Tg Þ ei ðpa L; p; Tg Þ ¼ exp cij log10 p e T ð L Þ o a o o i¼0 j¼0 ð5:45Þ

where ei is the emissivity of the individual gas, pa is the partial pressure of the gas, L is the path length through the gas, Tg is the absolute temperature of the gas, eo is the emissivity of the individual gas at a reference state such as atmospheric pressure, To is the absolute reference temperature of the gas (1000 K), and cij are constants. The second term in Equation 5.45 is given as: ( "

  #) e ða
1Þð1
PE Þ ðpa LÞm 2 ðpa L; p; Tg Þ ¼ 1
exp
c log10 eo a þ b
1 þ PE pa L

ð5:46Þ

where the details of a, b, c, PE, and (paL)m/paL are given in Ref. [6]. The total emissivity is given as: eCO2 þ H2 O ¼ eCO2 þ eH2 O
De

ð5:47Þ

where De is the overlap between the H2O and CO2 bands, and is computed as
De ¼

j
0:0089j10:4 10:7 þ 101j


 ðpH2 O þ pCO2 ÞL 2:76 log10 ðpa LÞo

ð5:48Þ

where j is defined as: j¼

pH2 O pH2 O þ pCO2

The absorptivity of H2O and CO2 is sffiffiffiffiffi
 Tg Ts e pa L ; p; Ts aðpa L; p; Tg ; Ts Þ ¼ Ts Tg

ð5:49Þ

ð5:50Þ

5.5 Radiation Heat Transfer

where Ts is the wall surface temperature. The absorptivity within the band overlap between H2O and CO2 is given as: aCO2 þ H2 O ¼ aCO2 þ aH2 O
De

ð5:51Þ

where De is calculated using a pressure path length of paLTs/Tg. 5.5.2.2 Luminous Radiation The continuous radiant emission of particles in the flame, such as soot, produces luminous flames [6]. An early study by Yagi and Iino [57] showed the soot radiation to be greater than the gaseous radiation for both the luminous (qrs ¼ radiation from soot) and nonluminous (qrg ¼ gaseous radiation) from turbulent diffusion flames. Echigo et al. [58] noted that a rigorous definition of luminous and nonluminous flames was difficult to create. In general, it is assumed that soot remains in the solid phase during the combustion process, and emits a continuous spectrum of visible and infrared radiation [59, 60]. The dehydrogenation and polymerization of hydrocarbon fuels occur in the liquid phase, such that the decomposed and polymerized compound (the “pre-soot substance”) emits banded spectra, after which the soot particles agglomerate when the dehydrogenation and polymerization have been completed. The amount of soot produced in a flame is heavily dependent on the fuel composition. Luminous radiation is important for liquid and solid fuels, such as oil and coal, but is not significant for gaseous fuels, such as natural gas. Sootier flames are easily produced with fuels having higher carbon-to-hydrogen (C: H) weight ratios, and when the temperature range is 1000 to 2500  C [6]. Soot consists of carbon, which is formed into long chains; Tien and Lee [61] reviewed a variety of such models available for the emissivity of luminous flames, including the homogeneous nongray model, the homogeneous gray model, and the nonhomogeneous non-gray model. When Glassman [62] discussed the detailed chemistry of soot formation as it related to fuel composition, it was found that, for fuel gases with a C: H weight ratio of between 3.5 and 5.0, the flame emissivity could be correlated as: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi e ¼ 0:048 MWfuel ð5:52Þ

where MWfuel is the molecular weight of the fuel. The following expression is used for liquid fuels with C: H ratios between 5 and 15: pffiffiffiffiffiffiffiffiffiffi e ¼ 1--68:2 expð
2:1 C=HÞ ð5:53Þ In order to calculate the luminous radiation from soot particles, the extinction coefficient, single-scattering albedo, and phase function in the visible and nearinfrared (NIR) wavelengths are required. The formation and physical properties of soot, light scattering, and extinction by small particles, effects of complex refractive index, particle size distribution, and agglomeration have all been discussed elsewhere [63], while Bockhorn [64] recently described soot formation in combustion, including modeling processes. The properties of a luminous gas can be expressed as: el ¼ 1
e
kL L

ð5:54Þ

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where el is the monochromatic emittance of the luminous gas, kL is the absorption coefficient of the luminous gas, and L is the equivalent length of the radiating system.

5.6 Summary

Combustion phenomena involve many complicated processes, such as multiphase fluid dynamics, heat transfer, chemical kinetics, and the turbulent mixing of different species. Moreover, the interaction of these processes can be highly problematic. To date, three basic heat transfer modes have been identified, namely conduction, convection, and radiation, which have been reviewed in this chapter. Heat transfer analysis is also important in combustion systems, as it may greatly influence not only the chemical kinetics but also energy utilization efficiency. Conduction heat transfer takes place from a higher to a lower temperature location within a body, or from one body at a higher temperature to another body at a lower temperature, if the two bodies are in close contact. In addition to reviewing the basic concepts of conduction heat transfer, the governing equations and the boundary conditions, this chapter has emphasized the importance of thermal resistance and shape factors for various body shapes, sizes, and coordinates. Miniature thermal systems, such as those used in microturbines for portable energy supply, require careful analyses of thermal conduction through the system wall surface, due to their large surface-to-volume ratio. Other applications of conduction heat transfer in combustion systems include the thermal insulation of combustor or furnace walls so as to reduce heat losses from the system, and thermal analyses when such combustion systems are started up. Convection relates to heat transfer from a body surface to a fluid in motion. Forced and natural convections depend on fluid motions induced by an external power source, or by a gravity difference caused by a temperature gradient. The concepts of boundary layer, laminar and turbulent flow, and Reynolds number have been introduced, together with the general conservation equations of mass, momentum, and energy, when subjected to boundary conditions. As a result, some typical analytical solutions of forced and natural convection heat transfer have been provided. Convection heat transfer may be involved in many combustion systems, and analytical and/or empirical correlations of convection heat transfer in typical combustion systems such as gas-fluidized beds, flame impingent studies have been reviewed. Radiation transfers energy by electromagnetic waves. There are many concepts related to radiation heat transfer, including radiation intensity, black-body, emissivity, absorptivity, and reflectivity; notably, these parameters may change during the operation of combustion systems. Surface modifications such as the use of nanoparticle coatings to improve surface radiation parameters may reduce heat losses through the combustor walls. Whereas, the complete combustion of hydrocarbon fuels produces CO2 and H2O, generating nonluminous flames, a continuous emission of particles in flames, in the

References

form of soot, produces luminous radiation. The radiation parameters required to determine radiation heat transfer in combustion systems have been discussed for both of these cases. Acknowledgments

These studies were supported by the National Science Fund for Distinguished Young Scholars from the National Natural Science Foundation of China (No. 50825603).

References 1 Viskanta, R. and Menguc, M.P. (1987)

2

3

4

5

6

7

8

9

Radiation heat transfer in combustion systems. Prog. Energy Combust. Sci., 13, 97–160. Javed, T., Irfan, N., and Ibrahim, M.A. (2010) Combustion kinetic modeling, in Handbook of Combustion (eds M. Lackner, F. Winter and A.K. Agarwal), Vol 1, Ch. 8, Wiley-VCH Verlag GmbH, Weinheim. Cho, Y.I., Ganic, E.N., Hartnett, J.P., and Rohsenow, W.M. (1998) Basic concepts of heat transfer, in Handbook of Heat Transfer, 3rd edn (eds W.M. Rohsenow, J.P. Hartnett and Y.I. Cho), McGraw-Hill, New York. Chander, S. and Ray, A. (2005) Flame impingement heat transfer, a review. Energy Convers. Manage., 46, 2803–2837. Zbogar, A., Frandsen, F.J., Jensen, P.A., and Glarborg, P. (2005) Heat transfer in ash deposits: a modeling too-box. Prog. Energy Combust., 31, 371–421. Baukal, C.E. Jr (2000) Heat Transfer in Industrial Combustion, CRC Press, New York. Cao, H.L. and Xu, J.L. (2007) Thermal performance of a micro-combustor for micro-gas turbine system. Energy Convers. Manage., 48, 1569–1578. Carey, V.P., Chen, G., Grigoropoulos, C., Kaviany, M., and Majumdar, A. (2008) A review of heat transfer physics. Nanoscale Microscale Therm. Eng., 12, 1–60. Xu, J.L. and Li, Y.X. (2007) Boundary conditions at the solid-liquid surface over the multiscale channel size from nanometer to micron. Int. J. Heat Mass Transfer, 50, 2571–2581.

10 Yovanovich, M.W. (1998) Conduction

11

12

13

14

15

16

and thermal contact resistances (conductances), in Handbook of Heat Transfer, 3rd edn (eds W.M. Rohsenow, J.P. Hartnett and Y.I., Cho), McGraw-Hill, New York. Heisler, M.P. (1947) Temperature charts for induction heating and constant temperature. ASME, 69, 227–236. Goldstein, R.J., Ibele, W.E., Patankar, S.V., Simon, T.W., Kuehn, T.H., Strykowski, P.J., Tamma, K.K., Heberlein, J.V.R., Davidson, J.H., Bischof, J., Kulacki, F.A., Kortshagen, U., Garrick, S., and Srinivasan, V. (2006) Heat transfer - a review of 2003 literature. Int. J. Heat Mass Transfer, 49, 451–534. Bourouga, B., Goizet, V., and Bardon, J.P. (2003) Predictive model of dynamic thermal contact resistance adapted to the workpiece-die interface during hot forging process. Int. J. Heat Mass Transfer, 46 (3), 565–576. Degiovanni, A., Remy, B., and Andre, S. (2003) Thermal resistance of a multiconstructions contact: a simple model. Int. J. Heat Mass Transfer, 46 (19), 3727–3735. Prasher, R.S., Shipley, J., Prstic, S., Koning, P., and Wang, J.L. (2003) Thermal resistance of particle laden polymeric thermal interface materials. J. Heat Transfer - Trans. ASME, 125 (6), 1170–1177. Savija, I., Yovanovich, M.M., Culham, J.R., and Marotta, E.E. (2003) Thermal joint resistance of conforming rough surfaces with grease-filled interstitial gaps. J. Thermophys. Heat Transfer, 17 (2), 278–282.

j131

j 5 Heat Transfer in Combustion Systems

132

17 Chen, J.C. (2003) Surface contact-its

18

19

20

21

22

23

24

25

26

27

significance for multiphase heat transfer: diverse examples. J. Heat Transfer - Trans. ASME, 125 (4), 549–566. Jordan-Steen, M., Barber, G.C., and Shillor, M. (2003) The heat exchange coefficient function in thermoelastic contact (c). Lubricat. Eng., 59 (3), 10–16. Kumar, S.S. and Pamamurthi, K. (2003) Influence of flatness and waviness of rough surfaces on surface contact conductance. J. Heat Transfer - Trans. ASME, 125 (3), 394–402. Le Meur, G., Bourouga, B., and Dupuy, T. (2003) Measurement of contact parameters at electrode/sheet interface during resistance spot welding process. Sci. Technol. Weld. Join., 8 (6), 43–52. Chen, Y.M., Wang, S.W., and Zuo, Z. (2003) A procedure for calculating transient thermal load through multilayer cylindrical structures. Appl. Therm. Eng., 23 (16), 2133–2145. Haji-Sheikh, A., Beck, J.V., and Agonafer, D. (2003) Steady-state heat conduction in multilayer bodies. Int. J. Heat Mass Transfer, 46 (13), 2363–2379. Lee, H.L. and Yang, Y.C. (2003) Function estimation in predicting time dependent heat flux of an annular fin. J. Thermal Stress, 26 (8), 799–813. Manners, W. (2003) Effect of gap size and spacing on heat conduction between materials with irregularly spaced plane strip contacts. Proc. Inst. Mech. Eng. Part C - J. Mech. Eng. Sci., 217 (4), 457–466. Nakayama, W. (2003) A methodology to work on geometrically complex heat transfer systems: the cases of heat conduction through composite slabs. Int. J. Heat Mass Transfer, 46 (18), 3397–3409. de Monte, F. (2003) Unsteady heat conduction in two-dimensional two-slabshaped regions, Exact closed-form solution and results. Int. J. Heat Mass Transfer, 46 (8), 1455–1469. Monde, M., Arima, H., Liu, W., Mitutake, Y., and Hammad, J.A. (2003) An analytical solution for two-dimensional inverse heat conduction problems using Laplace transform. Int. J. Heat Mass Transfer, 46 (12), 2135–2148.

28 Monde, M., Arima, H., and Mitsutake, Y.

29

30

31

32

33

34

35

36

37

38

39

(2003) Estimation of surface temperature and heat flux using inverse solution for one-dimensional heat conduction. J. Heat Transfer - Trans. ASME, 125 (2), 213–223. Milanez, F.H., Culham, J.R., and Yovanovich, M.M. (2003) Experimental thermal contact conductance of beadblasted SS304 at light loads. J. Thermophys. Heat Transfer, 17 (4), 534–536. Milanez, F.H. and Mantelli, M.B.H. (2003) Theoretical and experimental studies of a bi-metallic heat switch for space applications. Int. J. Heat Mass Transfer, 46 (24), 4573–4586. Prabhat, P., Look, D.C., and Balakrishnan, S.N. (2003) Experimental model determination for neurocontrol of a thermal conduction system. J. Thermophys. Heat Transfer, 17 (4), 471–480. Rosochowska, M., Balendra, R., and Chodnikiewicz, K. (2003) Measurements of thermal contact conductance. J. Mater. Process. Technol., 135 (2–3), 204–210. Yeh, C.L., Chen, Y.F., Wen, C.Y., and Li, K.T. (2003) Measurement of thermal contact resistance of aluminum honeycombs. Exp. Therm. Fluid Sci., 27 (3), 271–281. Ebadian, M.A. and Dong, Z.F. (1998) Forced convection, internal flow in ducts, in Handbook of Heat Transfer, 3rd edn (eds W.M. Rohsenow, J.P. Hartnett, and Y.I. Cho), McGraw-Hill, New York. Hickman, H.J. (1974) An asymptotic study of the Nusselt-Graetz problem, part 1: large x behavior. J. Heat Transfer, 96, 354–358. Kadaner, Y.S., Rassadkin, Y.P., and Spektor, E.L. (1971) Heat transfer in laminar liquid flow through a pipe cooled by radiation. Heat Transfer - Sov. Res., 3 (5), 182–188. Raithby, G.D. and Hollands, K.G.T. (1998) Natural convection, Handbook of Heat Transfer, in 3rd edn (eds W.M. Rohsenow, J.P. Hartnett and Y.I. Cho), McGraw-Hill, New York. Churchill, S.W. and Usagi, R. (1972) A general expression for the correlation of rates of transfer and other phenomenon. AICHE, 18, 1121–1128. Raithby, G.D. and Hollands, K.G.T. (1985) Natural convection, in Handbook of Heat

References

40

41

42

43

44

45

46

47

48

49

50

51

Transfer Fundamentals (eds W.M. Rohsenow, J.P. Hartnett and E.N. Ganiceds), Chapter 6, McGraw-Hill, New York. Yusuf, R., Melaaen, M.C., and Mathiesen, V. (2005) Convective heat and mass transfer modeling in gas-fluidized beds. Chem. Eng. Technol., 28 (1), 13–24. Sibulkin, M. (1952) Heat transfer near the forward stagnation point of the body of revolution. J. Aero. Sci., 19, 570. Fay, J.A. and Riddell, F.R. (1958) Theory of stagnation heat transfer in dissociated air. J. Aero. Sci., 25, 73–85. Hustad, J.E. and Sonju, O.K. (1991) Heat transfer to pipes submerged in turbulent jet diffusion flames, in Heat Transfer in Radiating and Combusting Systems, Spinger-Verlag, Berlin, pp. 477–490. Mickley, H.S. and Fairbanks, D.F. (1955) Mechanism of heat transfer to fluidized beds. AICHE, 1, 374. Baskakov, A.P. (1964) The mechanism of heat transfer between a fluidized bed and a surface. Int. Chem. Eng., 4, 320. Subbarao, D. and Basu, P. (1986) A model for heat transfer in circulating fluidized-beds. Int. J. Heat Mass Transfer, 29, 487. Gelperin, N.I. and Einstein, V.G. (1971) Heat Transfer in Fluidized Beds (eds J.F. Davidson and D. Harrison), Academic Press, New York. Brodkey, R.S., Kim, D.S., and Sidner, W. (1991) Fluid to particle heat transfer in a fluidized-bed and to single particles. Int. J. Heat Mass Transfer, 34, 2327. Howell, J.R. and Menguc, M.P. (1998) Radiation, in Handbook of Heat Transfer, 3rd edn (eds W.M. Rohsenow, J.P. Hartnett and Y.I. Cho), McGraw-Hill, New York. Docherty, P. and Tucker, R.J. (1986) The influence of wall emissivity on furnace performance. J. Inst. Energy, 59 (438), 35–37. Elliston, D.G., Gray, W.A., Hibberd, D.F., Ho, T.Y., and Williams, A. (1987) The effect of surface emissivity on furnace performance. J. Inst. Energy, 60 (445), 155–167.

52 Xin, X., Qingchang, G., and Yi, J. (1999)

53

54

55

56

57

58

59

60

61

High emissivity refractory coating improves furnace output in China. Ind. Heating, (6), 49–50. Wall, T.F., Bhattacharya, S.P., Zhang, D.K., Gupta, R.P., and He, X. (1993) The properties and thermal effects of ash deposits in coal fired furnaces. Prog. Energy Combust. Sci., 19, 487–504. Wang, T.F., Bhattacharya, S.P., Baxter, L.L., Richards, G., and Harb, J.N. (1995) The character of ash deposits and the thermal performance of furnaces. Fuel Process. Technol., 44, 143–153. Ludwig, C.B., Malkmus, W., Reardon, J.E., and Thomson, J.A.L. (1973) Handbook of Infrared Radiation from Combustion Gases, National Aeronautics and Space Administration Report SP-3080, Washington, DC. Leckner, B. (1972) Spectral and total emissivity of water vapor and carbon dioxide. Combust. Flame, 19, 33–48. Yagi, S. and Iino, H. (1961) Radiation from soot particles in luminous flames, in Eighth Symposium (International) on Combustion, The Combustion Institute, Pittsburgh, PA., pp. 288–293 (reprinted 1991). Echigo, R., Nishiwaki, N., and Hirata, M. (1967) A study on the radiation of luminous flames, in Eleventh Symposium (International) on Combustion, The Combustion Institute, Pittsburgh, PA, pp. 381–389. Deutschmann, O. and Konstandopoulos, A.G. (2010) Catalytic technology for soot and gaseous pollution control, in Handbook of Combustion (eds M. Lackner, F. Winter and A.K. Agarwal), Vol. 2, Ch. 11, WileyVCH Verlag GmbH, Weinheim. Leipertz, A. (2010) Soot and soot diagnostics by laser-induced incandescence, in Handbook of Combustion (eds M. Lackner, F. Winter and A.K. Agarwal), Chapter B15, WileyVCH Verlag GmbH, Weinheim. Tien, C.L. and Lee, S.C. (1982) Flame radiation. Prog. Energy Combust. Sci., 8, 41–59.

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j 5 Heat Transfer in Combustion Systems

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62 Glassman, I. (1988) Soot formation in

combustion process, in Twenty-second Symposium (International) on Combustion, The Combustion Institute, Pittsburgh, PA, pp. 295–311. 63 Ku, J.C. and Shim, K.H. (1990) The effects of refractive indices, size distribution, and agglomeration on the

diagnostics and radiative properties of flame soot particles, in Heat and Mass Transfer in Fires and Combustion Systems, vol. 148 (eds W.L. Grosshandler and H.G. Semerjian), ASME HTD, New York, pp. 105–115. 64 Bockhorn, H. (ed.) (1994) Soot Formation in Combustion, Springer-Verlag, Berlin.

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6 Thermochemistry Elke Goos and Alexander Burcat 6.1 Introduction

Thermochemistry is a section of thermodynamics, which is a macroscopic approach and treats matter in bulk. Thermochemistry deals with chemical equilibria, the final state of chemical processes, where reaction kinetics can provide information concerning the rate at which the final state is reached. Chemical thermodynamics provides information about the amount of heat required for, or released during, chemical processes such as combustion, and the effects of heat on chemical reactions. Therefore, thermochemistry plays an important role in technically relevant fields such as combustion and process engineering, particularly in areas where the transport of heat and materials is dominant. Thermochemical calculations can provide information on the energy efficiency, location and stability of a chemical equilibrium (for example, it can provide information about yields), and also indicate from a theoretical basis, the possibility of a process taking place.

6.2 Thermochemical Properties

Many nonchemists struggle to understand which combination of chemical substances will react when brought together, and which will not. Thermochemistry has the ability to provide an approximate answer to this question. In 1883, Malhard and Le Chatelier [1] demonstrated that the interaction of substances (called reactants) which resulted in the formation of new products, was connected with the release of heat Q (Q < 0 if heat is released, Q > 0 if heat is added). Thus, reactions that release heat will proceed more or less spontaneously (e.g., combustion), while those that absorb heat will not proceed without a heat supply from outside. The heat released when producing 1 mole of a substance from its reference elements at a specified temperature T, and at constant pressure P, is defined as the enthalpy of formation Df HT of the formed product at this temperature. Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

j 6 Thermochemistry

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The enthalpy of formation assigns a certain value, positive or negative, to each compound. By definition, all reference elements (e.g., molecular gaseous hydrogen H2, nitrogen N2, oxygen O2, chlorine Cl2, fluorine F2, crystal and liquid bromine Br2, solid graphite Cgraphite, white phosphorus Pwhite, etc.) in their standard states have each been assigned the value of zero. For a given material or substance, the standard state is the reference state for the material’s thermodynamic state properties such as enthalpy, entropy, Gibbs free energy, and for many other of the compound’s standards. The standard state (IUPAC 1982 [2]) of a gaseous substance is the (hypothetical) state of the pure gaseous substance at standard pressure (1 bar), assuming ideal gas behavior. For a pure phase, a mixture, or a solvent in the liquid or solid state, the standard state is the state of the pure substance in the according phase at standard pressure. The existence of the standard state of a substance in Nature is not mandatory. For instance, it is possible to calculate values for steam at 20
C and 1 bar, even though steam does not exist as a gas under these conditions. However, this definition results in the advantage of self-consistent tables of thermodynamic properties. As mentioned above, the change of standard enthalpy of formation for an element in its standard reference state is zero; hence, this convention allows the enthalpy of compounds to be calculated and tabulated. The enthalpy of a reaction Dr HT is the sum of enthalpies of formation Df HT of all products minus the sum of enthalpies of formation of all reactants: X X D r HT ¼ D f HT  D f HT ð6:1Þ products

react

For instance, for the formation of carbon dioxide (CO2) from its reference elements: Cgraphite þ O2 ! CO2 þ Q

ð6:2Þ

393.5 kJ mol1 of heat are released for a reactor at constant temperature of 298 K (and no pressure change). By using Equation 6.1, Q ¼ 393.5 kJ mol1 ¼ Dr HT ¼ Df H298K ðCO2 Þ fDf H298K ðO2 Þ þ Df H298K ðCgraphite Þg ¼ Df H298K ðCO2 Þf0:0 þ 0:0g an enthalpy of formation of Df H298K ðCO2 Þ ¼ 393.5 kJ mol1 is obtained, since oxygen and graphite are reference elements in its standard states. If the reaction releases heat, then the enthalpy of reaction will be negative; this reaction type is defined as exothermic and, in principle, is possible and will normally occur instantaneously, assuming that the reaction under investigation has no or only an energy barrier, which is small enough to overcome with the available energy, which is defined through the temperature. On the other hand, an endothermic reaction has a positive enthalpy of reaction, and can only take place if there is a particular amount of energy available to absorb, which is equal or larger than the value of the enthalpy of reaction. Assuming a reaction at constant pressure with change in temperature, usually a different heat release is obtained, which means the enthalpy itself is dependent on temperature. This temperature-dependence is related to the sensible enthalpy or sensible heat, which is defined as the heat required to raise the temperature of a substance by 1 K, without changing its molecular structure.

6.2 Thermochemical Properties

The derivative of the enthalpy towards the temperature at constant pressure defines the specific heat capacity, Cp, of a substance:
 qH Cp ¼ ð6:3Þ qT p It is usually easier to measure experimentally Cp rather than the sensible enthalpy H, and therefore it is customary to calculate the enthalpy by integration of Cp; thus: ðT

Cp dT~

HT ðTÞ ¼ H298K þ

ð6:4Þ

298K

and therefore ðT HT H298K ¼

~ Cp dT

ð6:5Þ

298K

The “chemist’s enthalpy” HT H298K is usually found in thermochemical tables; however, in engineering practice the sensible enthalpy is defined arbitrarily as: ðT




HT ðTÞ ¼ Df H298K þ

Cp dT~

ð6:6Þ

298K

This value is usually found in engineering thermodynamics books, in the NASA tables, and the NASA thermochemical polynoms (e.g., Ref. [3]). Since enthalpy is a state function, the heat change associated with a reaction does not depend on the reaction pathway. Therefore, if the reaction proceeds from reactants to products in a single step, or in a series of steps, the same result will be obtained; this is the basis of Hess’s law. A handy combination of reactions enables the calculation of enthalpies of formation of substances, which cannot be measured directly. For instance, the heat of formation of carbon monoxide (CO) through the reaction of graphite with oxygen Cgraphite þ 1=2 O2 ! CO

ð6:7Þ

cannot be measured accurately by calorimetric methods, because a part of the CO will react further to CO2. By contrast, the heat released during oxidation of CO to CO2 CO þ 1=2 O2 ! CO2

ð6:8Þ

is measurable, as well as the released heat for complete combustion of graphite to CO2: Cgraphite þ O2 ! CO2

ð6:9Þ

According to Hess’s law, the enthalpy of formation of CO, which is the enthalpy change for the net reaction (Equation 6.7), can be obtained by subtracting the reaction enthalpy value for the reaction (Equation 6.8) from the enthalpy of reaction for (Equation 6.9).

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Since most of the “formation” reactions for reactive substances are not feasible, their enthalpies of formation are usually obtained from calculations and experimentally derived values. The enthalpy of reaction for complete combustion of substances into the products carbon dioxide and water is called the combustion enthalpy.

6.3 First Law of Thermodynamics

The general form of the first law of thermodynamics is the conversion of energy. Energy can be transformed (changed from one form to another), but it can be neither created nor destroyed. The increase in the internal energy U of a system is equal to the amount of energy added as heat Q to the system, minus the amount of heat lost as a result of the work W done by the system on its surroundings: DU ¼ QW

ð6:10Þ

In an adiabatic process, no heat is transferred to the surrounding (Q ¼ 0). In thermodynamics the convention is that the value of the heat Q is positive, if the working fluid receives heat from the surrounding, while the value of work W is positive if net work is done to the surroundings. Therefore, for adiabatic compression (where Q ¼ 0) the internal energy rises and DU > 0. The internal energy U of a given mass of a material is a measure of its total energy. If at constant pressure, the only work done in a thermodynamic process is the change in volume from the initial state 1 to the final state 2 (e.g., through moving a piston), then the work can be calculated by: ð2 W ¼ PdV ¼ PðV2 V1 Þ

ð6:11Þ

1

where V is the volume of the working fluid. The transferred heat Q within this process corresponds to: Q ¼ ðU2 þ PV2 ÞðU1 þ PV1 Þ

ð6:12Þ

and the quantity U þ PV is defined as the above-mentioned sensible or engineering enthalpy, H.

6.4 Second Law of Thermodynamics

The second law of thermodynamics states that the entropy of an isolated system which is not at equilibrium tends to increase over time. Thus, DS ¼ S2 S1  0

ð6:13Þ

6.5 Third Law of Thermodynamics

where the indices 2 and 1 represent the final and initial states of the system, respectively. The entropy, S, is a measure of molecular randomness, and is defined as:
 dQ S¼ ð6:14Þ T int;rev where dQ is the heat received during an internal reversible process. The units of the entropy are for example, J K1. The entropy is defined as zero for an ideally ordered pure crystal with a temperature at the absolute zero point of 0 K. In this state, the positions of the atoms are totally predictable. In the gas phase at T > 0 K the entropy is much higher, because the molecules and/ or atoms are continuously moving around. The velocity of the movements of the molecules and/or atoms will increase with temperature, as also will the entropy. In contrast, the entropy of a gas mixture will decrease with increase in pressure, as it reduces the possibilities of the molecules to move. Surprisingly, entropy is a material property, as the randomness of a material is determined by temperature and pressure, although at present no method has yet been devised to measure entropy directly. Nonetheless, some relationships can be established to determine the entropy value. Assuming an internally reversible process at constant temperature and pressure, where dQ int;rev dWint;rev ¼ dU

ð6:15Þ

dQ int;rev ¼ TdS

ð6:16Þ

dWint;rev ¼ PdV

ð6:17Þ

with

and results in TdS ¼ dU þ PdV

ð6:18Þ

or dS ¼

dU PdV dT PdV þ ¼ Cv þ T T T T

ð6:19Þ

with the heat capacity Cv at constant volume V.

6.5 Third Law of Thermodynamics

The third law of thermodynamics states that, “As a system approaches the absolute zero point of temperature, all processes cease and the entropy of the system approaches its minimum value of zero.” However, due to the fact that entropy

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increases in all spontaneous processes, it would be impossible to reach absolute zero of temperature.

6.6 Consequences of Thermodynamic Laws to Chemical Kinetics

The second law of thermodynamics also states, that every closed isolated system will approach an “equilibrium” state, where the properties of the system are timeindependent. Thermodynamics, however, is unable to predict the time required to reach equilibrium, nor the system composition and its changes during the time needed before reaching equilibrium. On the other hand, the thermochemical properties represent strong quantitative constraints on the kinetic parameters driving a time-varying system. The reason for this is that an equilibrium state is, in reality, a dynamic state in which on the molecular level chemical changes are still occurring. On the macroscopic level, however, such changes in composition are not observable because the rate of production of a given substance is equal to its rate of destruction. It was shown empirically, that the rate W by which a reaction A þ B ! C þ D occurred would be equal to: Y n W ¼ kf ½C i ð6:20Þ i n

where [C] are the concentrations of the reactants i to the power of n, and kf is the reaction rate coefficient of the forward reaction, the value of which is in Arrhenius form: kf ¼ AT n expðEa =RTÞ

ð6:21Þ

The thermochemistry can help to identify the different values of the Arrhenius reaction coefficient that are given by
 kB T DG exp  ð6:22Þ kf ¼ h RT where kB is the Boltzmann constant, h is Planck’s constant, and DG is the change in Gibbs energy G equal to X X DG ¼ Gprod  Greact ¼ Df HTDS ð6:23Þ i

i

The activation energy Ea in Equation 6.21 is given by Ea  DG ¼ Df HTDS

and DS, the reaction entropy, is given by: X X DS ¼ Sprod  Sreact i

i

ð6:24Þ

ð6:25Þ

6.7 Adiabatic Combustion Temperature

The factor AT n in Equation 6.21 can be found from the same correlations: AT n ¼ kf expð þ Ea =RTÞ

ð6:26Þ

If there is no T n dependency of the reaction rate present, then this factor can be neglected. The equilibrium constant, which is another thermochemical property, can be defined as:
 kf DG ð6:27Þ Keq ¼ ¼ exp  RT kr where kr is the Arrhenius reaction rate coefficient for the backward reaction.

6.7 Adiabatic Combustion Temperature

In a number of engineering applications, such as gas turbine cycles and rocketpropulsion, it is desirable to predict the maximum attainable temperature of the product gas. In the absence of any work done and any kinetic energy change and energy loss (especially heat loss), the maximum temperature is the adiabatic flame temperature or adiabatic combustion temperature of the reacting mixture. The energy balance under adiabatic conditions (and therefore without heat losses) is: h i X X  

ni Df H298K þ ðHTproducts H298K Þ ¼ nj Df H298K þ ðHTreac H298K Þ j i products

i

j reac

ð6:28Þ

As the initial temperature and composition of the reactants are known, then the right-hand side of this equation can be evaluated. Under the assumption of complete combustion, the stoichiometric coefficients ni of the products are also defined. The only unknown numbers in the equation are thenðHT H298K Þi values for the product gases, due to the unknown adiabatic combustion temperature. As the ðHT H298K Þi values are tabulated against temperature (e.g., in JANAF tables [4]), one solution might be to search iteratively for the adiabatic temperature. This could be achieved by using Newton iteration, in combination with an assumed starting temperature and enthalpy values for the products at that particular temperature from the tables. If the numerical values of both sides of the equation are equal, then the adiabatic flame or adiabatic combustion temperature is found. However, in real technical combustion systems, an “adiabatic flame temperature for complete combustion” cannot be achieved. The reasons for this are heat losses to the surroundings, or a very high adiabatic temperature, where further dissociation of combustion products would occur. In general, the latter consumes some of the energy released by the overall reactions, because dissociation reactions are usually endothermic. However, the main reason is that, in real life, combustion is incomplete and therefore

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the ni-values of the products are unknown as they result from a chemical equilibrium of the products. Thus, it is necessary to include the temperature-dependent equilibrium compositions ni in the adiabatic flame temperature calculation. For example, during the combustion of carbon with pure oxygen, mainly CO2 will be formed; however, as another product is CO, then some O2 will also be left over: 1 CðSolidÞ þ 1 O2 ! x CO2 þ y CO þ z O2

ð6:29Þ

At adiabatic flame temperature the reaction products are in equilibrium among themselves, through the dissociation of CO2: CO2 ! CO þ 1=2 O2

ð6:30Þ

The respective equilibrium constant is given by: 1=2

Kp ðTadiabatic Þ ¼

PCO PO2
¼ expðDGr =RTadiabatic Þ PCO2 P
1=2

ð6:31Þ

with the pressure P
at standard conditions.

6.8 Measurement of Thermochemical Values

When using the technique of calorimetry, the time-dependent heat changes of substances or of chemical reaction systems can be measured in an enclosed chamber through the monitoring of temperature changes. Here, as no work is performed in the constant volume chamber, the heat measured will equal the change in internal energy U of the system. Hence, with a known temperature change, the heat capacity Cv at constant volume V can be derived under the assumption, that Cv is constant for the temperature variation measured. q ¼ Cv DT ¼ DU

ð6:32Þ

Since the pressure is not kept constant, the heat measured does not represent the enthalpy change.

6.9 Where to Find Thermochemical Data?

Reliable values for stable gas-phase molecules are relatively well established and have been collected in several compilations, which provide cost-free data for academic usage; examples include the NIST Chemistry Webbook [5] and other databases of thermochemical data. The NIST Chemistry Webbook [5] is a widely used free source of thermodynamics data which lists experimental data with no evaluation. It contains data for over 48 000

6.9 Where to Find Thermochemical Data?

chemical substances, including thermochemical data for more than 7000 organic and small inorganic compounds, such as enthalpy of formation, enthalpy of combustion, heat capacity, entropy, phase-transition enthalpies, and vapor pressure. In addition, there are thermochemistry data for over 8000 reactions such as the enthalpy of reaction and free energy of reaction. Besides other useful data (infrared, UV/visible, electronic, vibrational, mass spectra, ion energetics), the Webbook contains thermophysical property data for more than 70 fluids, including density, specific volume, heat capacity at constant pressure (Cp) and at constant volume (Cv), enthalpy, internal energy, entropy, viscosity, thermal conductivity, and Joule–Thomson coefficients. At present, three other free databases are accessible, which list the main thermochemical properties, specific heat capacities Cp and Cv, enthalpy HT H298K , entropy S, Gibbs free energy G, enthalpy of formation Df HT , and equilibrium “constants” Kc, in both tabular and NASA polynomial forms: .

.

.

The anonymous NASA-Glenn Research Center Multi-Purpose Thermochemical Database [6] (http://cea.grc.nasa.gov) which is a non-annotated database built by Bonnie McBride providing nine-term NASA polynomials. It contains approximately 2000 species, and its development was discontinued in 2002. The Third Millennium Ideal Gas and Condensed Phase Thermochemical Database for Combustion [7], developed at the Technion which lists both seven-term and also nine-term NASA polynomials, and contains more than 2500 species. This database contains a long list of organic radicals not found in other databases. Another free database [8] which lists thermochemical data in tabulated form and seven-term NASA polynomials is provided by Sandia National Laboratories, and is dedicated to compounds with heteroatoms such as Al, B, Be, Ca, Cl, Cr, F, Fe, In, K, Li, Mg, Mn, N, Na, O, Sb, Si, and Sn, as well as some organic molecules and radicals. Species properties were calculated using Bond Additivity Correction Methods. This database contains approximately 1000 species.

The two latter sources are fully annotated and the estimated uncertainty of the thermochemical data is also provided. Theremainder of the sources are found in tabular formin printed compendia. These contain the excellent Russian compendium known as Gurvich’s thermochemical data composed during the early 1970s and translated to English in the early 1990s [9]. Internationally agreed values for thermodynamic properties of key chemical substances were established by the Committee on Data for Science and Technology (CODATA) a few years ago. These have been published [10], while tables of Df H
, S
, H
ð298:15 KÞH
ð0 KÞvalues are available also on the internet [10]. The use of these recommended, internally consistent, values is encouraged in the analysis of thermodynamic measurements, data reduction, and preparation of other thermodynamic tables. The very well-known JANAF Thermochemical tables were openly published in 1972, but contain a remarkable number of errors and old evaluations. The later editions from 1985 and 1998 [11] contained almost no new data, and no correction of errors.

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There are three other compendia: .

. .

The thermodynamics of organic compounds [12] that was published in 1994, and which includes basically all thermodynamic data published by the Texas Thermodynamic A & M Center (TRC) [13] as loose-leaf data between the 1950s and the 1980s, plus some later updates. The German compendium, which was published by Ihsan Barin in 2004 [14] and is devoted mainly to inorganic compounds. The excellent book of Stull, Westrum and Sinke [15], which includes ideal gas calculations of stable organic compounds, between 298K and 1000 K.

6.10 How are the Data Represented?

Thermochemical data such as Df H
, S
, Cp, and HðTÞHð298:15 KÞ are normally tabulated as a function of temperature at selected temperatures, for example 298 K, 500 K, 1000 K, and 2000 K. However, the temperature dependence of the thermochemical properties is not linear. The Cp curve over T has, for most species in the gas state, an S-shape followed by a low, almost linear increase above approximately 1500 K. If any attempt is made to fit a large temperature range with a single parabolic polynomial, then large errors would occur as a result. The thermodynamic properties of each species are therefore mostly represented by so-called NASA polynomials, which allow direct calculation of the thermodynamic properties such as enthalpy, entropy, and heat capacity at any temperature of the defined temperature range, in addition to a limited extrapolation of the thermodynamic properties beyond the fitted range of the polynomial. The temperature dependence of the heat capacity and, to a lesser extent, of the other thermochemical functions, changes its slope; consequently, two polynomial fits are usually given with seven polynomial coefficients, ai (i ¼ 1,. . .7) for two separate temperature ranges (Tlow  T  Tmiddle and Tmiddle  T  Thigh). The thermochemical data are calculated from these fits through the following equations: Cp =R ¼ a1 þ a2 T þ a3 T 2 þ a4 T 3 þ a5 T 4 HT =R ¼ a1 T þ

a2 T a3 T 2 a4 T 3 a5 T 4 þ þ þ þ a6 2 3 4 5

S0 =R ¼ a1 ln T þ a2 T þ

a3 T 2 a4 T 3 a5 T 4 þ þ þ a7 2 3 4

ð6:33Þ ð6:34Þ

ð6:35Þ

where R is the universal gas constant, HT is the sensible enthalpy, S
is standard entropy at 1 atm, and Cp is the heat capacity at constant pressure (p). These are in

6.11 Statistical Thermodynamics

agreement with known relationships among these properties, including: ðT HT ðTÞ ¼ Df H298K þ

Cp dT~

ð6:36Þ

298K

ðT S ðTÞ ¼ 0

Cp d ln T~ þ S
ð298 KÞ

ð6:37Þ

298K

The enthalpy of formation can be calculated by: X Df HT ¼ HT ðTÞ ni HT;i ðTÞ

ð6:38Þ

elements

where ni is the molecular composition of the molecule. 6.10.1 Extrapolation

The use of a polynomial outside the temperature range where it was fitted requires special attention. The uncertainty in the calculated thermochemical properties increases normally, but due to the polynomial form the extrapolated curve may often deviate in the opposite direction to the normal trend of the thermochemical properties! Therefore, special care must be taken if this procedure is required. Specialized methods to perform this task have been reported by Ritter [16].

6.11 Statistical Thermodynamics: Calculation of Thermodynamic Functions from Molecule-Specific Properties (Partition Functions)

In general, thermodynamics deals with macroscopic quantities of a huge number of molecules together, which can be calculated with the help of statistical thermodynamics from the individual microscopic properties (e.g., energy levels) of an ensemble of molecules. The molecular energy levels ei are related to molecular translation (i.e., motion through space), rotation, vibration, and electronic excitation of the specific molecule, and can be obtained in some cases experimentally through analyzing the specific vibrational and rotational spectrum of the molecule. All molecular energy levels are used to compute the partition function Q (note: the symbol Q is used for both heat release and partition function) from the molecule’s specific energy levels ei and the Boltzmann constant kB. X QðTÞ ¼ expðei =kB TÞ ð6:39Þ i

However, a complete set of molecular energy levels is mostly not available (by experiment or calculation).

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The arising problem can be simplified, through the approximation that the different types of motion are unaffected by each other, and are decoupled. This leads to a separation of Q into factors that correspond to separate partition functions for translation, external molecular rotation, hindered and free internal rotation, vibration and electronic excitations, the calculation procedures for which are explained later. Q ¼ Qtrans Qrot Qvib Qelect . . .

ð6:40Þ

In 1940, Mayer and Mayer [17], and later also Irikura in 1998 [18], showed that all basic thermochemical properties: entropy S, heat capacities Cp and Cv and enthalpy H, can be calculated from the partition function Q and Avogadro’s number N. For example, the entropy S is defined as:   q ðT ln QÞln N þ 1 ð6:41Þ S¼R qT with q T qQ ðT ln QÞ ¼ ln Q þ qT Q qT

ð6:42Þ

qQ 1 X ¼ ei expðei =kB TÞ qT kB T 2 i

ð6:43Þ

and

The heat capacity Cp at constant pressure is Cp ¼ Cv þ R

ð6:44Þ

with the heat capacity at constant volume Cv ¼ RT

q2 ðT ln QÞ qT 2

ð6:45Þ

and
 q2 2 qQ T q2 Q T qQ 2 þ ðT ln QÞ ¼  Q qT Q qT 2 Q 2 qT qT 2

ð6:46Þ

q2 Q 2 qQ 1 X 2 þ 2 4 ¼ e exp ðei =kB TÞ qT 2 T qT kB T i i

ð6:47Þ

and

The enthalpy difference relative to absolute temperature of zero Kelvin is calculated from the heat capacity ðT HT H0 ¼ Cp dT ¼ 0

RT 2 qQ þ RT Q qT

ð6:48Þ

6.11 Statistical Thermodynamics

The formula to calculate individual partition functions for translation and rotation of the whole molecule, as well as the partition functions of the vibration, electronic excitation and internal rotation, will be provided in the following paragraphs, as well as their contribution to entropy, heat capacity, and enthalpies. 6.11.1 Translation

The partition function for all translational modes is Qtrans ¼ ð2pmkB TÞ3=2 h3 V

ð6:49Þ

where the sum over all the translational energy levels that are available to a molecule confined to a cubic box of volume V ¼ RT/P (the molar volume of an ideal gas at temperature T and pressure P) was approximated as an integral (where h is Planck’s constant). The resulting thermodynamic functions can be derived through Strans ¼ R½ð3=2Þ ln ð2p  m=h2 Þ þ ð5=2Þ ln ðkB TÞln P þ 5=2

ð6:50Þ

Cp;trans ¼ ð5=2ÞR

ð6:51Þ

½HT H0 trans ¼ ð5=2ÞRT

ð6:52Þ

where m is the mass given in atomic mass units (amu). 6.11.2 Vibrations

The partition function for all vibrational modes is Y Qvib ¼ ½1expðhni =kB TÞ1

ð6:53Þ

i

and the contribution of the vibrational modes to the thermochemical properties is X X
hni  ehni =kB T Svib ¼ R ln ½1expðhni =kB TÞ þ R ð6:54Þ kB T ð1ehni =kB T Þ i i Cp;vib ¼ R

X
hni 2 i

ð1ehni =kB T Þ2

kB T

½HT H0 vib ¼ RT

ehni =kB T

X
hni  i

ehni =kB T kB T ð1ehni =kB T Þ

ð6:55Þ

ð6:56Þ

where ni are the frequencies of molecular vibrations in the simple harmonic oscillator model. A molecule of N atoms has 3N-6 vibrational frequencies, and a linear

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molecule has 3N-5 normal vibration modes. An ideal atomic gas has no vibrational degree of freedom. 6.11.3 External Rotation

For external rotation of a nonlinear molecule the partition function results in rffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 8p2 p 1 3=2 3=2 nonlinear ð6:57Þ Qrot ¼ ð2pk TÞ I I Þ ¼ ðk T=hÞ ðI B A B C B ABC s s  h3 with the rotation constants A

h h h ; B  2 and C  2 2 8p IA 8p IB 8p IC

ð6:58Þ

where s is the “rotational symmetry number” or “external symmetry number” for the molecule. This is the number of indistinguishable unique orientations of the rigid molecule that only interchange identical atoms into which the molecule can be transformed by simple rigid rotations. (For a general discussion and pertinent formulae, see Ref. [19]). The quantities Ix are the principal moments of inertia of the molecules for each of the three dimensions, with the convention IA  IB  IC (and therefore A  B  C). For linear molecules, the rotational partition function is linear Qrot ¼

8p2 I  kB T kT ¼ s  h2 shB

ð6:59Þ

For the typical case of nonlinear molecules, the thermodynamic functions are: Srot ¼ R½ln ð8p2 =sÞ þ ð3=2Þln ð2pkB T=h2 Þ þ ð1=2Þln ðIA IB IC Þ þ 3=2 ¼ R½ð3=2Þln ðkB T=hÞð1=2Þln ðABC=pÞln s þ 3=2

ð6:60Þ

Cp;rot ¼ ð3=2ÞR

ð6:61Þ

½HT H0 rot ¼ ð3=2ÞRT

ð6:62Þ

and should be changed accordingly for the linear case. An ideal atomic gas has no rotational degree of freedom. 6.11.4 Internal Rotation

For molecules with internal rotation such as ethane CH3-’ CH3, the partition function for a free rotor is given by: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 8p3 Iint kB T ð6:63Þ Qfree rotor ¼ sint h

6.11 Statistical Thermodynamics

where 2 Iint ¼ Itop Itop


2  a b2 c2 þ þ IA IB IC

ð6:64Þ

and Itop ¼

X

ð6:65Þ

mi ri2

Here, a, b, and c are the cosines of the angles between the internal rotational axis and the axis of the three external moments of inertia, Ix. Sfree rotor ¼ R½ð1=2Þ ln ð8p3 Ir kB TÞlnðsint hÞ þ ð1=2Þ

ð6:66Þ

Cp; free rotor ¼ ð1=2ÞR

ð6:67Þ

½HT H0 free rotor ¼ ð1=2ÞRT

ð6:68Þ

There are, of course, possibilities of hindered internal rotations, where the friction between the atoms of the internal rotation imposes an energetic barrier. 6.11.5 Electronic

Free radicals usually have unpaired electrons in their electronic ground states, and a net electron spin S (note: the symbol S is used for entropy and electron spin) of the half of the number of unpaired electrons. The multiplicity or degeneracy g of such a state is g ¼ (2S þ 1), which can result for example, in a doublet ground electronic state, although the radicals may not have low-lying electronic excited states. The electronic contributions to the thermochemical properties will be: Q elec ¼

X

Selec ¼ R ln

gi expðei =kB TÞ

X

P Cp; elec ¼ R

P gi ðei =kB TÞexpðei =kB TÞ P gi expðei =kB TÞ þ R gi expðei =kB TÞ

ð6:69Þ ð6:70Þ

!
P  gi ðei =kB TÞ2 expðei =kB TÞ gi ðei =kB TÞexpðei =kB TÞ 2 P P þR gi expðei =kB TÞ gi expðei =kB TÞ ð6:71Þ P

½HT H0 elec ¼ RT

gi ðei =kB TÞexpðei =kB TÞ P gi expðei =kB TÞ

ð6:72Þ

where ei are the energies of excited electronic states and gi is the degeneracy caused by the electron number being even or odd – that is, paired or unpaired electrons.

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6.12 Applications in Research and Industry

Today, thermochemistry is used widely in all branches of chemistry, and specifically in the chemical industry, where knowledge of the amount of energy released or absorbed in a chemical process is critical for plant design. Additionally, thermochemistry is useful when optimizing chemical processes through the optimization of raw materials usage and “waste” management. Likewise, thermochemistry is widely used in combustion kinetics, because the respective processes are highly complex – a reaction mechanism may incorporate hundreds of species and even thousands of elementary reactions. Thus, the simulation of these processes is crucial for the design of combustion applications such as powergenerating facilities and combustion engines. Knowledge of the thermochemistry of stable compounds and many free radicals is required not only to simulate the combustion efficiency of the designed engine, but also to estimate the amount of pollutants formed. Therefore, simulations are also widely used in projects aimed at predicting air pollution, in global warming studies, in research on ultraviolet radiation caused by the “hole” in the ozone layer, as well as in local weather forecasts. Other uses of thermochemistry are found in pharmacology, where it can help in the quest for new drugs, and also highlight the degree of reactivity of a proposed drug. In addition, thermochemistry can be used in evaluating the abrasion properties of metals needed in the development of rockets and satellites, as well as in re-entry problems of spacecraft. Finally, thermochemistry is heavily involved in analytical chemistry, in mass spectrometry, and also in astrophysics; an excellent example of the latter application is in identifying the composition of atmospheres around planets and stars.

6.13 Outlook

Whilst, to a large extent, the future of thermochemistry could be said to be “already here,” it is not yet available for public use. However, a new approach from the Argonne National Laboratories, entitled the “Active Thermochemical Tables” (ATcTs) [20], is currently in its developmental stages. Information contained in the ATcTs is derived by first analyzing and solving a thermochemical network (TN). Such a network does not store the enthalpies of formation per se, but rather stores available experimental and theoretical determinations, such as reaction enthalpies, bond dissociation energies, equilibrium constants, ionization energies, and dissociative photoionization onsets. The ATcTs carries out detailed statistical and simultaneous analyses of the TN, so as to produce thermochemical data that reflect the entire knowledge content of that network. Thus, the addition of new data or additional species can be analyzed and weighed against other pre-existing data, and this will, in turn, allow not only the automatic update of all affected values but also the detection of less-reliable values.

References

At present, it appears that insufficient computing power is available to easily satisfy the processing and memory demands of these network systems. However, when such facilities do (inevitably) become publicly available, the systems will become capable of estimating the best and most accurate thermochemical values. Enthalpies of formation calculated by ATcT are listed in [7].

References 1 (a) Malhard, M.M. and Le Chatelier, H.

2

3

4

5 6

7

8

(1883) Annales de Mines, 4, 273; (b) Malhard, M.M. and Le Chatelier, H. (1883) Annales de Mines, 4, 379. International Union of Pure and Applied Chemistry (1982) Notation for states and processes, significance of the word standard in chemical thermodynamics, and remarks on commonly tabulated forms of thermodynamic functions. Pure Appl. Chem., 54 (6), 1239–1250. Available at: http://media.iupac.org/publications/ pac/1982/pdf/5406x1239.pdf. McBride, B.J., Heimel, S., Ehlers, J.G., and Gordon, S. (1963) Thermodynamic properties to 6000 K for 210 substances involving the first 18 elements; NASA-SP3001. Chase, M.W. Jr, Davies, C.A., Downey, J.R. Jr, Frurip, D.J., McDonald, R.A., and Syverud, A.N. (1985) JANAF Thermochemical Tables, Third edition, J. Phys. Chem. Ref. Data, 14 (Suppl. 1), 1–1856. Available at: http://www.nist.gov/ srd/PDFfiles/jpcrdS1V14.pdf. NIST Chemistry Webbook, http:// webbook.nist.gov/chemistry/. NASA-Glenn Research Center MultiPurpose Thermochemical Database, http://cea.grc.nasa.gov. Burcat, A. and Ruscic, B. Third Millennium Ideal Gas and Condensed Phase Thermochemical Database for Combustion with Updates from Active Thermochemical Tables, ANL-Report -05/ 20 and TAE Report 960. Available at: http://www.thermodata.de. Gas Phase Thermochemical database provided by Mark D. Allendorf from Sandia National Laboratories. Available at: http://www.ca.sandia.gov/ HiTempThermo/.

9 Gurvich, L.V., Veyts, I.V., and Alcock, C.B.

10

11

12

13

14 15

16

17

18

(1989, 1991) Thermodynamic Properties of Individual Substances, vols. 1 and 2, Hemisphere Publishing Corporation, New York. Cox, J.D., Wagman, D.D., and Medvedev, V.A. (1989) CODATA Key Values for Thermodynamics, Hemisphere Publishing Corp., New York. Available at: http://www. codata.org/resources/databases/key1. html. Chase, M.W. Jr (1998) NIST-JANAF thermochemical tables. J. Phys. Chem. Ref. Data Monograph, Monograph No. 9, 4th edition. Frenkel, M., Kabo, G.J., Marsh, K.N., Roganov, G.N., and Wilhoit, R.C. (1994) Thermodynamics of Organic Compounds in the Gas State, Trc Data Series, CRC Press Inc.. Thermodynamic Research Center (1993) Thermodynamics Tables, Texas A&M University, College Station, TX. Barin, I. (2004) Thermochemical Data of Pure Substances, Wiley-VCH. Stull, D.R., Westrum, E.F. Jr and Sinke, G.C. (1969) The Chemical Thermodynamics of Organic Compounds, John Wiley & Sons, New York. Ritter, E.R. (1990) THERM. User’s Manual. Department of Chemical Engineering, New Jersey Institute of Technology, Newark, NJ. Mayer, J.E. and Mayer, M.G. (1940) Statistical Mechanics, John Wiley & Sons, New York (10th Reprint 1963). Irikura, K.K. (1998) Computational Thermochemistry: Prediction and Estimation of Molecular Thermodynamics, ACS Symposium Series 677 (eds K.K. Irikura

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and D.J. Frurip), American Chemical Society, Washington, DC. 19 Herzberg, G. (1945) Molecular Spectra and Molecular Structure II, Infrared and Raman Spectra of Polyatomic Molecules, Van Nostrand, New York, p. 508.

20 Ruscic, B., Pinzon, R.E., Von

Laszewski, G., Kodeboyina, D., Burcat, A., Leahy, D., Montoy, D., and Wagner, A.F. (2005) Active Thermochemical Tables: Thermochemistry for the 21st century. J. Phys.: Conference Series, 16 (1), 561–570.

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7 Combustion Kinetic Modeling Muhammed Tayyeb Javed, Naseem Irfan, and Muhammad Asim Ibrahim 7.1 Introduction

Combustion is a diverse and complex subject that can be viewed in terms of chemical, physical, and mechanical aspects. The chemical aspect involves the very chemistry of the system, which governs the combustion processes that occur in the heart of the flame. The subsequent heat transfer through various regions of the flame and spatial temperature distribution reflects the apparent physical aspects involved. Since molecular diffusion of the various species is as important as the diffusion of heat, the role of the mechanical aspects of a system is to bring together those chemical species that mix intimately by molecular diffusion in flame in which they approach each other sufficiently close so as to enable the chemical reaction to occur. Hence, an overall picture for theoretical modeling of combustion problems involves complex interactions between many sub-constituting disciplines of chemistry, physicsand mechanics, including chemical kinetic, thermodynamics, fluid mechanics, heat and mass transfer, and turbulence (see also Chapters 1–3). Whilst the theoretical formulation of a combustion model requires numerical methods and intricate mathematics, model validation also needs the design of test apparatus for combustion research, instrumentation and data acquisition, analysis, and correlation. Hence, this chapter is divided into three sections. The first section describes combustion modeling in general (the initial theme is taken from references [1–8]), after which a second section provides an overview of the detailed kinetic mechanisms and their implication in combustion modeling. The third section details model validation aspects, by comparison with experimental research data, giving due consideration to flow fields categorized as a perfectly stirred reactor, a plug-flow reactor, a perfectly mixed constant-volume fixed-mass reactor, or a perfectly mixed fixed-mass constant-pressure reactor.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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7.2 Combustion Modeling 7.2.1 General Aspects

A combustion can be expressed in the form of chemical reactions that can be either global or elementary. The global reactions express the chemistry of specific problems as the “black box,” whereas elementary reactions involve radicals or reactive molecules, or atoms that have unpaired electrons. The main aim of combustion modeling is to provide an ability to predict the behavior of combustion systems that are operating under specific physical conditions. While formulating a model based on fundamental concepts, one may interpret and understand the observed phenomenon of combustion. Once a model has been developed and validated, it provides an economic alternate to study the effect of individual parameters on the overall combustion process, without going into difficult and expensive experimentation options. In the initial stages, combustion modeling may provide a guideline in the design and the development of various combustion set-ups, whilst in the later stages a valid combustion model may help in simulating various relevant features of specific combustion processes. Hence, the application of combustion modeling extends from power plants, utility boilers, furnaces, and various process industries, as well as uses in safety and design of household and industrial heating. Furthermore, combustion modeling has a clear role in the evaluation of combustion effects on the environment, including pollutants formation during combustion as well as the estimation of composition and temperature profiles of combustion products that prevail in many combustion set-ups. Combustion modeling concepts are based on various parameters and conditions under which the model is formulated, such as: (i) the combustion velocity, which categorizes whether it is an ordinary combustion, deflagration or detonation; (ii) the degree of compressibility of the flow – that is, whether it is compressible or incompressible; (iii) the flow conditions – that is, whether it is laminar or turbulent; and (iv) whether the combustion is time-dependent – that is, steady or unsteady. It also includes the physical state of the initial reactants: (i) whether they are in single phase or multiphase; (ii) whether they are initially premixed or non-premixed; and (iii) whether finite or infinite rate of reactions are considered. Combustion modeling concepts are also based on heat transfer phenomena associated with the combustion – that is, whether combustion is occurring at an isothermal state or under natural or forced convection conditions. Last, but not the least, it is also important whether in the model, a one-, two-, or three-dimensional case is being considered. As far as the mathematical treatment and physics of a model is concerned, three possible types of mathematical model – each with two subcategories – may be considered: these are the theoretical, semi-empirical, and empirical models. A theoretical model has no adjustable parameters, and may further be categorized as a fundamental theoretical model or a simulation. A fundamental theoretical model has known form, factors, and coefficients, thus providing the highest level of

7.2 Combustion Modeling

understanding by representing the actual physics involved, and can be used to make accurate generalizations about a new situation. A simulation is a computer-generated result based on the solutions of fundamental physical equations. The computational fluid dynamics (CFD) model may be taken as an example, which solves simultaneous equations for mass, momentum and heat transfer based on some simplifications, so as to validate the results. Simulations are validated by comparison with experimental results, and subsequently they may be utilized quite accurately for extrapolating new problems of a similar form. Computational fluid dynamics has proven its ability to simulate fluid flow problems as well as thermal analysis in combustion systems. However, without including detailed chemistry, CFD is not generally valid for quantitative combustion kinetics specifically for the formation and emission of nitrogen oxides and carbon monoxides, although it may provide an idea of general concentration profiles and their production and elimination tendencies. Semi-empirical modeling is the another option in which factors and forms are known but the coefficients involved are explored from the experiments. Theoretical aspects and geometrical considerations, independently or collectively, may be required to define the form of the model. Associating the data obtained from simulation with the empirical model can make the model more compact, swift, and convenient. Dimensionless models are a type of semi-empirical model based on the consideration of units involved such as that of mass, moles, length, temperature, and time. The system physics is assessed by these models only in the sense of understanding the units associated with the phenomena. The models are generally expressed as a power law relationship. Following validation of the behavior of a prototype by using a dimensionless model, a scaling law can be employed to determine the response of a full-scale system. Finally, the pure empirical model, may be further subdivided into quantitative and qualitative empirical models. In a quantitative empirical model all of the factors are known, but the form of the model or the coefficient values are not known. However, it is known qualitatively which factors belong to the model. In a qualitative empirical model, nothing is known with confidence, except the response. Those factors that are important may not be known, although an option is available for the possible factors. The terms mathematical modeling, CFD modeling, turbulence modeling, and combustion modeling are used intermittently for comparative purposes, though each term has its independent interpretation. The use of mathematical modeling in place of others is imprecise, as it properly embraces all analytical models of complex problems, whereas the other terms specifically designate the numerical solution of partial differential equations, which describe the behaviors of reacting or nonreacting fluids. Among these, combustion modeling is more directed towards the chemistry and chemical kinetics, in addition to fluid flow regimes. 7.2.2 History and Emergence of Combustion Modeling

Modern digital computers and computer-based technology have enabled the generalized numerical solution of the complex Navier–Stokes equations, such that today

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the real flow problems of the industry could be undertaken without the need for undue simplification. Back in 1967, Spalding and his coworkers were the first to develop computation-based techniques which resulted in the landmark GENMIX program [9] for calculating boundary layer-type flows. A subsequent report in 1972, on turbulence modeling [10], remains a valuable input to this day, providing usable and fundamental descriptions to enormously intricate physical phenomena. Spalding was also involved in the development of a second landmark computer method, which could be applied to recirculation or elliptic flows [11]. The method was formulated around a solution of the vorticity and stream function variables, and was therefore restricted to two-dimensional (2-D) flows. However, the initial enthusiasm within the engineering community soon faded when it was realized that virtually all of their problems were three-dimensional (3-D). Consequently, methods based on the so-called “primitive variables” – the velocity components and pressure – were quickly evolved [12]. S.V. Patankar, a student of Spalding, also wrote a classic and much-cited volume [13], following the above-mentioned computer codes. The literature that has evolved in the wake of these initial computational methods has concentrated mainly on improved numerical techniques to ensure more reliable convergence and improved precision, and on meshing techniques capable of accommodating the intricate geometry’s of engineering equipment [14, 15]. However, it should be noted that, despite the vast development and applications of finite element methods for solving systems of partial differential equations, the solution schemes deployed in fluid as well as in combustion dynamics have remained firmly based on the finite volume concept. This was primarily because the finite volume method ensures that the principles of conservation, which are fundamental to the analysis of fluid flows, are not violated. Prior to the inclusion of modern mathematical modeling, furnace or combustor design was almost entirely a “black box” technique, and subsequently evolved slowly on the back of expensive and time-consuming experimentation. The only analytical treatment existed was based entirely on Hoyt Hottel, who offered physical understanding with simple zero- or one-dimensional (1-D) analytical treatments [16, 17]. Sarofim, which was the first modern furnace prediction procedure “zone-method” for the calculation of the radiation heat transfer in combustors (and also attributed to Hoyt Hottel and coworkers) [18, 19], was a fully fledged numerical method for the solution of the 3-D integro-partial differential equation which governs radiative transfer. The combination of this method, along with subsequently developed finite volume methods for fluid mechanics, set a foundation for the realization of fundamental predictive methods for furnaces. Although commercial software packages quickly emerged which developed user-friendly versions of mathematical models, engineering users soon found that the technology was inevitably sophisticated and demanded considerable user expertise. More importantly, the predictions included some errors that arose primarily because almost all engineering flows are turbulent. Turbulence modeling has proved to be an extraordinarily complex area, and the development of usable models of turbulence has progressed little over the past twenty years. Today, although while combustors embrace many complex phenomena

7.2 Combustion Modeling

that need to be modeled, turbulence remains a major uncertainty among combustor predictions [8]. Analysis in general becomes considerably complicated by the inclusion of chemical reactions. For example, the combustion of a typical engineering hydrocarbon fuel involves literally hundreds of elementary reactions, as well as significant interactions between the turbulence and the chemistry, notably because of the typical nonlinear dependence of the reaction rate on the fluctuating temperature. Fortunately, the former problem may be overcome in practical applications, as the speed of the overall reaction is much faster than the physical processes, such as mixing. However, when this is not the case, and reactions are slow and involve important pollutants in trace concentrations, then reduced chemistry schemes [8, 20] with the controlling reaction paths appropriately modeled are generally preferred over detailed schemes; an exception might be in the limited application of simple flames that are typical of fundamental combustion studies. Hence, when the chemical rates are not fast with respect to the physical rates, the turbulence and chemistry interaction cannot be ignored. In fact, this represents one of the most challenging aspects of current-day combustion research [21–24] (see Chapter 21). 7.2.3 Combustion Model Components

The central component of a combustion model is a set of governing equations that include conservations equations, equations of state, and transport equations. Conservation equations consist of mass, energy, linear and angular momentum, as well as conservation of molecular and atomic species. Conservation equations are coupled with the equation of state for the determination of flow property distribution. These include the concentration of chemical species, their temperature, density, pressure, and velocity profiles. While developing a combustion model that is meant to predict turbulent combustion problems, transport equations are needed that may include the transport of turbulence kinetic energy and dissipation rate, the transport of turbulent Reynolds stresses and probability density function, and the transport of momentum. These mathematical descriptions, which together form a combustion model, are based upon certain assumptions so as to overcome the complexities that may occur in solution. These include the assumption of ideal gas law applicability, a uniform pressure for low-speed combustion scenario, the validity of Fick’s law of diffusion, and so on. In some simplified models, simple, one-step, forward irreversible elementary reactions are considered at equilibrium conditions, although some of these assumptions may be avoided by using advanced numerical techniques with high-performance computing facilities. The assumptions, empirical input data and correlation are coupled with the set of governing equations, along with other components of the model such as the initial and boundary conditions, reaction mechanism data, thermodynamic, transport, material properties, and structural characteristics. A numerical technique is also incorporated to solve the set of these coupled mathematical equations, using

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a specific convergence criterion in order to obtain the required output in the form of temperature and concentration profiles, flame structure and velocity, and so on [6]. 7.2.4 Combustion Modeling Procedure

When developing a combustion model, the very first step is to short-list and specify the overall aims and objectives for which the model is being developed – that is, whether the ultimate aim is to determine the flame structure or the flame speed or the ignition delay, or simply the concentration and temperature profiles or any other. This is achieved by giving due consideration to the category of the combustion system as well as to the possible physical parameters involved. Subsequently, it is necessary to establish a physical model based upon the understanding of the major mechanisms involved and appropriate basic assumptions. This is followed by the construction of a major framework of the mathematical model by incorporating the conservation and transport equations. In the next step, the other components of the model, such as chemical kinetic mechanism, kinetic rate constants, thermodynamic and transport properties, initial and boundary conditions, must be coupled together. In order to simplify the model, and to reduce the computational time, chemical kinetic mechanism reduction techniques based on sensitivity analysis may be used so as to decrease the number of elementary equations involved. The next step is to incorporate appropriate analytical or numerical techniques to solve the mathematical equations of the model. Before relying on the predictive ability of the model, the capability of the model must be examined for generating accurate solutions for simple limiting cases. Moreover, it is necessary to compare the predicted results based on theoretical solutions of the model with experimental data available. In the case that the comparison is satisfactory, the model may be used for further predictions; otherwise, repetition of the entire procedure may be required. 7.2.5 Inclusion of Chemical Kinetics

Recently, chemical kinetic modeling has emerged as an indispensable tool for interpreting and understanding the observed combustion phenomenon, with substantial investigations having been undertaken in the development of chemical kinetic mechanisms for fuel combustion, resulting in many reports and reviews on modeling in combustion chemistry [25–40]. As noted by Evans [41], the first qualitative treatments of flames, that comprised the nineteenth century thermal theories, were developed independently by Mallard and Le Chatelier in France, Haber in Germany, and Mikelson in Russia. The period that followed was dominated by a debate between those who believed that combustion was controlled by thermal conduction, and those who believed that it was controlled by diffusion. The Russian school of FrankKamenetzki [42], Semenov [43], and Zeldovich [44] pointed out that, for species of equal molecular weight and diameter, the transport contributions were equal and

7.3 Kinetic Mechanisms

opposite and could be cancelled. Later, a multicomponent kinetic theory was developed by Hirschfelder et al. [45]; this was followed Spalding’s nonstationary technique, called the “marching method,” that was well adapted to computer simulation [46]. Spalding and Stephenson [47] subsequently applied their model to the hydrogen bromine flame, and this was followed by a full synthetic model of the methane flame by Smoot et al. [48]. During the late 1960s, P. Dagaut and coworkers studied the kinetic modeling of various hydrocarbons such as propane, ethylene, propene, methane, and ethane. These studies [49–60] laid the basis of chemical kinetics studies to provide an understanding of the general CH2O flame system and oxygen [7, 61].

7.3 Kinetic Mechanisms 7.3.1 Chemical Kinetic Mechanisms Studies

Although combustion kinetics modeling depends solely on the reaction mechanism, these complex mechanisms are in fact progressive developmental sequences based on the concepts of chemists, as verified by the results of experiments that have been conducted. Yet, this will surely change with time as further insights are developed. The hydrogen–oxygen system was the first model used for combustion modeling, as it is the important basic mechanism that may form part of all other hydrocarbon combustion mechanisms. The modeling studies of hydrogen combustion in a wellstirred reactor [62] and in a shock-tube [63] were the first to be presented at the Eleventh International Combustion Symposium, while Dixon-Lewis [64] also reported numerical modeling results for stabilized laminar hydrogen flame in a premixed burner. Detailed reviews of H2–O2 kinetics can be found elsewhere [65–67]. The oxidation of hydrogen that describes the modeling of the H2–O2 system is most commonly given by as many as 40 reactions involving eight species, including H2, O2, H2O, OH, O, H, HO2, and H2O2. This model effectively explains the explosion limits of hydrogen combustion, and also highlights how useful an understanding of the detailed chemical kinetic mechanism of system can be for explaining these experimental observations [2, 68]. The next important mechanism is that of moist carbon monoxide (CO), which also forms the subsystem in the oxidation of hydrocarbons. The combustion of hydrocarbon simply can be explained as a two-step process: (i) a breakdown of the hydrocarbon fuel into CO; and (ii) oxidation of the CO to carbon dioxide (CO2). The term “moist” indicates the presence of water, and its importance in providing the OH atoms as chain initiators; this has a tremendous effect on the oxidation rate. The reaction of CO with OH to produce CO2 and H-atoms is the key elementary reaction in the overall scheme. Among the array of popular hydrocarbons, methane has unique combustion characteristics that include a high ignition temperature and a low flame speed; these may be attributed to methane’s tetrahedral molecule with large carbon–hydrogen

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bond energy. The extensive studies conducted on methane combustion have led to a greater understanding of its chemical kinetic mechanism. Indeed, between 1970 and 1982, it emerged that about 15 elementary reactions involving 12 species were involved, but this was subsequently extended to 150 elementary reactions (half of these were the reverse reactions) with 25 species. Detailed numerical modeling studies for methane and ethane combustion were reported by Higgin and Williams [69], Seery and Bowman [70], Marteney [71], Bowman [72], D’Souza and Karim [73], Sorenson et al. [74], and Cooke and Williams [75]. An optimized methane kinetic mechanism has been created by a collaborative effort of several research groups, specifically those based at the Gas Research Institute (GRI) of Berkley University. A typical mechanism GRI Mech version 2.11 considers 277 elementary reactions involving 49 species exhibited online at their web site. The hydrocarbons that form the group of most common fuels are alkanes, the combustion of which is normally initiated by the attack of O and H atoms on the fuel molecule; this leads to their breakdown with the formation of unsaturated molecules and hydrogen. If oxygen is available, the hydrogen will be oxidized to water, while the unsaturated olefins further oxidize to CO and H2 such that, subsequently, all of the H2 is converted to water. The CO burns out by a typical key reaction of the moist CO mechanism involving the OH radical forming CO2 and H radical, thus releasing all of the heat associated with the overall combustion process (see Chapter 2). Among the initial kinetic models, Halstead et al. reported a detailed model of the nonisothermal oxidation of acetaldehyde. This model was found to provide a realistic simulation of single and multiple cool flames, their limits, amplitudes, and induction periods. It also simulated a two-stage ignition and the negative temperature coefficient for the maximum rate of slow combustion. It was realized that realistic models would require a large number of elementary reactions. Moreover, cool flames were found often to exert thermo-kinetic effects (though not exclusively) of an oscillatory nature, and that a satisfactory account of cool-flame phenomena must necessarily take the reactant consumption into account [76]. The stoichiometric combustion of propane was studied in shock waves, both experimentally and analytically, over a temperature range from 1700 to 2600 K and a pressure range from 1.2 to 1.9 atmospheres. A kinetic mechanism was developed which, when used in a computer program for a flowing, reacting gas behind an incident shock wave, predicted a good agreement with the experimentally measured results. The model also predicted quite well the ignition delay times reported elsewhere. A kinetic mechanism consisting of 59 elementary chemical reactions was developed for propane oxidation [77]. Philippe Dagaut also studied the oxidation of propane in a jet-stirred flow reactor over the temperature range 900–1200 K at pressures extending from 1 to 10 atmospheres for a wide range of fuel–oxygen equivalence ratios (0.15–4.0). These authors also developed a computer code to model the experimental data, by using a detailed chemical kinetic reaction mechanism [78]. The oxidation of ethylene was studied in a jet stirred-flow reactor in the temperature range 900–1200 K, at pressures extending from 1 to 10 atmospheres for a wide range of fuel–oxygen equivalence ratios (0.15–4.0). Here, a direct method was developed to determine the first-order sensitivities of the concentration of each species with

7.3 Kinetic Mechanisms

respect to the rate constants, and then used to develop the kinetic scheme. The experimental ignition delays reported by various other groups were also compared with the delay obtained behind reflected shock waves. In this case, a good agreement was found for methane over the temperature range 1200–2150 K, for ethane over 1200–1700 K, and for ethylene over 1050–1900K [79]. Axelsson et al. described the development of detailed chemical kinetic reaction mechanisms for the oxidation of n-octane and iso-octane, with emphasis on the factors that are specific to many large hydrocarbon fuel molecules. The abstraction of H atoms and a rapid beta-scission of the alkyl radicals were found to be of particular importance. These features, combined with distinctions in the types of intermediate olefin species produced, have been used to explain the significant differences in the rates of oxidation between n-octane and iso-octane [80]. A model for describing chemical reactions in a turbulent shear layer was proposed by Broadwell and Mungal. This showed clearly the effects of equivalence ratio and of the Schmidt, Reynolds and Damkoehler numbers on the overall reaction rate. The model predictions were in agreement with the experimental results in which all of these parameters were varied. These comparisons implied that the Reynolds number influences the effective reaction rate, which becomes mixing-limited for values of the Damkoehler number of about 40 [81]. Curran et al. developed and used a detailed chemical kinetic mechanism to study the oxidation of n-heptane in flow reactors, shock tubes, and rapid compression machines. The initial pressure ranged from 1 to 42 atmosphere, the temperature from 550 to 1700 K, the equivalence ratio from 0.3 to 1.5, and nitrogen–argon dilution from 70–99%. The development and validation of the reaction mechanism at both low and high temperatures was achieved using previously reported experimental results on ignition behind reflected shock waves, and in a rapid compression machine. A sensitivity analysis showed the low-temperature chemistry to be very sensitive to the formation of stable olefin species from hydroperoxy-alkyl radicals, and also to the chain-branching steps involving ketohydroperoxide molecules [82]. When Westbrook and Dryer reviewed the mechanisms used for the combustion of hydrocarbon fuels and some of the practical problems, they concluded that the chemical kinetic reaction mechanisms were strongly hierarchical, in that the mechanisms for the combustion of more complex fuels contained within them sub-mechanisms for simpler fuel molecules. Westbrook and Dryer’s review contained some of the principles and techniques involved in the development and application of these kinetics models for hydrocarbon fuels. The role of chemical kinetics in different combustion systems such as shock tubes, plug-flow reactors, and stirred reactors, laminar flame propagation, flame quenching, and flame inhibition were also discussed [83]. 7.3.2 Mechanism Development and Reduction

A quantitative prediction is possible if a comprehensive, detailed mechanism can be adopted in a CFD code, without imposing an excessively high computational burden,

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although there will always be a trade-off between model complexity for error analysis and reduction costs. One well-known challenge for the simulation of reacting flow systems is that detailed chemical mechanisms contain hundreds to thousands of species and thousands of reactions, leading to high central processing unit (CPU) requirements despite the use of state-of-the-art solvers. For specific conditions of interest (temperature, pressure, composition), smaller mechanisms can predict the chemistry relatively accurately; the strategies for mechanism reduction are discussed in the following sections. 7.3.2.1 Skeletal Mechanism Reduction Various techniques are in place to remove unimportant species and reactions from detailed mechanisms, such as sensitivity analysis, principal component analysis (PCA), detailed reduction, lumping, and Jacobian analysis. Recently, methods such as directed relation graph (DRG), integer linear program (ILP) formulations were developed and shown capable of rapidly and automatically identifying – and hence eliminating – the unimportant species, with a quantifiable and high degree of accuracy. It should be noted that, whilst the skeletal and reduced mechanisms were developed for homogeneous systems, they may also be applied to systems that involve diffusive transport, with a minimal loss of accuracy. The influence of transport in mechanism reduction is a topic of current research interest. 7.3.2.2 Time-Scale Mechanism Reduction Time-scale reduction has its basis on the observation in fast chemical reaction entities, such as quasi-steady state (QSS) species and partial equilibrium reactions, which frequently exist in combustion systems. When the forward and reverse reaction rates of a single or a group of fast reactions are in approximate balance, the reaction or the reaction group is said to be in partial equilibrium. In contrast, when the production and destruction rates of a particular species are in approximate balance, the species is said to be in a quasi-steady state. Thus, both assumptions can be applied to remove not only short time scales but also the number of variables in the governing equations. In general, the QSS assumption is simpler to apply because the resulting reduced mechanisms can be readily compiled when the QSS species have been identified, using standard computer programs such as CARM. Thus, the primary task in the reduction procedure is the identification of QSS species and the efficient calculation of their concentrations. It may also be noted that element conservation and the second law of thermodynamics are both honored when the QSS species are excluded from the conservation equations. The earliest method used to identify the QSS species was based on species lifetime analysis, which has been recently applied to obtain reduced mechanisms for various fuels such as methane, n-heptane, and iso-octane. Lifetime analysis is simple and fast to apply, although the off-diagonal elements of the Jacobian matrix, and hence species coupling, are not considered. Currently, two methods of time-scale reduction enjoy considerable usage, namely intrinsic low-dimensional manifold (ILDM) and computational singular

7.3 Kinetic Mechanisms

perturbation (CSP). ILDM performs an eigenvalue decomposition of the Jacobian matrix, and assumes that the reaction rate component along the direction of the base vectors that is associated with large negative eigenvalues vanishes. This method does not consider the time dependence of the Jacobian matrix, and therefore approximates the system to be locally linear. In order to expedite the computation, re-used results are frequently cached through structured tabulation. In recent years, ILDM has been widely used for the simulation of reacting flows, including many turbulent combustion applications, with further improvements on storage efficiency and alternative approaches. In contrast, CSP does consider the time-dependence of the Jacobian matrix, and features higher-order accuracy by performing a refinement procedure. To identify the QSS species, a characteristic time such as the extinction time for the PSR or the autoignition time is selected to normalize the time scales of species. The species are considered to be in QSS if their normalized time scales are smaller than a specified critical value. 7.3.2.3 Diffusion Coefficient Reduction The computation cost in the assessment of the binary diffusion coefficients depends quadratically on the number of species in the reaction mechanism. Because these coefficients are exponential functions of temperature, a reduction in the number of distinct diffusion species could yield considerable savings for computations requiring the evaluation of these coefficients, particularly for mechanisms with a large number of species or for simulations in which diffusion evaluation may dominate the overall simulation cost, such as those with explicit integration solvers. Currently, extensive research is being undertaken on the generation and compilation of detailed reaction mechanisms for the oxidation of fuels, which range from hydrogen to the large hydrocarbons. These include the acquisition of experimental data using shock tubes, flow reactors, well-stirred reactors, and rapid compression machines, the ab initio quantum chemistry calculation of the potential energy of interactions, and the development of reaction rate theories. Advances have also been made in the computer algorithms of mechanism generation and management. These algorithms use empirical kinetic rules, for example group-additivity-based methods, and computational chemistry tools [84–86]. 7.3.2.4 Method of Computational Singular Perturbation The method of CSP was presented by Lam [87] with special emphasis on the interpretation of CSP data to obtain physical insights on massively complex reaction systems. It also presents a simple example to demonstrate how CSP deals with complex chemical kinetics problems, without the benefits of intuition and experience. CSP, which exploits the power of the computer to perform simplified kinetic modeling, is a systematic mathematical procedure to perform boundary layer-type analysis. It can be used to obtain analytical results for simple problems as well as for massively complex problems using computation, generating not only the numerical solutions of the given problem but also the simplified equations in terms of the given information.

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In short, the success of any mechanism reduction technique is measured in terms of quantitative predictive capacity and the simplicity of the model. Simplified approximate models in the presence of double precision numerical solutions are useful for a physical understanding of a stiff reacting system, and to make general statements relevant to chain branching, chain termination, ignition delay, building up of radical pool, heat release, identification of rate-controlling reactions, and the identification of fast reactions, superfluous reactants, and reactions. Stiffness is an undesirable attribute due to a disparity of reaction time scales, which is also responsible for the simplifications and approximations in a reacting system [88, 89]. As can be deduced from above discussion, in the development of a combustion model, the chemistry and chemical kinetics are the most demanding parts as they introduce a large number of nonlinear differential equations into the model. Additionally, simulating the details of combustion in a complex flow field, where both fluid mechanics and chemical kinetics are treated simultaneously, makes the problem even more difficult. When considering the prevailing analytical methods and computer performance, it is necessary to compromise and to make a decision regarding the extent of details of chemistry and complexity of flow field. In other words, it may be necessary to start with a detailed mechanism with simple flow patterns that can easily be tackled by a commercial chemical kinetic package, such as CHEMKIN. Further precision in simulations to match experimental results may allow the chemical reaction mechanism to be reduced by the above-mentioned strategies. This can be achieved to an extent that the number of species and reactions lies within a range where the computational performance of any CFD code (e.g., Fluent or CFX) may not be exhausted.

7.4 Coupling of Chemical Kinetics and Fluid Dynamics 7.4.1 Detailed Chemical Kinetics with Ideal Flow Fields

Studies related to the modeling of combustion systems can be performed based on chemical kinetics alone, assuming an ideal flow field. The idealized flow fields may be categorized as perfectly stirred reactor, plug-flow reactor, perfectly mixed constant volume fixed-mass reactor, or perfectly mixed constant-pressure fixed-mass reactor. During the process of modeling, it was realized that writing software with everchanging and continuously developing chemical kinetic mechanisms, specifically with different combustion scenarios such as premixed flames, shock tubes, stirred reactors and so on, is quite cumbersome and may be inherently inefficient. It was recognized that increasingly complex chemistry and transport phenomena should be handled independent of the particular type of flame or reactor. Moreover, an increase in efficiency with which new models were developed for different combustion situations, solution techniques may be dealt in isolation with that of physical model

7.4 Coupling of Chemical Kinetics and Fluid Dynamics

itself, thereby benefiting from the rapid advances in computational mathematics and software. These ideas led to the development of the general-purpose, problemindependent chemical kinetic code CHEMKIN, as developed at Sandia Laboratories USA [90]. The CHEMKIN code allows for the use of detailed chemical mechanisms composed of a set of finite rate elementary reactions in the gas phase. The mechanism is input by the user, as are their corresponding rate constants. The code uses a set of subroutines and data libraries to calculate the thermodynamic properties. The computational solution is accomplished by a separate mathematical code such as for example, DASAC, which handles the solution of governing differential equations. The CHEMKINÔ software is based on modular approach in which software utilities provide an interface between the problem-specific information and the problem-independent “application,” which is further composed of submodules that perform different functions such as residual evaluation, matrix manipulation, and so on. These applications include: AURORA, which is used to predict the steady-state or time-averaged properties of a perfectly stirred reactor for chemical systems; PLUG, which is employed for the analysis of plug-flow reactors with gas-phase chemistry; SHOCK, which predicts chemical behavior behind incident and reflected shock waves; and SENKIN, which predicts the time evolution of homogeneous, gas-phase kinetics with sensitivity analysis. Many other applications also run in conjunction with CHEMKIN to produce the final results. Among others, one typical study on the kinetic modeling of an ammonia selective noncatalytic reduction (SNCR) performed by the present authors using SENKIN may be referred to at this point [91]. Although the chemical kinetic modeling results obtained with CHEMKIN were in close agreement with actual experimental results, it lacked the ability to model the effects of actual flow and real geometry. However, these issues were subsequently addressed by newer versions of CFD codes, such as FLUENT and CFX. 7.4.2 Reduced Chemical Kinetic Mechanisms with Actual Flow Fields

FLUENTÔ is a finite volume-based commercial CFD package which has the capabilities to handle Steady State, Transient, Incompressible, Compressible and Laminar, as well as Turbulent, flow regimes. The initial versions of FLUENTÔ incorporate the simple global combustion reactions while analyzing a combustion flow problem for a particular geometry. Although a good simulation of the general flow pattern could be generated by these early versions, an absence of any detailed elementary chemistry – which is an essence of combustion phenomena – could not be handled. In later developments, however, the provision to handle chemistry to certain extent was incorporated into FLUENT, and its ability has since been enhanced to cater for a mechanism with not more than 50 species. Various combustion models available in FLUENTcan be employed by considering typical scenarios such as nonpremixed combustion, premixed combustion, partially premixed combustion, ignition, pollutant formation, species transport, or finite rate

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chemistry and composition. FLUENT has a provision of modeling not only for bulk volumetric reactions but also handles multiphase modeling and simulates reactions on walls or particle surfaces and in porous regions; an example is the combustion of coal. For incorporating turbulence–chemistry interactions, FLUENT provides four choices to facilitate its use. First, laminar finite-rate may compute only the Arrhenius rate and neglects turbulence–chemistry interaction. Second, FLUENT in finite-rate/ eddy-dissipation (for turbulent flows), may compute both the Arrhenius rate and the mixing rate, and use the smaller of the two. Third, eddy dissipation (for turbulent flows) computes only the mixing rate, and fourth is the eddy dissipation concept (EDC), which models turbulence–chemistry interactions with detailed chemical mechanisms. Using the EDC model is computationally expensive, although the computational time of the chemistry calculations can be reduced by increasing the number of flow iterations per chemistry update. FLUENT also provides the option of introducing different types of injection into the flow field. The aim of newer versions of FLUENT in gas-phase combustion modeling is to provide affordable, detailed, finite-rate chemistry. With these new models, kinetically controlled processes such as pollutant formation (NOx, CO, etc.) and flame ignition/ extinction can be simulated with high reliability. The difficulty in including detailed kinetics is the extreme nonlinearity of the chemical mechanisms. Notably, very long computational times are required to integrate the equation set, and special care is required to properly couple the chemistry with the turbulent flow. For these two reasons, most commercially available chemistry codes are limited to physical dimensions of zero or one. In order to overcome the massive computational demands of detailed chemistry simulation in 2-D and 3-D domains, FLUENT incorporates ISAT (In-Situ Adaptive Tabulation), which can accelerate chemistry calculations up to a 1000-fold. For a chemical mechanism with N species, ISAT builds N-dimensional chemistry tables during the simulation; the expensive kinetic integrations are then mitigated by retrieving the appropriate values from the table. ISAT can be used with two turbulence–chemistry interaction models in FLUENT, namely the EDC model and the PDF Transport model [92]. A pilot-scale combustion research flow reactor facility has been designed and validated at the Pakistan Institute of Engineering and Applied Sciences, after optimizing operational parameters using chemical kinetic code CHEMKIN and SENKIN (Figure 7.1). In this case, a research group is not only working on chemical kinetic modeling using CHEMKIN and FLUENT on various aspects of combustion, but also on the validation of predicted simulation with experimental results, as obtained at the said facility. The details of these investigations are available in a variety of reports made in this context [93–95]. Figure 7.2 shows a 3-D surface plot of NO concentration, temperature, and residence time for a typical SNCR reaction mechanism presented by Kilpinen for a molar ratio (R) of 3.0. In this case, a surface plot is employed as it can accommodate a greater amount of data in a single plot, which otherwise may require several 2-D plots. In addition, it can demonstrate the interdependence of NO concentration on temperature and residence time, concomitantly. A dip showing NO reduction with

7.4 Coupling of Chemical Kinetics and Fluid Dynamics

Figure 7.1 Schematic representation of the pilot-scale combustion research flow reactor facility at the Pakistan Institute of Engineering and Applied Sciences [95].

temperature is visible in the NO concentration versus temperature plane. The third plane also shows the effect of residence time, and indicates a fall in NO reduction beyond about 300 ms; this may be shown more clearly by taking a projection of the surface plot on the residence time and temperature plan. The three projection lines indicate residual NO concentrations of 50, 100, and 150 ppm, corresponding to 90%, 80%, and 70% reduction, with the innermost corresponding to a higher reduction efficiency. The solid line shown parallel to the temperature axis indicates the fact that 80% of the NO reduction efficiency may be achieved if the flue gas is given 300 ms to

Figure 7.2 Three-dimensional surface plot of NO concentration, temperature, and residence time for Kilpinen mechanism at molar ratio (R) ¼ 1.0.

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100 R = 0.25 R = 0.50 R = 1.0 R = 1.5 R = 2.0

NOx REDUCTION EFFICIENCY [%]

90 80 70 60 50 40 30 20 10 0 700

800

900

1000

1100

1200

1300

TEMPERATURE [ºC]

Figure 7.3 Effect of temperature and molar ratio (R) on NOx reduction efficiency for aqueous urea.

react with the urea, while it is passing through a section within a temperature range of 970–1100
C, with peak at about 1060
C. Hence, 300 ms may be considered as an optimum residence time corresponding to an 80% NO reduction. This experimental set-up was designed on the basis of modeling observations, and the experimental results were subsequently found to agree with what was observed computationally. The experimental results are shown in Figure 7.3, where the peak efficiency is at almost the same temperature, but at an impaired efficiency. This may be attributed to the fact that, while the code assumes ideal conditions, the experimental set-up has a temperature gradient and practical mixing limitations. With the advent of high-performance computing facilities and a better understanding of the reaction mechanisms, CFD is becoming a significant technology for reacting flows and, as a result, many commercial CFD codes are emerging as tools for coupling chemistry and hydrodynamics. The development of such new processes, and the optimization of existing procedures at a fraction of the cost and time of traditional experimental and pilot-plant set-ups, is rapidly approaching.

7.5 Outlook and Summary

Although the scientific knowledge associated with combustion kinetic modeling is too vast to contain within a single chapter, the aim here has been to outline the emergence and development of combustion modeling. For this, discussions regarding the various components of a model, including conservation and transport

References

equations, initial and boundary conditions, thermodynamic and transport properties, kinetic data, and the material and structural properties of reactants and reactor and other empirical inputs, have been outlined. A general modeling procedure was briefly described, elaborating on the importance of chemical kinetics in combustion modeling. The results of such modeling depend on a variety of important mechanisms, starting from the simplest (i.e., hydrogen combustion), followed by the kinetic mechanism of moist CO and popular hydrocarbons (including methane, acetaldehyde, propane, ethylene, n-octane and iso-octane), and these have been briefly discussed. Notably, the references included may if required, be further explored for detailed sets of elementary equations, along with Arrhenius coefficients. Interest in the incorporation of chemistry in flow has created an impetus in the development of reduced and improved reaction mechanisms, such that different techniques for mechanism reductions, including both skeletal mechanism and time scale mechanism reductions (the latter has its basis in quasi-steady state and partial equilibrium) have been elaborated. Diffusion coefficient reductions and methods of singular perturbation have also been described. For those “beginners” in the field of computational kinetic modeling, a brief account of the ideal reactors, together with details of software such as CHEMKIN and FLUENT have also been provided. It is clear that combustion kinetic modeling should be regarded as an inexpensive aid to decision-making when designing – and predicting the behavior and outcome of – a specific combustion scenario. Indeed, if properly validated, these models may represent a highly economic alternative to expensive and time-consuming experimentation.

References 1 Emmons, H.W. (1971) Fluid mechanics

2 3

4

5

6 7

and combustion. 13th Symposium (International) on Combustion, Pittsburg, The Combustion Institute. Glassman, I. (1987) Combustion, 2nd edn, Academic Press Inc. Williams, F.A. (1992) The role of theory in combustion science. 24th Symposium (International) on Combustion, Pittsburgh, The Combustion Institute. Borghi, R. and Destriau, M. (1998) Combustion and Flame: Chemical and Physical Principles, Editions Technip, Paris. Colannino, J. (2006) Modeling of Combustion system: A Practical Approach, CRC Press. Kuo, K.K. (2005) Principles of Combustion, 2nd edn, John Wiley & Sons Inc. Habik, S.E. (2002) Fundamentals and Technology of Combustion, Elsevier.

8 Lockwood, F.C., Abbas, T., Kandamby,

9

10

11

12

N.H., and Sakthitharan, V. (2000) CFD experience in industrial combustors. Prog. Comp. Fluid Dyn., Int. J., 1 (1–3), 1–13. Spalding, D.B. and Patankar, S.V. (1967) Heat and Mass Transfer in Boundary Layers, Morgan-Grampian, London. Launder, B.E. and Spalding, D.B. (1972) Mathematical Models of Turbulence, Academic Press. Gosman, A.D., Pun, W.M., Runchal, A.K., Spalding, D.B., and Wolfshtein, M. (1969) Heat and Mass Transfer in Recirculating Flows, Academic Press. Caretto, L.S., Gosman, A.D., Patankar, S.V., and Spalding, D.B. (1972) Two numerical procedures for threedimensional recirculating flows. Proceedings of the International Conference on Numerical Methods in Fluid Dynamics, Paris.

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13 Patankar, S.V. (1980) Numerical Heat 14

15

16

17

18 19

20

21

22

23

24

25

26

Transfer and Fluid Flow, Hemisphere P.C. Theodoropoulos, T. (1990) Prediction of three-dimensional engine flow on unstructured meshes, PhD Thesis, University of London. Thompson, J.F., Warsi, Z.U.A., and Mastin, C.W. (1985) Numerical Grid Generation, Elsevier Science Publishing Co. Hottel, H.C. (1961) The Melchett Lecture for 1960; Radiative transfer in combustion chambers. J. Inst. Fuel, 34, 220. Hottel, H.C. (1954) Chapter 4, Heat Transmission, 3rd edn (ed. W.H. McAdams), McGraw-Hill, New York. Hottel, H.C. and Sarofim, A.F. (1965) Int. J. Heat Mass Transfer, 8, 1153–1169. Hottel, H.C. and Sarofim, A.F. (1967) Radiation Transfer, McGraw- Hill, New York. Linstedt, R.P., Lockwood, F.C., and Selim, M.A. (1995) A detailed kinetic study of ammonia oxidation. Combust. Sci. Technol., 108 (4–6), 231. Peters, N. (1986) Laminar flamelet concepts in turbulent combustion. 21st Symposium on Combustion, The Combustion Institute, Pittsburg, The Combustion Institute. Pope, S.B. (1976) The probability approach to the modelling of turbulent reacting flows. Combust. Flame, 27, 299. Emami, M. and Lockwood, F.C. (1999) Calculation of finite-rate chemistry turbulent diffusion flames based on particle pdf approach. Combust. Sci. Technol., 152, 39–56. Weller, H.G., Tabor, G., and Gosman, A.D. (1995) Application of flame-wrinkling LES combustion model to a turbulent mixing layer. 27th Symposium on Combustion, Pittsburg, The Combustion Institute. Dixon-Lewis, G. (1991) Structure of laminar flames, 23rd Symposium on Combustion (International), Pittsburgh, The Combustion Institute, p. 305. Kee, R.J., Miller, J.A., Evans, G.H., and Dixon-Lewis, G. (1988) A computational model of the structure and extinction of strained, opposed flow, premixed methane air flame, 22nd Symposium (International) on Combustion,

27

28

29

30

31

32

33

34

35

36

37 38

Pittsburgh, The Combustion Institute, p. 1479. Peters, N. and Rogg, B. (eds) (1993) Reduced Kinetic Mechanisms for Applications in Combustion Systems, Springer-Verlag, Berlin, Heidelberg, New York. Seshardri, K. (1996) Multistep asymptotic analyses of flame structures, 26th Symposium (International) on Combustion, Pittsburgh, The Combustion Institute. Miller, J.A. (1996) Theory and modeling in combustion chemistry, 26th Symposium (International) on Combustion, Pittsburgh, The Combustion Institute. Patnaik, G., Kailasanath, K., and Sinkovits, R.S. (1996) A new time-dependent, threedimensional, flame model for laminar flames, 26th Symposium (International) on Combustion, Pittsburgh, The Combustion Institute. Fristrom, R.M. (1995) Flame Structure and Processes, Oxford University Press, Inc., Oxford, New York, Toronto. Leung, K.M. and Lindstedt, R.P. (1995) Detailed kinetic modeling of C1-C3 alkane diffusion flames. Combust. Flame, 102, 129. Zuo, B. and Bulck, V.D. (1998) A quasiglobal mechanism for oxidation of fuel oil and the laminar flame data library. Combust. Flame, 113, 615. Yang, B. and Pope, S.B. (1998) Treating chemistry in combustion with detailed mechanisms – in situ adaptive tabulation in principal directions – premixed combustion. Combust. Flame, 112, 85. Bozzelli, J.W. and Dean, A.M. (1995) O þ NNH: A possible new route for NOx formation in flames. Int. J. Chem. Kinet., 27, 1097. Dupont, V. and Williams, A. (1998) NOx mechanisms in rich methane-air flames. Combust. Flame, 114, 103. GRI Mech. Version 2.1, 19/6/95, www. address, http://www.grLorgf- þ 995. Westbrook, C.K. and Dryer, F.L. (1981) Inhibition effect of halogens on the oxidation of hydrocarbon/air flames, 18th Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, p. 749.

References 39 Oran, E. and Boris, J. (1981) Detailed

40

41

42

43 44

45

46

47

48

49

50

51

52

53

modelling of combustion systems. Prog. Energy Combust. Sci., 7, 1–172. Warnatz, J. (1981) The structure of laminar alkane, alkene and acetylene flames, 18th Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, p. 369. Evans, M. (1952) Current theoretical concepts of steady state flame propagation. Chem. Rev., 51, 363. Frank-Kamenetzki, D.A. (1955) Diffusion and Heat Exchange in Chemical Kinetics, Princeton University Press, Princeton. Semenov, N. (1935) Chemical Kinetics and Chain Reactions, Oxford, UK. Zeldovich, Ya.B. (1949) Combustion theory. National Advisory Committee for 76 Aeronautics, Technical Report F.TS1226-LA, Air Material Command, Washington D.C. (Trans. From Russian). Hirschfelder, J.O., Curtiss, C.F., and Bird, R.B. (1954) Molecular Theory of Gases and Liquids, John Wiley & Sons, New York. Spalding, D.B. (1956) Theory of flame phenomena with a chain reaction. Philos. Trans. R. Soc. London, 249 (957), 1–25. Spalding, D.B. and Stephenson, P.L. (1971) Laminar flame propagation in hydrogen þ bromine mixtures. Proc. Roy. Soc. London, 324, 315–337. Smoot, L.D., Hecker, W., and Williams, G. (1976) Prediction of propagating methane air flames. Combust. Flame, 26, 323. Dagaut, P. et al. (1986) A jet stirred reactor for kinetic studies of homogeneous gasphase reactions at ten atmospheres. J. Phys. E.: Sci. Instrum., 19, 207–209. Dagaut, P., Cathonnet, M., Boettner, J.C., and Gaillard, F. (1986) Kinetic modeling of propane oxidation. Combust. Sci. Technol., 56, 23–63. Dagaut, P., Cathonnet, M., Boettner, J.C., and Gaillard, F. (1988) Kinetic modeling of ethylene oxidation. Combust. Flame, 71, 295–312. Dagaut, P., Cathonnet, M., and Boettner, J.C. (1988) Experimental study and kinetic modeling of propene oxidation in a jet stirred reactor. J. Phys. Chem., 92, 661–671. Dagaut, P., Cathonnet, M., and Boettner, J.C. (1991) Methane oxidation:

54

55

56

57

58

59

60

61 62

63

64

experimental and kinetic modeling study. Combust. Sci. Technol., 77, 127–148. Dagaut, P., Cathonnet, M., and Boettner, J.C. (1991) Kinetics of ethane oxidation. Int. J. Chem. Kinet., 23, 437–455. Dagaut, P., Reuillon, M., and Cathonnet, M. (1994) High-pressure oxidation of liquid fuels from low to high temperature. 1. n-heptane and iso-octane. Combust. Sci. Technol., 95, 233–260. Dagaut, P., Reuillon, M., and Cathonnet, M. (1994) High-pressure oxidation of liquid fuels from low to high temperature. 2. Mixtures of n-heptane and iso-octane. Combust. Sci. Technol., 103, 315–336. Dagaut, P., Reuillon, M., and Cathonnet, M. (1994) High-pressure oxidation of liquid fuels from low to high temperature. 3. n-Decane. Combust. Sci. Technol., 103, 349–359. Ranzi, E., Gaffuri, P., Faravelli, T., and Dagaut, P. (1995) A wide modeling study of n-heptane oxidation. Combust. Flame, 103, 91–106. Dagaut, P., Reuillon, M., Voisin, D., Cathonnet, M., McGuinness, M., and Simmie, J.M. (1995) Acetaldehyde oxidation in a JSR and ignition in shock waves: Experimental and comprehensive kinetic modeling. Combust. Sci. Technol., 107, 301–316. Curran, H.J. et al. (1998) A wide range modeling study of dimethyl ether oxidation. Int. J. Chem. Kinetic, 30, 229–241. Fawzy, E.-M. (2002) Fundamentals of Technology of Combustion, Elsevier. Jenkins, D.R., Yumlu, V.S., and Spalding, D.B. (1967) Combustion of hydrogen and oxygen in a steady-flow adiabatic stirred reactor. Eleventh Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, pp. 779–790. Hamilton, C.W. and Schott, G.L. (1967) Post-induction kinetics in shock-initiated H2-O2 reactions. Eleventh Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, pp. 635–643. Dixon-Lewis, G. (1967) Flame structure and flame reaction kinetics. Proc. R. Soc. London, A298, 495–513.

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j 7 Combustion Kinetic Modeling

172

65 Gardiner, W.C. Jr and Olson, D.B. (1980)

76 Halstead, M.P., Prothero, A., and Quinn,

Chemical kinetics of high temperature combustion. Annu. Rev. Phys. Chem., 31, 377–399. Yetter, Y.A., Dryer, F.L., and Rabitz, H. (1991) A comprehensive reaction mechanism for carbon monoxide/ hydrogen/oxygen kinetics. Combust. Sci. Technol., 79, 97–128. Westbrook, C.K. and Dryer, F.L. (1984) Chemical kinetic modeling of hydrocarbon combustion. Prog. Energy Combust., 10, 1–57. Turns, S.R. (1996) An Introduction to Combustion Concepts and Applications, 2nd edn, McGraw-Hill, Inc. Higgin, R.N.R. and Williams, A. (1969) A shock-tube investigation of the ignition of lean methane and n-butane mixtures with oxygen. Twelfth Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, pp. 579–590. Seery, D.J. and Bowman, C.T. (1970) An experimental and analytical study of methane oxidation behind shock waves. Combust. Flame, 14, 37–47. Marteney, P.J. (1970) Analytical study of the kinetics of formation of nitrogen oxide in hydrocarbon-air combustion. Combust. Sci. Technol., 1, 461–469. Bowman, C.T. (1970) An experimental and analytical investigation of the hightemperature oxidation mechanisms of hydrocarbon fuels. Combust. Sci. Technol., 2, 161–172. D’Souza, M.V. and Karim, G.A. (1971) An analytical study of methane oxidation in a steady-flow reactor. Combust. Sci. Technol., 3, 83–89. Sorenson, S.C., Myers, P.S., and Uyeara, O.A. (1971) Ethane kinetics in sparkignition engine-exhaust gases. Thirteenth Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, pp. 451–459. Cooke, D.F. and Williams, A. (1971) Shock-tube studies of the ignition and combustion of ethane and slightly rich methane mixtures with oxygen. Thirteenth Symposium (International) on Combustion, Pittsburgh, The Combustion Institute, pp. 757–766.

C.P. (2009) A mathematical model of the cool-flame oxidation of acetaldehyde. Proc. Roy. Soc. Lond. A Math. Phys. Sci., 322 (1550), 377–403. Mclain, A.G. and Jachimowski, C.J. (1977) Chemical kinetic modeling of propane oxidation behind shock waves. NASA Center: Langley Research Center, NASATN-D-8501. Dagaut, P. et al. (1987) Kinetic modeling of propane oxidation. Combust. Sci. Technol., 56 (1), 23–63. Dagaut, P. et al. (1988) Kinetic modeling of ethylene oxidation. Combust. Flame, 71 (3), 295–312. Axelsson, E.I., Brezinsky, K., Dryer, F.L., Pitz, W.J., and Westbrook, C.K. (1986) Chemical kinetic modeling of the oxidation of large alkane fuels: n-octane and iso-octane. 21st International Symposium on Combustion, Munich, Germany, pp. 783–793. Broadwell, J.E. and Mungal, M.G. (1988) Molecular mixing and chemical reactions in turbulent shear layers. 22nd International Symposium on Combustion, Seattle, WA, pp. 579–587. Curran, H.J., Gaffuri, P., Pitz, W.J., and Westbrook, C.K. (1998) A comprehensive modeling study of n-heptane oxidation. Combust. Flame, 114 (1–2), 149–177. Westbrook, C.K. and Dryer, F.L. (1980) Chemical kinetics and modeling of combustion processes, Report No. UCRL-84653; CONF-800809-4. Law, C.K. (2007) Combustion at a crossroads: Status and prospects. Proc. Combust. Inst., 31 (1), 1–29. Mitsos, A., Oxberry, G.M., Barton, P.I., and Green, W.H. (2008) Optimal automatic reaction and species elimination in kinetic mechanisms. Combust. Flame, 155 (1–2), 118–132. Wang, H. and Frenklach, M. (1991) Detailed reduction of reaction mechanisms for flame modeling. Combust. Flame, 87 (3–4), 365–370. Lam, S.H. and Goussis, D.A. (1988) Understanding complex chemical kinetics with computational singular perturbation. 22nd International Symposium on Combustion, Seattle, WA, pp. 931–941.

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67

68

69

70

71

72

73

74

75

77

78

79

80

81

82

83

84

85

86

87

References 88 Lam, S.H. (1993) Using CSP to

89

90

91

92

understand complex chemical kinetics. Combust. Sci. Technol., 89, 375–404. Lam, S.H. and Goussis, D.A. (1994) The CSP method for simplifying kinetics. Int. J. Chem. Kinet., 26, 461–486. Kee, R.J., Rupley, F.M., and Miller, J.A. (1994) CHEMKIN-II: A FORTRAN chemical kinetic package for analysis of gas phase chemical kinetics. Sandia Report, SAND89-8009.UC-706, Sandia National Laboratories, Livermore, California. Tayyeb Javed, M., Ahmed, Z., Ibrahim, M.A., and Irfan, N. (2008) A comparative kinetic study of SNCR process using ammonia. Braz. J. Chem. Eng., 25 (1), 109–117. FLUENT (2008) Optimizing Plant-Scale LDPE Reactors, 3255 Kifer Road Santa

Clara, CA 95051 USA. Available at: http:// www.fluent.com/about/news/ newsletters/03v12i2/pdfs/nl387.pdf. 93 Ibrahim, M.A. (2006) Modeling SNCR process for nitrogen oxides removal from flue gases using CHEMKIN/SENKIN and FLUENT codes, MS Thesis, Pakistan Institute of Engineering and Applied Sciences. 94 Mahmood, A. (2007) Assessment and identification of some novel NOx-reducing reagents for SNCR process, MS Thesis, Pakistan Institute of Engineering and Applied Sciences. 95 Tayyeb Javed, M. (2008) Modelling and analysis of SNCR process for NOx control in 150 kW combustion facilities, PhD Thesis, Pakistan Institute of Engineering and Applied Sciences.

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8 Modeling of Turbulent Combustion Bart Merci, Epaminondas Mastorakos, and Arnaud Mura

8.1 Introduction

In many technical applications, the flow is turbulent. This may be deliberate, for example by enhanced mixing, or inevitable, due to velocities and geometric aspects. One example is the notion that flames become shorter when the mixing of fuel and oxidizer is enhanced, as this allows a more complete combustion and a higher heat release rate in a more compact volume. Turbulence almost always very heavily affects the combustion processes taking place whereas, in contrast, combustion will affect turbulence in the flow. In other words, turbulence–chemistry interaction is often very important. Major differences have been identified between turbulent premixed combustion and turbulent nonpremixed combustion. In a combustible mixture of gaseous fuel and oxidizer (premixed combustion), an existing flame has its own dynamics, in that it attempts to move towards the unburned mixture. In nonpremixed combustion situations, on the other hand, the flame is much more passive, and it can only survive by virtue of the mixing process of fuel and oxidizer. These physically fundamental differences are reflected in different modeling strategies. Since, in all configurations turbulence plays an important role, some important physical aspects of turbulence and the different modeling options will first be discussed. Attention will then be focused on the extreme configurations of turbulent premixed and nonpremixed combustion. Finally, partially premixed combustion will be briefly discussed. At this point, the discussions will be restricted to single-phase gaseous combustion, and radiation will not be addressed.

8.2 Turbulence: Physics and Modeling

As excellent reviews on turbulence and turbulent flows are available [1, 2], only certain aspects of the subject will be described at this point.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Turbulence is not related to a fluid, but rather to a flow. Turbulence appears when the Reynolds number (Re), defined as: Re ¼

rul m

ð8:1Þ

is large. In Equation 8.1, r denotes the fluid density, u is a characteristic velocity, l a characteristic length, and m the fluid’s dynamic viscosity. There is no straightforward definition of turbulence, but typically it is described by means of a number of characteristics: . . . .

Chaos: a turbulent flow is in a certain sense “at random”; yet, there are structures in the chaos, leading to the existence of turbulent time, length and velocity scales. Dissipation: unless energy is supplied, a turbulent flow very rapidly loses its (kinetic) energy. Diffusion: a turbulent flow is strongly diffusive (enhancement of mixing). Vorticity: the existence of vortices and vortex stretching is essential for turbulence.

At this point, the existence of turbulent time, length and velocity scales will be briefly describe, after which possible strategies for numerical simulations of turbulent flows will be discussed. 8.2.1 Turbulent Scales

Turbulent flows contain structures within the randomness of the flow that can be described by creating a statistical picture of the flow itself. Following the introduction of such a statistical analysis, the different scales in turbulence and the energy cascade will be outlined. Consider a variable w with a certain sample space. By definition, a probability density function (PDF), f, states that f (y)dy is the probability that w takes a value in the interval [y; y þ dy]. The ensemble mean value and fluctuations are:
¼ w

1 ð


yf ðyÞdy; w0 ¼ w
w

ð8:2Þ


1

Although, by definition, w
0 ¼ 0, the variance typically differs from zero: Ð1 2

2 f ðyÞdy ¼ w
2
w
2. w ¼
1 ðy
wÞ It is instructive to focus now on velocity components. At a certain point in space and time, the statistical covariance of instantaneous velocity components ui and uj is: u0i u0j ¼

1 ð

1 ð


1
1

ðvi
ui Þðvj
uj Þfij ðvi ; vj Þdvi dvj ; rij 

u0i u0j 02 1=2 ½u02 i uj 

ð8:3Þ

The correlation coefficient rij indicates to what extent individual coefficients are correlated. This notion can now be extended to correlations in time and in space. For a process, with variations in time, the autocorrelation function is defined as

8.2 Turbulence: Physics and Modeling 0

0

rðsÞ ¼ u ðtÞu0 ðt2þ sÞ . If the integral converges, then the integral time scale of the process u ðtÞ 1 Ð is tI ¼ rðsÞds. The larger this quantity, the longer fluctuations of the variable are 0

correlated in time. Similarly, correlations in space lead to integral length scales. We now discuss the theoretical energy cascade concept in turbulence. Consider a turbulent flow with a sufficiently high Reynolds number, a characteristic velocity u0, and a characteristic length l0. The largest “eddies” – that is, turbulent motions in which there is some coherence – have velocity and length scales in the order of u0 and l0 . The corresponding time scale is t(l0) ¼ u(l0)/l0. The large eddies are unstable in nature and break up into smaller eddies, during which process energy is transferred from the larger to the smaller eddies. This cascade process continues until the Reynolds number (Re) ¼ ru(l)l/m, which is associated with the velocity and length scales of the eddies, becomes so small that viscous dissipation consumes the turbulent kinetic energy and the motion of the eddies becomes stable. Whereas, the dissipation takes place at the smallest scales, the dissipation rate is, at equilibrium, determined by the energy transfer rate through the eddy cascade. This energy is taken from the kinetic energy of the mean flow. Note that there is a range of length scales in turbulent flows (“turbulence spectrum”), and that this range becomes wider as the flow Reynolds number increases. The largest scales remain in the order of the characteristic geometric dimensions of the flow configurations, while the smallest scales become smaller. The picture is thus as follows: .

. .

Energy is taken from the mean flow and primarily becomes “turbulent” kinetic energy of the largest eddies, in the “energy-containing range” of the turbulence spectrum. As the unstable large eddies break up into smaller and smaller eddies, the energy is mainly transferred towards these smaller eddies. The smallest eddies have a length scale, corresponding to a turbulent Reynolds number, based on this length scale and the corresponding velocity scale, in the order of unity. At this level, in the “dissipation range”, the turbulent kinetic energy is dissipated into heat. In Ref. [2], the Kolmogorov hypotheses are summarized:

. .

.

Eddies become increasingly isotropic as they become smaller. For sufficiently large Re, the statistics of the small-scale motions are universal, determined by the kinematic viscosity n (¼ m/r) and the dissipation rate e; the range of scales for which this hypothesis holds, is called the “universal equilibrium range.” For sufficiently large Re, there exists a range of scales for which the statistics are solely determined by e, independent of n; this is the “inertial sub-range.”

From the second hypothesis, unique length, velocity and time scales – the “Kolmogorov” scales – can be constructed: g  ðn3 =eÞ1=4 ;

ug  ðenÞ1=4 ;

tg  ðn=eÞ1=2

ð8:4Þ

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Figure 8.1 Model turbulent energy spectrum.

The largest eddies contain most of the turbulent kinetic energy. When expressed in terms of wave number k ¼ 2p/l, Pope [2] suggests a model spectrum. In the energycontaining range, E(k)  k2, while in the inertial sub-range E(k)  e2/3k
5/3. Figure 8.1 summarizes the above-described relationships in a schematic manner, where P denotes the “production” of turbulent kinetic energy, derived from the mean flow kinetic energy, and T denotes the energy transfer through the inertial sub-range to the dissipation range. The absolute numbers in the figure depend on the actual flow Reynolds number (the turbulence Reynolds number). It should be noted that the Reynolds number must be sufficiently high for the different mentioned ranges to exist, although this is most often not the case. This is particularly true for combustion, as the density will decrease in high-temperature regions, while the viscosity will increase. Whilst Equation 8.1 reveals that this leads to lower Reynolds numbers, it transpires that many models – which implicitly assume the existence of a turbulent energy spectrum as described above – also functions quite well when this assumption is not fulfilled. 8.2.2 Direct Numerical Simulation

In a direct numerical simulations (DNS), turbulence is not modeled. However, as the instantaneous Navier–Stokes equations and continuity equation are solved, the entire range of length and time scales must be resolved. As the ratio of the largest the smallest length scales depends on the flow Reynolds number as l0 =g  Re3=4 , and the calculations are (by definition) three-dimensional (3-D), the number of grid cells required scales as  Re9/4. Moreover, the time step must be sufficiently small, so that the number of numerical operations in DNS scales as Re3. As a consequence, for the foreseeable future, DNS remains an appealing academic tool to study detailed flow features, but not as a tool to perform realistic technical flows.

8.2 Turbulence: Physics and Modeling

In reacting flows, the characteristic flame dimensions (e.g., flame thickness) can still be much smaller than the smallest turbulent scale (as discussed in Section 8.3). This makes direct numerical simulations even more cumbersome than for incompressible flows without reaction. There has also been some dispute regarding the terminology “DNS”; indeed, the use of detailed chemistry mechanisms, in combination with the resolution of the smallest turbulent scales, is prohibitive with presentday computers. The way around this would be to use simplified chemistry models, but this would affect the flame structure such that the simulation would, strictly speaking, no longer be a truly “direct numerical simulation”. 8.2.3 Turbulence Modeling

As discussed above, application of DNS is not feasible for simulations of turbulent reacting flows. Hence, in order to reduce computing times to an acceptable level, turbulence is modeled to a certain extent, despite this always having implied some means of averaging the original turbulent flow field. 8.2.3.1 Turbulence Closure Problem The averaging procedure, which is necessary to avoid the resolution of the turbulent flow field down to the smallest scales, inevitably leads to a “turbulence closure problem,” in that new terms appear in the averaged equations that cannot be closed in terms of directly calculated quantities, and need to be modeled. Consider first the ensemble averaging of the instantaneous momentum equations. In reacting flows, it is common practice to use “Favre”-averaged variables, that is, density-weighted ensemble mean variables: ~ ¼ rQ ; Q 00 ¼ Q
Q ~ Q
r

ð8:5Þ

An elaboration of the averaging procedure, as applied to the instantaneous Navier–Stokes (NS) equations for reacting flows, can be found in numerous textbooks (e.g., Refs [3–10]). The result, when applying the summation convention, is:
  qðr u~i Þ q r u~i u~j q
p q  00 u00 þ r
gi ; i ¼ 1 . . . 3 þ þ ¼ tij

rug ð8:6Þ i j qt qxi qxj qxj with the molecular viscous stress tensor (with dij the Kronecker delta):    qui quj 2 quk
dij tij ¼ m þ 3 qxk qxj qxi

ð8:7Þ

In Equation 8.6, which is called the Reynolds-averaged Navier–Stokes (RANS) equation, the term gi is the i-component of the gravity vector. Clearly, the terms 00 u00 play a very similar role as the components of the molecular viscous stress
r ug i j tensor; these are called the “turbulent stresses” or “Reynolds stresses,” and are exact but unclosed. They are modeled when RANS turbulence models are applied (see Section 8.2.3.2).

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Another way of averaging the instantaneous NS-equations consists of spatial filtering. The range of turbulent scales was illustrated in Figure 8.1 where, if a lowpass filter operation were to be applied, the smallest scales are – at least to a certain extent – removed and the solution of the set of filtered equations will consist of a less broad range of scales. Formally, the equations appear very similar to Equation 8.6, although there are some fundamental differences, depending on the filter choice. (More specialized information on this topic is available elsewhere [2, 11].) Here, it is suffice to say that, after the filtering operation, “residual” stresses appear in the equations that need to be modeled. These stresses are often referred to as “sub-grid stresses”; this stems from the fact that, typically, the computational mesh is implicitly used as the spatial filter, when applied to the equations. 8.2.3.2 RANS Turbulence Models As noted in the previous section, exact – but unclosed – terms appear when the instantaneous NS-equations are ensemble-averaged. The closing of these turbulent stresses in terms of known (i.e., averaged) quantities is referred to as “RANS turbulence modeling”. In this class of models, the entire range of turbulent scales (see Figure 8.1) is modeled, but only the mean flow is resolved. The major advantage here is the relatively very low computing time; in particular, when the mean flow is statistically stationary and there is symmetry (e.g., axial symmetry), then the computing time is several orders of magnitude lower than when the large-eddy simulations (LES) approach is adopted. The latter invariably requires 3-D and timeaccurate simulations (see Section 8.2.3.3). The major drawback of RANS is that none of the turbulent motions is resolved. Rather, it is often quite important to capture the large-scale turbulent motions in reacting flows, as they typically affect the flame dynamics and/or the mixing processes. Although at present there is a clear trend in academic research in the direction of LES, RANS turbulence models will remain popular in industry and for parameter studies. Consequently, the most important classes of RANS models will first be briefly discussed. The most widely used RANS turbulence model is the “standard” k-e model [12], which belongs to the class of “two-equation” turbulence models. In this case, in addition to the RANS equations, two transport equations are solved for the sake of turbulence modeling. In case of the k-e model, one equation is solved for the 00 u00 , and one for the dissipation rate e. From these turbulent kinetic energy k ¼ 12 ug i i quantities, the integral turbulent time scale is computed as tt ¼ ke, while the integral 3=2 length scale is proportional to k e . The turbulent (or “eddy”) viscosity is then computed as mt ¼ rcm ktt . The turbulent stresses in Equation 8.6 are then modeled much like the molecular viscous stresses (Equation 8.7). In other words, the eddy viscosity is simply added to the molecular viscosity in the RANS equations. Whilst this is not justified [2], even for simple basic flows, it is often not a serious drawback as typically the resulting force of the shear stresses is well predicted. This is usually more important than the resulting forces of pressure or turbulent normal stresses. Issues have also been raised concerning the modeling of transport equations for e, mainly because this equation is based on the transport equation for k, divided by tt, and because modeling on the basis of individual terms of the exact equation is not

8.2 Turbulence: Physics and Modeling

feasible [1]. Application of the standard k-e model reveals that this transport equation is the major “weak spot” (e.g., the effects of streamline curvature are not well captured, and the model suffers from the “plane jet–round jet anomaly”). In general, the model performs reasonably well for flows that are dominated by shear stresses, in which there are no important large-scale unsteadiness effects, when there is no important streamline curvature or rotation, and when there is no important stagnation region, and no swirl. Within the framework of k-e type modeling, it has been illustrated [13] that reasonable results can be obtained for a wide range of flows, when the model parameter cm depends locally on the mean flow and the e transport equation is adjusted in a general manner. This model was applied to reacting flows in Refs [14, 15]. In general, however, regardless of the effort put into modeling the turbulent stresses or turbulence quantities transport equations, two-equation models are incapable of capturing large-scale unsteadiness effects, such as vortex breakdown in swirling flows. This statement also holds true for k-v type models, including the SST model or other more recent variants [16, 17]. The physical reason for this is that although, as mentioned above, all turbulence is modeled, only one (integral) length, time and velocity scale is computed to that purpose. An attempt to overcome some deficiencies of two-equation RANS models was developed during the 1970s. In the Reynolds stress model (RSM) – which was also called the second-moment closure (SMC) model – the transport equations were solved for the individual turbulent stress tensor components. The basic model [18] was known as LRR-IP (Launder–Reece–Rodi–Isotropization of Production). Whilst, for reacting flows, other models have also been developed (e.g., Ref. [19]), such details will not be referred to here as the technique has never really been successful in turbulent combustion simulations. The major reason for this was that, despite the potential to model more complex flow phenomena more accurately than with two-equation RANS models, the added computational cost was substantial (in three dimensions, seven additional transport equations must be solved instead of two), and the merit is relatively modest. Indeed, a transport equation for e is still required to construct the integral length scale, which remains very much a weak spot. Moreover, large-scale unsteadiness effects are still not well captured. The tendency among the combustion community is to apply the computational effort to LES simulations, rather than to SMC modeling. RSM turbulence models primarily remain valuable in transported PDF simulations, mainly for reasons of consistency (not discussed here). Finally, it should be recalled that RANS turbulence models have been developed for nonreacting incompressible flows. The application of Favre averaging (Equation 8.5) is merely a mathematical operation, that does not express any physics. Hence, although the application of the developed turbulence models is not as straightforward as simply replacing Reynolds averages with Favre averages, this does seem to work quite well. The effect of variable density is incorporated in the pressure–rate-of-strain terms in RSM models, and terms are also added in the transport equation for the turbulent kinetic energy and turbulent dissipation rate. However, especially in the two-equation turbulence models, these additional terms can often have only a minor effect, even when buoyancy effects are important, unless special care is taken [20].

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The difficulties of incorporating buoyancy effects basically stem from the fact that all turbulence is modeled in the RANS approach. 8.2.3.3 Large-Eddy Simulation With the ongoing increase in computing power, the LES technique becomes increasingly feasible for simulations of turbulent flows with combustion. The major advantage here is that large-scale unsteadiness, including for example any turbulent mixing, is accurately resolved to a certain extent. As this is very important in many turbulent combustion configurations, there is today a huge and increasing interest in LES among the turbulent combustion research community. As noted above, the first operation in LES simulations of turbulent flows, is the application of a spatial filter to the original instantaneous NS-equations. This is a lowpass filter in wavenumber space; that is, the smallest turbulent scales are no longer resolved and their effect on the turbulent flow is modeled. Conceptually, the filter must be in the inertial sub-range of Figure 8.1. Consequently, orders of magnitude are gained in computational time, as compared to DNS. Modeling of the smallest eddies is easier than modeling all turbulence, as their behavior is more universal than is the case for the largest eddies, which are typically anisotropic and depend relatively heavily on the specific configuration (boundary conditions). Also, only a relatively small amount of the turbulent kinetic energy is modeled in LES, with the largest part of the motions being resolved. Pope [2] has argued that in a high-quality LES, at least 80% of the turbulent kinetic energy is resolved. This criterion remains valuable in cases where there is no clear inertial sub-range in the energy spectrum, as usually occurs in technically relevant turbulent reacting flows. The Smagorinsky model [21] is the most widely used model for the “residual” or “sub-grid” stresses. Here, all the residual stresses are typically grouped and modeled in a similar way as the molecular viscous stresses (Equation 8.7), replacing the instantaneous velocities by the resolved (filtered) velocities and defining the viscosity as:   uj 1 q
ui q
1 q
uk 2


1=2

dij nS ¼ ðCS DÞ 2Sij Sij ; Sij ¼ þ ð8:8Þ 2 qxj 3 qxk qxi

This model can be refined in several ways. One popular method is the “dynamic” Smagorinsky model [22], where the model parameter CS is determined automatically, locally in the computational domain. However, special precautions are typically required to prevent CS becoming negative, as this may lead to numerical stabilization problems. In general, it is important to appreciate that in LES, numerical issues and modeling are strongly intertwined, although Pope [2, 23] has provided some interesting insight in this matter. Modeling errors and numerical errors can compensate each other to a certain extent, and this may also affect the dynamic procedure, as mentioned above. With respect to numerical accuracy, within the combustion community, it is common practice to resort to second-order accurate schemes both in space and in time. Whilst a higher-order accuracy is possible, the advantages are not always clear, due to the interaction with modeling and possible difficulties with boundary conditions.

8.3 Turbulent Premixed Combustion

To conclude, the following issues on LES should be noted: . . .

.

.

LES results depend very heavily on the (inlet) boundary conditions; one method of modeling turbulence at the inlet in a reasonable manner is described in Ref. [24]. When the computational mesh is used as spatial filter, no grid-independent results can be obtained with LES; this is a major difference from RANS modeling. When the LES technique is applied to too-coarse meshes, so that an insufficient amount of turbulent kinetic energy is resolved, the quality of the results can deteriorate dramatically. However, this can be remedied to a certain extent by an improved sub-grid scale modeling. In Ref. [25], a (hybrid RANS/LES) model is described, based on renormalization group theory, with one additional transport equation; one very appealing model feature here [25] is that the dissipation rate is the basic quantity, and not the heavily mesh-dependent sub-grid scale turbulent kinetic energy; Large-scale buoyancy effects in the flow are automatically accounted for, because the large-scale motions are resolved; this is an important advantage over RANS modeling. When the simulations need to be 3-D (and time-accurate), it is often better to spend the computational time to perform LES rather than (time-accurate) RSM simulations; the required time to obtain a sufficient amount of statistics in LES is often less than the cost of solving seven additional transport equations (as is necessary for RSM simulations).

Clearly, whilst there remains much to be done, there is today a generally increased interest in the quality assessment of LES results [26]. Without doubt, this will lead to LES becoming the more primary application in turbulent combustion simulations, and provide further insight into the physics of important phenomena.

8.3 Turbulent Premixed Combustion

Under many circumstances of applied interest, combustion proceeds in mixtures in which the reactants have been fully mixed prior to chemical reaction. In practice, premixed combustion is almost always turbulent, with well-known examples in the field of propulsion including spark ignition engines and jet engines. In the area of safety, another important example is a vapor cloud explosion; this is associated with the leakage of fuel into the atmosphere, where it mixes with air and can subsequently ignite. 8.3.1 Turbulent Premixed Flame Structure

A common example of turbulent premixed combustion is the flame above a Bunsen burner, where the flow of premixed reactants is turbulent due to the high velocity of the mixture in the feeding tube, or due to a turbulence-generating grid. The apparent

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thickness dT of the turbulent premixed flame is much larger than the thickness of a laminar flame dL of the same mixture under the same conditions (pressure and temperature). The reason for this is that turbulence causes flapping and wrinkling of the instantaneous flame front. From the flame angle, the so-called turbulent flame speed ST , which is greater than the laminar flame speed SL , can be estimated. Damk€ohler was the first to suggest the existence of two different types of turbulent premixed flame [27]. The first type consists of thin and wrinkled flame elements, whereas in the second type chemical reactions occur in quite a thick zone. For the first flame type, associated with the so-called “wrinkled flame regime”, the turbulent flame speed pffiffiffi ST was expected to be determined by the velocity fluctuations intensity uRMS ¼ k, with k the turbulence kinetic energy. In contrast, in the second regime – often denoted “distributed combustion” – ST appears proportional to the product of the laminar propagation velocity SL with the square root of the turbulence Reynolds number [28]. Many investigations have been devoted to the analysis of the peculiar quantity ST from theoretical, numerical, and experimental points of view [28–35]. Nevertheless, the belief in a universal expression for ST versus both molecular and turbulence properties, has clearly failed. A more general description of the structure of turbulent premixed flames now requires the notion of a combustion regime diagram to be introduced. Under many practical circumstances, chemical reactions in premixed systems are localized in very thin surfaces, denoted “flamelets.” The turbulent flame brush can then be considered as a collection of laminar flame elements that keep their internal structure and are only affected by turbulence through curvature and strain-rate influence. On the other hand, “distributed combustion” is expected to occur for very large values of the turbulence Reynolds number. In this respect, the combustion diagram allows a delineation of the occurrence of different regimes, depending on the values of a retained set of well-defined parameters. 8.3.2 Turbulent Premixed Combustion Regime Diagram

The previous discussions benefit from the consideration of certain basic criteria, involving nondimensional ratios that have been found suitable for characterizing the structure of turbulent premixed flames through combustion diagrams [6, 36–38]. Such classifications are essentially based on nondimensional ratios that relate the laminar flame features (laminar flame thicknessdL and laminar flame propagation velocity SL ) to turbulence properties (velocity fluctuations uRMS and relevant characteristic length scales, such as the integral length scale l0 and the Kolmogorov length scale g) (see for example, Ref. [38]). The resultant diagrams are based on statistical information, and similar diagrams have been constructed for LES applications by using the filter size D and the subfilter velocity fluctuation u0D [39]. Using the Barrere–Borghi coordinates [36–38], such a combustion diagram is depicted in Figure 8.2. In this diagram, Da is the Damk€ ohler number, that is, the ratio of the integral turbulence time scale to a characteristic chemical time scale, and Ka is the Karlovitz number, the ratio between the chemical time scale and the Kolmogorov

8.3 Turbulent Premixed Combustion

T

u RMS /SL

=

chem

(Da 1) r

(Ka 100)

Thickened flames

Thickened wrinkled flames

L

Re 1 Wrinkled flames with pockets

l0 /

L

Wrinkled flames Figure 8.2 Turbulent premixed combustion regime diagram in the Barrere and Borghi coordinates [36–38].

time scale. Depending on the ratios of l0 to dL , and of uRMS to SL , different regimes of turbulent premixed combustion can be delineated. Typically, for a given mixture and a given experimental set-up, the value of the first characteristic ratio is fixed, whereas the second can be varied by increasing the mass flow rate and, as a result, the intensity of turbulence. This corresponds to the evolution along a vertical line in the diagram depicted in Figure 8.2. As the value of the ratio uRMS /SL reaches unity, the flame brush structure is likely to exhibit “pockets” of unburned gases, even if the PDF of the temperature or a species mass fraction still exhibits a two delta-peaked shape. The importance of the ratio uRMS =SL with respect to the flame structure, along with the connection to the formation of such “pockets”, was inferred as early as 1968 [40]. The crucial influence of this parameter on the flame topology was confirmed more recently [41]. Further increase of the characteristic velocity ratio leads to local thickening effects, as the turbulent eddies are able to enter the flame front, thus modifying the flamelet inner balance between molecular transport effects and chemical reactions. Finally, for distributed combustion, the turbulent flame thickness becomes larger than the integral length scale (no more peaks in the scalar PDF). 8.3.3 Progress Variable Formalism

To describe how turbulent combustion proceeds in premixed reactants, it is useful to introduce a normalized variable c 2 ½0; 1 that indicates the progress of the chemical reactions from the hot, fully burned product side ðc  1Þ towards the fresh reactants ðc  0Þ. This variable can be defined as a normalized composition: cðx; tÞ 

Yi ðx; tÞ
Yiu Yib
Yiu

ð8:9Þ

where Yi ; Yiu ; Yib denote respectively the mass fraction of species i and the corresponding value in fresh reactants and fully burned products.

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Restricting ourselves to situations where Fick’s law for diffusion applies, with a single diffusion coefficient Di ¼ D per species, this variable obeys the following transport equation:   q q q qc þ rvc ðrc uk Þ ¼ rD ðrcÞ þ qt qxk qxk qxk

ð8:10Þ

Provided that premixed combustion is not affected by heat losses, and can be described using a single irreversible chemical step, the consideration of this single variable provides a complete characterization of the premixed flame front. From Equation 8.10 the ensemble averaged transport equation for the mean progress variable reads:   q q q qc
~c þ
v ~c
u ~k ~c ¼ r rD
r u00k c00 þ r r qt qxk qxk qxk

ð8:11Þ

Much recent research effort has been devoted to the two major issues, associated with the closures of: (i) the turbulent scalar fluxes (the second term in the right-hand side (RHS) of Equation 8.11); and (ii) the mean chemical rate (last term in the RHS of Equation 8.11). Much insight have also been gained from experimental and numerical studies. At the same time, numerous theoretical studies have been carried out, with attention having been given to the underlying phenomena, expected to play at the smallest scales, as well as to the development of robust and reliable models for the prediction of turbulent flames over a wide range of combustion regimes. Many modeling strategies have been developed during the past twenty years to tackle the numerical simulation of turbulent premixed flames, especially for the flamelet regime of turbulent premixed combustion. The models can rely on algebraic relationships that hold between the mean chemical rate and the mean reactive scalar dissipation rate, as in the BML (Bray, Moss and Libby) approach [42, 43], or with a semi-empirical expression for the turbulent flame speed, as in the turbulent flame speed closure (TFC) approach proposed by Zimont and coworkers [44]. The studies of Bray et al. [45] have provided an interesting comparison of the different closures for turbulent premixed impinging flames. Other strategies have involved a transport equation for a quantity related to the mean chemical rate, such as the mean flame surface density [46] or the mean scalar dissipation rate [47–51]. It is worth noting that the latter quantifies the mixing rate, and its interest largely exceeds the restricted scope of turbulent premixed combustion in the very fast chemistry limit. A synthetic overview of the available models for premixed turbulent combustion is provided in the next section, by focusing on the most important results. 8.3.4 Fast Chemistry Models 8.3.4.1 The BML Model The analyses conducted by Bray, Moss, and Libby provided a well-grounded theoretical framework for the analysis of turbulent combustion with premixed

8.3 Turbulent Premixed Combustion

reactants [42, 43]. An important finding from the infinitely fast chemistry limit is that ~ c and the mean scalar dissipation rate ~ec (or x) are interthe mean chemical rate v related [47]: Ð1 cvðcÞPðcÞdc rec
v ~c ¼ with b0 ¼ Ð01 ð8:12Þ r 1=2
b0 0 vðcÞPðcÞdc Other important features, such as counter-gradient diffusion (CGD) transport and flame-generated turbulence (FGT), have also been evidenced in this framework [42, 43]. The joint PDF of the progress variable and velocity at a given time and location is expressed as the sum of fresh, fully burned, and burning gases contributions: Pðc; uk ; xk ; tÞ ¼ aðxk ; tÞdðcÞPu ðuk Þ þ bðxk ; tÞdð1
cÞPb ðuk Þ þ cðxk ; tÞf ðc; uk ; xk ; tÞ ð8:13Þ

where the coefficients denote the probability to have fresh reactants (a), burned products (b) and reacting mixtures (c), dðcÞ and dð1
cÞ are Dirac delta functions corresponding to fresh gases and fully burned products, and Pu ðuk Þ and Pb ðuk Þ are the conditional PDFs of velocity in fresh reactants and fully burned products. In the limit of high Damk€ohler values, that is, c ! 0, it follows from Equation 8.13 that the turbulent scalar flux can be expressed as:

~cð1
~cÞ uk b
uk u r c00 u00k ¼ r

ð8:14Þ

where uk b and uk u denote the conditional velocities in burned and unburned mixtures. This illustrates the possibility of either gradient diffusion (GD) or CGD, depending on the difference between the conditional velocities in burned and unburned gases. In experiments, CGD has been confirmed in the pioneering study of Moss [52], followed by other experimental investigations using more advanced laser diagnostics [53, 54]. Veynante et al. [55] introduced the Bray number NB, a dimensionless parameter to characterize, at least qualitatively, the transition between GD and CGD scalar transport in turbulent premixed flames: NB ¼

t SL 2a uRMS

ð8:15Þ

where t is the heat release parameter. According to Ref. [55], the parameter a is an order-unity function that describes the ability of various-sized eddies to affect the flame front. When NB > 1, CGD takes place, NB < 1 corresponds to GD. Recent studies of turbulent premixed combustion modeling have considered conditional pressure gradients [56–58]. Indeed, the interaction between combustion-induced density changes and the pressure gradient plays a crucial role in both CGD diffusion and combustion-generated turbulence [59]. Within the LES framework, these questions are challenging since the consideration of these phenomena – that is, the possibility of CGD, FGT, and unresolved thermal expansion phenomena in general – remain to be modeled at the sub-grid-scale level [60, 61].

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8.3.4.2 G-Equation Model A recent alternative framework for turbulent premixed combustion modeling takes advantage of the level set formalism [62], which is also widely used in two-phase flows [63]. This approach, called the “G-equation” model, consists of a transport equation for an iso-scalar surface Gðxk ; tÞ ¼ G0 , dividing the flow field into two distinct regions, where G > G0 corresponds to a region of burned gases and G < G0 refers to unburned gases. The resulting instantaneous field transport equation, first introduced by Williams in the field of combustion [64], reads: qG qG þ uk ¼ SL jrGj qt qxk

ð8:16Þ

The main issues associated with its use for turbulent combustion modeling, are described in details in the reference textbook of Peters [6], who provided models for the applications within the RANS context [65, 66]. The same formalism offers an interesting framework for LES of turbulent premixed combustion [67], although other strategies can also be used. In fact, it is possible to deal with the sub-grid-scale closure problem following two principal strategies, namely: (i) by resolving the flame front; or (ii) by capturing the front dynamics without resolving it. The first approach [68] relies on the “thickened flame method,” which allows to resolve the flame front directly on the LES grid. The strategies of the second type are either based on the G-equation as just described, or on the consideration of the filtered progress variable transport equation [69]. The main closure problems have been discussed above. 8.3.5 Finite-Rate Chemistry

For the regime of thickened flames, it is expected that the previous closures, developed for the fast chemistry limit, become less suitable. The influence of finite-rate chemistry effects is generally accounted for by considering the progress variable PDF. Nevertheless, it must be recognized that the applications of the transported PDF approach to premixed conditions still remain relatively scarce. Undoubtedly, this results from the difficulty associated with the closure of the micromixing terms for reactive scalars [70, 71]. The same difficulty exists for turbulent nonpremixed flames but, in this case, the use of nonreactive closures for these terms seems less critical. The essential issue of premixed combustion in the PDF framework is that, in order to recover satisfactorily the propagative characteristics, it is mandatory to take into account the strong coupling between molecular diffusion and chemical reaction [71], as emphasized in the early studies of Pope and Anand [70]. Finally, robust methodologies based on the presumed PDF approach are also available [72], that are well-suited to deal with the wrinkled flamelet as well as with the thickened flame regime, provided that a satisfactory closure is set forth to represent the mean scalar dissipation functions [49–51]. The corresponding strategies are also readily extended to partially premixed conditions [72], as discussed in Section 8.5.

8.4 Turbulent Nonpremixed Combustion

8.4 Turbulent Nonpremixed Combustion

In turbulent nonpremixed combustion, the fuel and oxidizer are fed separately to the reaction region, but combustion can only take place as they mix. There are parallels between (passive) scalar concentrations and velocity components. The diffusion coefficient D (in m2 s
1) plays the role of the kinematic viscosity n; the ratio of these quantities is the Schmidt number, Sc ¼ n/D. Whereas viscosity works on velocity gradients, the diffusion coefficient works on scalar concentration gradients. The smallest scalar length scale is called the Batchelor scale lB ¼ g Sc
1/2, with g the Kolmogorov scale from Equation 8.6. The counterpart of the turbulent dissipation rate e is the scalar dissipation rate x. The scalar dissipation rate damps species mass fraction fluctuations, just as e dissipates the turbulent kinetic energy (i.e., velocity fluctuations). Restricting ourselves to situations where Fick’s law for diffusion applies, with a single diffusion coefficient per species, and ignoring the Soret effect, the ensembleaveraged transport equations for the mass fractions of the different species, are:   ~ i Þ qð
~ iu ~k Þ qð
rY rY q qYi 00 u00 þ v _i þ ¼ rDi

rYg i k qt qxk qxk qxk

ð8:17Þ

The turbulent diffusion flux is typically modeled by means of the linear gradient ~i m t qY 00 00 rYg diffusion hypothesis, with the turbulent Schmidt number Sct:

i uk ¼ Sct qxk . As mentioned, the major issue is the closure of the averaged chemical source terms. 8.4.1 Eddy Break-Up and Eddy Dissipation Concept

During the early 1970s, the eddy break-up (EBU) model was presented [73], first for turbulent premixed combustion, but was subsequently elaborated to the eddy dissipation concept (EDC) [74]. In the reaction F þ O ! P,
the mean reaction 
ek Y~F 1
Y~F , where CEBU rate for premixed combustion is modeled as v_ F ¼
CEBU r is a parameter to be tuned with experiments. Under nonpremixed combustion, and with S the stoichiometric oxidizer/fuel mass ratio, this reads: v_ F ¼  ~ ~
A
r ke min Y~F ; YSO ; 1YþP S . As such, EBU and EDC cannot handle finite-rate chemistry. This point is revisited in Section 8.4.6.1. 8.4.2 Mixture Fraction Concept

The mixture fraction concept has been introduced in numerous textbooks (e.g., P Refs [3–10]). The starting point is the definition Zj ¼ N i¼1 mji Yi , with mji the mass of element j in component i. Due to the conservation of elements during reactions, P _ i ¼ 0. Starting from the the chemical source term for Zj is equal to zero: N i¼1 mji v instantaneous transport equations for the species mass fractions, and ignoring the

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Soret effect and assuming Fick’s law for diffusion with identical diffusion coefficients (Di ¼ D) for all species, a conserved scalar can be defined as, such that its ensemble averaged transport equation reads: ! ~k Þ qð
r~jÞ qð
r~ji u q qj 00 00 ¼ rD

rjg u ð8:18Þ þ i k qt qxk qxk qxk We immediately modified the notation from Z to j. This is a normalized quantity, called the “mixture fraction,” which is equal to zero in a pure oxidizer, to unity in pure fuel, and to a value in between for a mixture of fuel and oxidizer. As the chemical source term is identically zero, the closure problem is circumvented. Obviously, the price to pay is that all information is lost with respect to the chemical reaction process. Indeed, the number of elements is conserved, but the elements themselves provide no information on the reaction status. This information is brought back into the simulations through the chemistry model. This is explained in Section 8.4.5. As mentioned above, the turbulent flux is modeled with the GD hypothesis: mt q~ 00 00 j

rjg i uk ¼ Sct qxk . The value of the turbulent Schmidt number is typically lower in LES simulations (around 0.4) than in RANS simulations (around 0.7–0.9). 00 00 j is also important. Ignoring molecular The local mixture fraction variance ~g ¼ jg transport (high Reynolds number assumption), the transport equation is (e.g., Ref. [75]):   ~k Þ qð
r~g Þ qð
r~g u q mt q~g m q~ j q~ j þ þ2 t ¼

r~ x qt qxk qxk Sct;g qxk Sct qxk qxk

ð8:19Þ

Mixture fraction variance is thus generated by mean mixture fraction gradients, 00

 00

qj while the scalar dissipation rate ~x ¼ 2Dqj qxk qxk attempts to reduce the variance (as

mentioned above). Assuming similarity to the dissipation of the turbulent kinetic energy, a simple model for the scalar dissipation rate is: ~ x ¼ Cw ~~ke ~g , where Cw is usually taken as 2. This model implies that the time scale for the scalar dissipation rate is proportional to the integral turbulent time scale. It should be noted that, in LES combustion simulations, the large-scale fluctuations in mixture fraction are resolved. For the sub-grid-scale variance, a transport equation can be solved, or an algebraic expression is used, relating the sub-grid-scale variance of mixture fraction to the square of the gradient of the resolved Favre mean mixture fraction: 00 00 j ¼ CD2 ðrjÞ:ðrjÞ. The constant C can be determined dynamically [76]. jg SGS

There is ongoing discussion whether similarity arguments, on which dynamic procedures are based, remain valid in reacting flows. 8.4.3 Turbulent Nonpremixed Combustion Diagram

A classification of turbulent nonpremixed combustion in regimes is less straightforward than for premixed combustion. First of all, nonpremixed flames do not have

8.4 Turbulent Nonpremixed Combustion

well-defined proper characteristic scales, as a diffusion flame does not have a propagation speed and the local flame thickness is flow-dependent. Moreover, the reactants must mix before they can react, and the chemical reaction rates are typically limited by this mixing process. Note that, if mixing were fast compared to chemistry, the topology would become that of premixed combustion. The diagram can be constructed on the basis of different scales. The flame  1=2 . The scalar thickness can be related to a diffusion length scale [5, 6]: dF  Dx st

dissipation rate, which is proportional to the flow strain rate, also provides (the inverse of) a flow or mixing time scale. The smallest time scale is the Kolmogorov time scale (Equation 8.6), while the Kolmogorov length scale g is an estimate for the smallest diffusion length scale (flame diffusion thickness dd). The reaction thickness (dr) depends on the chemical time scale. The integral length scale is of the order of the mean mixing region thickness, and can be approximated by the Favre mean mixture  
1



fraction gradient: dm  r~j

. The Damk€ohler number is the ratio of the flow t

time scale to the chemical time scale: Da ¼ tfc  x 1tc . Extinction (or “quenching”) can st be expressed by stating that the Damk€ohler number becomes too low or, alternatively, that the scalar dissipation rate becomes too high (xst > xq ). The combustion regime and the corresponding flame structures then depend on the ratio of the different scales: . .

.

Fast chemistry: Da is high and the flame is very thin: dd  g  dr : laminar flamelet regime. Intermediate-rate chemistry: dd  g  dr : unsteady effects due to vortex–chemistry interaction to be expected, along with departure from the laminar flame structure. Slow chemistry: local or global extinction can occur.

The above qualitative discussion is based on data in Ref. [5]. In Ref. [6], the discussion is formulated in mixture fraction space, rather than in physical space. The most important observation is that such combustion diagrams for nonpremixed combustion depend much more heavily on the turbulent flow and mixing field than for turbulent premixed combustion. 8.4.4 Turbulence Closure: PDF

Fluctuations in the flow field due to turbulence, which govern the mixing of fuel and oxidizer, will strongly affect the combustion process, as explained above. Indeed, in general, reaction rates are strongly nonlinear functions of temperature and species mass fractions (and pressure). Consequently, the approximation of computing the mean chemical reaction rates from the mean species mass fractions and mean temperature, is very inaccurate. Typically, in turbulent nonpremixed combustion the Favre mean species mass fractions and temperature and the ensemble mean density

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are computed as: 01 1
1 ð ~ ¼ T ðjÞ~f ðjÞdj; r ~ i ¼ Yi ðjÞ~f ðjÞdj; T
¼ @ r
1 ðjÞ~f ðjÞdjA Y ð1

ð1

0

0

ð8:20Þ

0

The most common approach is to preassume that the Favre PDF ~f ðjÞ is a bfunction, and to determine its actual shape from the mean mixture fraction and its variance. This is described in numerous text books [3–10]. As mentioned, preassuming the shape of the PDF for a passive scalar (mixture fraction) is much more reasonable than for a reactive scalar. The approach can be followed in RANS and LES simulations. It should be noted that Equation 8.20 assumes that the thermochemical quantities are uniquely defined by the mixture fraction, while the chemistry model defines the relationships. However, this is only true for sufficiently fast chemistry, and joint PDFs should be used when more than one independent variable is employed. Typically, statistical independence is assumed between these variables; an example of this is described in Section 8.4.5.3. 8.4.5 Fast Chemistry Models

As explained in Section 8.4.4, the chemistry model provides information on the progress of the chemical reactions into the simulations. In the limit of infinitely fast chemistry, there is a unique relationship between the mixture fraction (and scalar dissipation rate) and temperature and species mass concentrations. Yet, there is some freedom to define this relationship. 8.4.5.1 Flame Sheet Model This model [77] assumes that chemistry is an infinitely fast irreversible reaction, taking place only when j ¼ jst. For all other mixture fractions, inert mixing is assumed between the combustion products and oxidizer (j < jst) or fuel (j < jst). This model does not account for intermediate species, and is sometimes called the “mixed-is-burnt” model. 8.4.5.2 Chemical Equilibrium Another possible assumption is that, for all possible mixture fraction values, there is instantaneously a chemical equilibrium; in practice, however, the Gibbs free energy is minimized. With this chemistry model, intermediate species and radicals can be taken into consideration, but the main drawback is that there is often insufficient time for the complete system to proceed to local chemical equilibrium. As a consequence (typically at the rich side), radicals and minor species mass concentrations (such as CO) are overpredicted. To a certain extent, this can be overcome by imposing constraints onto the equilibrium (e.g., Ref. [78]). Yet, this is not based on fundamental physics or chemistry.

8.4 Turbulent Nonpremixed Combustion

8.4.5.3 Laminar Flamelet Concept The laminar flamelet concept relies on the physical assumption that, in turbulent flames, the local flame structure, in the neighborhood of the reaction region, resembles a laminar flame that undergoes stretching from the surrounding turbulent flow field. Profiles, taken from laminar diffusion flames, are computed and expressed in terms of mixture fraction as an independent variable. The degree of stretching can be related to the local scalar dissipation rate in the turbulent flow. The profiles, providing the relationship for species mass fractions and temperature as function of mixture fraction and scalar dissipation rate, can be computed with detailed chemistry and differential diffusion, and then used in Equation 8.14, yielding for the “steady laminar flamelet model” (SLFM) [79]: ~i ¼ Y

ð ð1 1

Yi ðj; xst Þ~f ðj; xst Þdxst dj

ð8:21Þ

0 0

Although laminar flamelet models could be classified as “finite-rate chemistry models” (e.g., Ref. [6]), they are included here because, for steady laminar flamelets, these finite-rate effects are hidden in Equation 8.21, and not included in the eventual combustion simulations themselves. The library of flamelets is parameterized by the scalar dissipation rate. There is an upper bound for the scalar dissipation rate (xext) above which the flames extinguish. Typically, statistical independence is assumed for mixture fraction and scalar dissipation rate. A log-normal distribution is assumed for the PDF of x. In the original flamelet equations, there is a time dependency term. In SLFM modeling, it is implicitly assumed that the scalar dissipation rate does not vary rapidly, so that this term can be omitted in the construction of the relationship Yi ðj; xst Þ. If this is not the case, the unsteady term in the flamelet equations, leading to a relaxation of the profiles, must be retained (this has been discussed for example, in Ref. [80]). This technique is related to the CMC-approach (see Section 8.4.6.3), and will be returned to at that time. 8.4.6 Finite-Rate Chemistry 8.4.6.1 Eddy Dissipation Concept The EDC of Section 8.4.1 can be extended for the inclusion of detailed chemical mechanisms in turbulent flows. The basic assumption is that reactions take place in the “fine scales,” that is, in small turbulent structures. Chemistry is modeled as a perfectly stirred reactor, with initial conditions equal to the local temperature and species mass fractions. The reaction time is taken as a fraction of the Kolmogorov time scale Equation 8.6. Typically, chemistry mechanisms are stiff and their integration becomes costly. Computational efficiency can be increased substantially by application of the “in-situ adaptive tabulation” (ISAT) technique [81].

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8.4.6.2 Transported PDF Although the preassumed PDF approach was described in Section 8.4.4, one disadvantage is that, in reality, PDFs do not always evolve within one class of functions. A more severe disadvantage in general is that chemistry needs to be parameterized by a (very) limited number of variables. The practical reason is that integrations as in Equation 8.21 are performed a priori, and the mean quantities are tabulated. During the combustion simulations, mean values retrieved and interpolated from the tables, while limitations in storage capacity limit the number of possible independent variables. A statistical independence of the basic PDF variables is also typically assumed to construct the joint PDF as a product on single-variable PDFs. With the transported PDF approach [82], the shape of the (joint) PDF is not preassumed, and the chemical source terms appear in closed form. In other words, in principle, no modeling is required for the chemistry. In practice, however, the high dimensionality of the PDF, when all species are involved, hampers the direct application of this technique in its full glory. Nevertheless, the application of the ISAT approach [81], in combination with increased computer power, makes this increasingly feasible. Fortunately, there is an evolution in composition space to lowdimensional manifolds in many applications, an observation which has led to a description of turbulent nonpremixed combustion chemistry in terms of mixture fraction and a limited number of progress variables (e.g., mass fraction of CO2, H2O and/or CO or combinations). Examples of these techniques include intrinsic low-dimensional manifold (ILDM) [83], flamelet-generated manifold (FGM) [84], flame prolongation of ILDM (FPI) [85], and reaction diffusion manifold (REDIM) [86]. The major modeling requirement in transported PDF simulations concerns the “micro-mixing” model. Indeed, mixing on the molecular level remains necessary for chemical reactions to take place. The most classical models are interaction exchange with the mean (IEM) [87], coalescence/dispersion (CD) [88] and the Euclidean Minimum Spanning Tree (EMST) [89]. More recently, the multiple mapping closure (MMC) approach has regained interest [90]. The transported PDF approach can be adopted in RANS or LES simulations (in the latter case it is known as filtered density function; FDF). Yet, due to the relatively high computational costs, most applications to date with the transported PDF approach have been two-dimensional or axisymmetric statistically stationary flows. 8.4.6.3 CMC The conditional moment closure (CMC) approach was introduced independently by Klimenko and Bilger, whose review [91] provided a complete description of the model approach. The basic idea is still to use integrations such as Equation 8.20, but to reformulate the integrand in terms of conditional averages, conditioned on mixture fraction, denoted as hYi jji or hT jji. Transport equations are solved for these conditional averages, so that the unconditional mean quantities depend not only on the evolution of the PDF in physical space, but also on the spatial variation of these conditional averages. The CMC equations for species are (with some relatively

8.5 Partially Premixed Combustion

gentle modeling):  qhYi jji q2 hYi jji qhYi jji 1 q  00 00 ¼ hxjji þhv_ i jji
huk jji
huk Yi jji
r ~f ðjÞ 2 qt qxk
~f ðjÞ qxk qj r ð8:22Þ

These equations are solved for all species, by dividing the mixture fraction range [0;1] into a number of intervals. Such a transport equation may also be solved for the temperature (or enthalpy). Thus, the dimensionality of the problem is much larger than with the approaches of Section 8.4.5. Nonetheless, the computational cost is typically less than for transported PDF simulations. The first three terms in Equation 8.22 are also present in the unsteady laminar flamelet models, as mentioned in Section 8.4.5.3. The major difference between CMC and unsteady laminar flamelets is thus in the conditional mean convection term and the conditional turbulent flux, for which various models exist, incorporating turbulent flow and mixing effects. In other words, when these terms are relatively unimportant, compared to the chemistry terms, there is little difference between (unsteady) laminar flamelet modeling and CMC. It should be noted that CMC includes temporal and spatial variations of the conditional scalar dissipation rate, which makes it applicable for a wide range of turbulent nonpremixed combustion simulations, including ignition and extinction. The chemical source term is worth mentioning at this point. In first-order CMC, it is assumed that hv_ i jji ¼ f ðhY1 jji; . . . ; hYN jji; hT jjiÞ; in other words, the turbulent fluctuations are ignored. Whereas, this is unacceptable for unconditional quantities, the error is much smaller (and often acceptably small) for conditional quantities, as the major source of error due to nonlinearity in the chemical source term is due to fluctuations in mixture fraction. The CMC technique can be further refined by second-order conditioning (i.e., conditioning on mixture fraction and mixture fraction variance) or double conditioning (i.e., conditioning on mixture fraction and enthalpy or a progress variable). With increasing computer power, CMC continues to attract more interest among the research community, and the development of submodels is currently an area of active research worldwide. Although CMC was originally applied to RANS simulations [92–94], it is also tractable in LES, such that combined CMC/LES simulations have begun to be reported.

8.5 Partially Premixed Combustion 8.5.1 Background

Modeling for partially premixed combustion is still at a quite embryonic state, compared to the modeling of premixed and nonpremixed combustion, as described

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in the previous sections. This is not because there is little practical or scientific interest in this type of flame; rather, it could be argued that many modern flames in low-pollution equipment are of this type. Such poor knowledge is more likely due to the large number of parameters involved, and the difficulties and complexities of the underlying phenomena. 8.5.2 Classification

The term “partially premixed” has very often conflicting and vague usage in the current research literature, and an attempt is made here to clarify its meaning. In some reports of nonpremixed combustion, “partially premixed” implies premixing a little air with the fuel so that the flame is still of a nonpremixed character (i.e., requiring air from the oxidizer stream to achieve combustion); the resultant fuel–air mixture in the fuel stream is beyond the rich flammability limit. This premixing is done, in fundamental experimental studies, in order to reduce soot formation and hence be in a position to deploy refined laser diagnostic techniques. A series of nonpremixed flames in the Turbulent Non-Premixed Flame Workshop [95] is of this nature. In practice, for example in domestic gas appliances, even a little premixing produces shorter, soot-free flames with obvious advantages over longer, purely nonpremixed flames. In some reports of premixed combustion, the term “partially premixed” implies that the mixture in the unburnt side of the propagating flame front is not homogeneous. Such situations may arise in direct-injection, spark-ignition engines, in gas turbine ignition, and in unwanted explosions of flammable gas releases. Figure 8.3 shows, schematically, a flame propagating in a nonuniform fuel–air mixture that contains stoichiometric composition. The degree of inhomogeneity is very important, and is best characterized by the PDF of the mixture fraction ~f ðjÞ and how this relates to the nominal flammability limits jlean and jrich (these are the standard limits within which laminar flame propagation is possible). The nature of Richer than rich flammability limit

ξ > ξst D

C ξst ξ < ξst

A B Leaner than lean flammability limit

Figure 8.3 Schematic of flame front propagating in an inhomogeneous mixture, with the nonpremixed flame trailing behind. Isolines of mixture fraction are shown. Details of the flame types A to D are provided in the text.

8.5 Partially Premixed Combustion

partially premixed combustion (propagation speed, flame shape, internal structure and emissions) depends crucially on this PDF and its spatial distribution. If ~f ðjÞ is narrow enough to contain only a small range of mixture fractions, and if these mixture fractions fall fully within the flammability limits, then the flame front would correspond to Flame A in Figure 8.3; this flame should be more accurately called a “stratified-charge premixed flame.” Flame B would be similar, but with the important distinction that now ~f ðjÞ is nonzero, where j < jlean. Such flames could sometimes include reaction even in mixtures with j < jlean due to assistance (by diffusion) of combustion at richer mixture fractions; however, unburned fuel or CO may escape from such lean regions. Flame C is again similar to flame A, in that ~f ðjÞ ¼ 0 at j < jlean and j > jrich, but now there is finite probability of encountering stoichiometric mixtures. Such flames have been called “premixed/nonpremixed” by Bilger et al. [96] and “turbulent edge flames” by Mastorakos [97]. The nonpremixed flame branch pegged on the stoichiometric mixture fraction (flame D in Figure 8.3) is not a conventional nonpremixed flame, as it consumes the products of the lean and rich branches of the flame front and not the original fuel and oxidizer, at least in the immediate vicinity of the so-called “triple point,” defined as the point where the lean and rich branches meet the stoichiometric mixture fraction. Far from the triple point, perhaps a sufficient new supply of reactants has occurred so that the trailing nonpremixed branch is close to the conventional nonpremixed flame expected between fuel and air. The overall turbulent flame can be thought of as the turbulent counterpart of the well-known “triple flame” that has been studied thoroughly in laminar configurations [98, 99]. Some recent findings on the structure and propagation speed of turbulent edge flames were reviewed in Ref. [97], but the available results were very limited. Turbulent flame speed correlations for stratified-charge flames are not available, and very little is known about the explicit effects of turbulent velocity and length scale on the overall propagation speed. Hence, extensive further research is necessary. From the above discussion, it is evident that significant complexity is present in partially premixed combustion. The term “partially premixed” itself – despite having been used here – is vague, and the reader is advised to always consider explicitly the PDF of the mixture fraction, and how this relates to the lean and rich flammability limits, when reading the growing research literature on this type of combustion. 8.5.3 Modeling

An extended review of the literature until 2000 on flame propagation in stratified mixtures is provided in Chapter 4 of Ref. [6]. A model based on the G-equation, and on a turbulent flame speed which depends on the laminar burning velocity as a function of the mixture fraction SL(j) and on ~f ðjÞ, apparently gives good results for the stabilization height of a lifted jet flame [100]. Experimental data on flame propagation in mixtures with inhomogeneities have recently become available from simplified geometries [101–103] and realistic directinjection, spark-ignition engines [104]. These results, together with findings from

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DNS [105], suggest that the inhomogeneity introduces an extra wrinkling mechanism that can make the turbulent flame speed higher in stratified mixtures with ~j < j compared to the equivalent homogeneous mixture. However, in stratificalean tion with ~j  jlean , the turbulent flame speed may decrease over the homogeneous flame at the stoichiometric mixture. However, these trends have not yet been captured by modeling. The progress variable approach to partially premixed combustion has been discussed in Ref. [106], where it was shown that if combustion were to be described by a progress variable and a mixture fraction, then additional terms that are at present unclosed, would appear in the governing equations.

8.6 Outlook

During the coming years, a further tendency from RANS turbulence modeling towards LES is to be expected, which would go hand-in-hand with increasing computing power, and with the result that higher Reynolds number cases would be tackled. In the same sense, DNS will be used for model development and validation, since more details are accessible than in experiments, and much added insight can be expected as such. The one drawback is that only relatively low Reynolds numbers will be investigated in the short term. With the expected increased research effort in LES, much attention will be devoted to quality assessment in the near future. Likewise, both in the domain of premixed and nonpremixed combustion, more experience will be acquired with existing combustion models, and adjustments developed where necessary. With regards to chemistry, much effort will be devoted to the “clever” reduction of detailed chemistry mechanisms, as detailed schemes are unaffordable in turbulent combustion simulations. In the case of turbulence–chemistry interactions, it will be interesting to see whether the transported PDF technique can be further developed (in terms of micromixing), and whether the high price in terms of computational time, particularly in combination with LES, is affordable. Beyond any doubt, a huge effort towards an efficient and accurate implementation of the numerics is to be expected here, although the alternatives, such as CMC and MMC, will remain of interest for the foreseeable future. The development of hybrid models between premixed and nonpremixed combustion that can model flames, such as that shown in Figure 8.3, based on PDF, flamelet, CMC, or MMC, will be of major importance.

8.7 Summary

In this chapter, turbulence modeling and turbulent scales were briefly discussed, and premixed and nonpremixed single-phase combustion considered. For turbulent premixed combustion, the regime diagram was introduced, the turbulent

References

flame speed discussed, and progress-variable formalism described. Fast chemistry and finite rate chemistry effects, with associated closure issues, were considered. For nonpremixed combustion, the eddy break-up model and the eddy dissipation concept were outlined, after which details of the mixture fraction approach were provided, along with the preassumed PDF approach. The laminar flamelet concept was discussed, and the importance of the scalar dissipation rate highlighted. Finite-rate chemistry effects were discussed, together with the concepts of transported PDF and CMC. The chapter closed with a discussion on partially premixed flames.

References 1 Tennekes, H. and Lumley, J.L. (1972) A

14 Merci, B., Roekaerts, D., Peeters, T.W.J.,

First Course in Turbulence, The MIT Press. Pope, S.B. (2000) Turbulent Flows, Cambridge University Press. Kuo, K.K. (1986) Principles of Combustion, John Wiley & Sons. Libby, P.A. and Williams, F.A. (1994) Turbulent Reacting Flows, Academic Press. Poinsot, T. and Veynante, D. (2005) Theoretical and Numerical Combustion, Edwards. Peters, N. (2000) Turbulent Combustion, Cambridge University Press. Law, C.K. (2006) Combustion Physics, Cambridge University Press. Cant, R.S. and Mastorakos, E. (2008) An Introduction to Turbulent Reacting Flows, Imperial College Press. Fox, R.O. (2003) Computational Models for Turbulent Reacting Flows, Cambridge University Press. Carey, W.J. (2006) New Developments in Combustion Research, Nova Science Publishers. Sagaut, P. (1998) Large Eddy Simulation for Incompressible Flows, Springer Verlag. Jones, W.P. and Launder, B.E. (1972) Prediction of laminarization with a 2equation model of turbulence. Int. J. Heat Mass Transfer, 15 (2), 301. Merci, B. and Dick, E. (2003) Heat transfer predictions with a cubic kepsilon model for axisymmetric turbulent jets impinging onto a flat plate. Int. J. Heat Mass Transfer, 46 (3), 469–480.

Dick, E., and Vierendeels, J. (2001) Application of a new cubic turbulence model to piloted and bluff-body diffusion flames. Combust. Flame, 126 (1–2), 1533–1556. Merci, B., Dick, E., and De Langhe, C. (2002) Application of an improved epsilon-equation to a piloted jet diffusion flame. Combust. Flame, 131 (4), 465–468. Wilcox, D.C. (1993) Comparison of 2equation turbulence models for boundary-layers with pressure-gradient. AIAA J., 31 (8), 1414–1421. Menter, F.R. (1994) 2-Equation eddyviscosity turbulence models for engineering applications. AIAA J., 32 (8), 1598–1605. Launder, B.E., Reece, G.J., and Rodi, W. (1975) Progress in development of a Reynolds-stress turbulence closure. J. Fluid Mech., 68, 537–566. Jones, W.P. and Musonge, P. (1988) Closure of the Reynolds stress and scalar flux equations. Phys. Fluids, 31 (12), 3589–3604. Van Maele, K. and Merci, B. (2006) Application of two buoyancy-modified kepsilon turbulence models to different types of buoyant plumes. Fire Safety J., 41 (2), 122–138. Smagorinsky, J. (1963) General circulation experiments with the primitive equations. Mon. Weather Rev., 91, 99–164. Germano, M., Piomelli, U., Moin, P., and Cabot, W.H. (1991) A dynamic subgridscale eddy viscosity model. Phys. Fluids A, 3, 1760–1765.

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9

10

11

12

13

15

16

17

18

19

20

21

22

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23 Pope, S.B. (2004) Ten questions

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25

26

27

28

29

30

31

32

33

34

concerning the large-eddy simulation of turbulent flows. New J. Phys., 6 (35). Klein, M., Sadiki, A., and Janicka, J. (2003) A digital filter based generation of inflow data for spatially developing direct numerical or large eddy simulations. J. Comp. Phys., 186 (2), 652–665. De Langhe, C., Merci, B., and Dick, E. (2005) Hybrid RANS/LES modelling with an approximate renormalization group. I: Model development. J. Turbul., 6 (13), 1–18. Meyers, J., Geurts, B., and Sagaut, P. (2008) Quality and Reliability of LargeEddy Simulations, ERCOFTAC Series. Damk€ohler, G. (1940) Der einfluss der turbulenz auf die flammengeschwindigkeit in gasgemichen. Z. Electrochem., 6 (11), 601–626. Summerfield, M., Reiter, S.H., Kebekely, V., and Mascolo, R.W. (1955) The structure and propagation mechanisms of turbulent flames in high speed flows. Jet Propulsion, 25 (8), 377–384. Talantov, A.V., Ermolaev, V.M., Zotin, V.K., and Petrov, E.A. (1969) Laws of combustion of an homogeneous mixture in a turbulent flow. Fizika Goreniya I Vzryva, 5 (1), 106–114. Karpov, V.P. and Severin, E.S. (1980) Effects of molecular transport coefficient on the rate of turbulent combustion. Fizika Goreniya I Vzryva, 16 (1), 45–51. Abdel-Gayed, R., Bradley, D., and Lung, F.K.-K. (1989) Combustion regimes and the straining of turbulent premixed flames. Combust. Flame, 73, 213–218. Zeldovich, Ya.B., Barenblatt, G.I., Librovich, V.B., and Makhviladze, G.M. (1985) The Mathematical Theory of Combustion and Explosions, Consultants Bureau, New-York. Sabelnikov, V.A., Corvellec, C., and Bruel, P. (1998) Analysis of the influence of cold front quenching on the turbulent burning velocity associated with an eddy break-up model. Combust. Flame, 113, 492–497. Lipatnikov, A. and Chomiak, J. (2002) Turbulent flame speed and thickness: phenomenology, evaluation, and application in multi-dimensional

35

36

37

38

39

40

41

42

43

44

45

46

47

simulations. Prog. Energy Combust. Sci., 28 (1), 1–73. Shy, S.S., Lin, W.J., and Wei, J.C. (2000) An experimental correlation of turbulent burning velocities of premixed methaneair combustion. Proc. R. Soc. A, 456, 1997–2019. Borghi, R. (1984) Mise au point sur la structure des flammes turbulentes. J. Chim. Phys., 81 (6), 361–370. Barrere, M. (1984) Mise au point, quelques recherches sur la combustion de la derniere decennie. J. Chim. Phys., 81 (9), 519–531. Borghi, R. (1985) On the structure and morphology of turbulent premixed flames, Recent Advances in the Aerospace Sciences (ed. C. Casci), Plenum Publishing, pp. 117–138. Pitsch, H. (2006) Large-Eddy Simulation of turbulent combustion. Annu. Rev. Fluid Mech., 38, 453–482. Shchelkin, K.I. (1968) Combustion hydrodynamics. Fizika Goreniya I Vzryva, 4 (4), 445–448. Pocheau, A. and Queiros-Conde, D. (1996) Scale covariance of the wrinkling law of turbulent propagating interfaces. Phys. Rev. Letters, 76 (18), 3352–3355. Libby, P.A. and Bray, K.N.C. (1981) Counter-gradient diffusion in premixed turbulent flames. AIAA J., 19 (2), 205–213. Bray, K.N.C., Libby, P.A., Masuya, G., and Moss, J.B. (1981) Turbulence production in premixed turbulent flames. Combust. Sci. Technol., 25 (3–4), 127–140. Karpov, V., Lipatnikov, A., and Zimont, V. (1996) A test of an engineering model of premixed turbulent combustion. Proc. Combust. Inst., 26, 249–257. Bray, K.N.C., Champion, M., and Libby, P.A. (2002) Premixed flames in stagnating turbulence, Part V: Evaluation of models for the chemical source term. Combust. Flame, 127, 2023–2040. Candel, S.M. and Poinsot, T.J. (1990) Flame stretch and the balance equation for the flame area. Combust. Sci. Technol., 70 (1–3), 1–15. Mantel, T. and Borghi, R. (1994) A new model of premixed wrinkled flame

References

48

49

50

51

52

53

54

55

56

57

propagation based on a scalar dissipation equation. Combust. Flame, 96 (4), 443–457. Mura, A. and Borghi, R. (2003) Towards an extended scalar dissipation equation for turbulent premixed combustion. Combust. Flame, 133, 193–196. Mura, A., Robin, V., and Champion, M. (2007) Modeling of scalar dissipation in partially premixed turbulent flames. Combust. Flame, 149 (1–2), 217–224. Chakraborty, N. and Swaminathan, N. (2007) Influence of Damk€ohler number on turbulence - scalar interaction in premixed flames. Phys. Fluids, 19 (4), 045103. Mura, A., Tsuboi, K., and Hasegawa, T. (2008) Modelling of the correlation between velocity and reactive scalar gradients in turbulent premixed flames based on DNS data. Combust. Theory Model., 12 (4), 671–698. Moss, J.B. (1980) Simultaneous measurements of concentration and velocity in an open premixed turbulent flame. Combust. Sci. Technol., 22 (3–4), 119–129. Frank, J.H., Kalt, P.A.M., and Bilger, R.W. (1999) Measurements of conditional velocities in turbulent premixed flames by simultaneous OH PLIF and PIV. Combust. Flame, 116, 220–232. Kalt, P.A.M., Chen, Y.C., and Bilger, R.W. (2002) Experimental investigation of turbulent scalar flux in premixed stagnation-plate flames. Combust. Flame, 129, 401–415. Veynante, D., Trouve, A., Bray, K.N.C., and Mantel, T. (1997) Gradient and counter-gradient scalar transport in turbulent premixed flames. J. Fluid Mech., 332, 263–296. Domingo, P. and Bray, K.N.C. (2000) Laminar flamelet expressions for pressure fluctuation terms in second moment models of premixed turbulent combustion. Combust. Flame, 121 (4), 555–574. Robin, V., Mura, A., Champion, M., and Hasegawa, T. (2008) A new analysis of the modeling of pressure fluctuations effects in premixed turbulent flames and its

58

59 60

61

62

63

64

65

66

67

68

69

validation based on DNS data. Combust. Sci. Technol., 180 (5), 997–1010. Robin, V., Champion, M., and Mura, A. (2008) A second-order model for turbulent reactive flows with variable equivalence ratio. Combust. Sci. Technol., 180 (10–11), 1709–1734. Bray, K.N.C. (1995) Turbulent transport in flames. Proc. R. Soc. A, 1941, 231–256. Tullis, S. and Cant, R.S. (2002) Scalar transport modelling in Large Eddy Simulation of turbulent premixed flames. Proc. Combust. Inst., 29, 2097–2104. Boger, M., Veynante, D., Boughanem, H., and Trouve, A. (1998) Direct numerical simulation analysis of flame surface density concept for large eddy simulation. Proc. Combust. Inst., 27, 917–925. Sethian, F.A. (1996) Level set Method, Cambridge Monograph on Applied and Computational Mathematics, Cambridge University Press, Cambridge United Kingdom. Sussman, M., Smereka, P., and Osher, S. (1994) A level set approach for computing solutions to incompressible two-phase flows. J. Comput. Phys., 114, 146–159. Williams, F.A. (1985) Combustion Theory, 2nd edn, Benjamin Cummins, Menlo Park. Peters, N. (1992) A spectral closure for premixed turbulent combustion in the flamelet regime. J. Fluid Mech., 242, 611–629. Peters, N. (1999) The turbulent burning velocity for large scale and small scale turbulence. J. Fluid Mech., 384, 107–132. Pitsch, H. (2005) A consistent level set formulation for large-eddy simulation of premixed turbulent combustion. Combust. Flame, 143 (4), 587–598. Colin, O., Ducros, F., Veynante, D., and Poinsot, T. (2000) A thickened flame model for large-eddy simulation of turbulent premixed combustion. Phys. Fluids, 12, 1843. Grinstein, F.F. and Fureby, C. (2005) LES studies of the flow in a swirl gas combustor. Proc. Combust. Inst., 30, 1791–1798.

j201

j 8 Modeling of Turbulent Combustion

202

70 Anand, M.S. and Pope, S.B. (1987)

71

72

73

74

75

76

77

78

79

80

81

Calculations of premixed turbulent flames by PDF methods. Combust. Flame, 67, 127–142. Mura, A., Galzin, F., and Borghi, R. (2003) A unified PDF-flamelet model for turbulent premixed combustion. Combust. Sci. Technol., 175, 1573–1609. Robin, V., Mura, A., Champion, M., Degardin, O., Renou, B., and Boukhalfa, M. (2008) Experimental and numerical analysis of stratified turbulent V-shaped flames. Combust. Flame, 153, 288–315. Spalding, D.B. (1971) Mixing and chemical reaction in steady confined turbulent flames. Proc. Combust. Inst., 13, 649–657. Magnussen, B.F. and Hjertager, B.H. (1977) On mathematical modeling of turbulent combustion. Proc. Combust. Inst., 16, 719–727. Veynante, D. and Vervisch, L. (2002) Turbulent combustion modeling. Prog. Energy Combust. Sci., 28, 193–266. Pierce, C.D. and Moin, P. (1998) A dynamic model for subgrid-scale variance and dissipation rate of a conserved scalar. Phys. Fluids, 10, 3041–3044. Burke, S.P. and Schumann, T.E. (1928) Diffusion flames. Industr. Eng. Chem., 20, 998–1004. Bilger, R.W. and Starner, S.H. (1983) A simple model for carbon-monoxide in laminar and turbulent hydrocarbon diffusion flames. Combust. Flame, 51 (2), 155–176. Peters, N. (1984) Laminar diffusion flamelet models in non-premixed turbulent combustion. Prog. Energy Combust. Sci., 10, 319–339. Pitsch, H., Chen, M., and Peters, N. (1998) Unsteady flamelet modeling of turbulent hydrogen-air diffusion flames. Proc. Combust. Inst., 27, 1057–1064. Pope, S.B. (1997) Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation. Combust. Theory Model., 1 (1), 41–63.

82 Pope, S.B. (1985) PDF methods for

83

84

85

86

87

88

89

90

91

92

turbulent reactive flows. Prog. Energy Combust. Sci., 11, 119–192. Maas, U. and Pope, S.B. (1992) Simplifying chemical kinetics – intrinsic low-dimensional manifolds in composition space. Combust. Flame, 88 (3–4), 239–264. van Oijen, J.A., Bastiaans, R.J.M., and De Goey, L.P.H. (2007) Low-dimensional manifolds in direct numerical simulations of premixed turbulent flames. Proc. Combust. Inst., 31, 1377–1384. Gicquel, O., Darabiha, N., and Thevenin, D. (2000) Laminar premixed hydrogen/ air counterflow flame simulations using flame prolongation of ILDM with differential diffusion. Proc. Combust. Inst., 28, 1901–1908. Bykov, V. and Maas, U. (2007) The extension of the ILDM concept to reaction-diffusion manifolds. Combust. Theory Model., 11 (6), 1–24. Dopazo, C. and O’Brien, E.E. (1974) Functional formulation of nonisothermal turbulent reactive flows. Phys. Fluids, 17 (11), 1968–1975. Janicka, J., Kolbe, W., and Kollmann, W. (1979) Closure of the transport-equation for the probability density function of turbulent scalar fields. J. Non-Equil. Thermodyn., 4, 47–66. Subramaniam, S. and Pope, S.B. (1998) A mixing model for turbulent reactive flows based on Euclidean minimum spanning trees. Combust. Flame, 115, 487–514. Klimenko, A.Y. and Pope, S.B. (2003) The modeling of turbulent reactive flows based on multiple mapping conditioning. Phys. Fluids, 15 (7), 1907–1925. Klimenko, A.Y. and Bilger, R.W. (1999) Conditional moment closure for turbulent combustion. Prog. Energy Combust. Sci., 25 (6), 595–687. Wright, Y.M., De Paola, G., Boulouchos, K., and Mastorakos, E. (2005) Simulations of spray autoignition and flame establishment with twodimensional CMC. Combust. Flame, 143, 402–419.

References 93 Kim, I.S. and Mastorakos, E. (2006)

94

95

96

97

98

99

100

Simulations of turbulent non-premixed counterflow flames with first-order conditional moment closure. Flow, Turbulence and Combustion, 76, 133–162. De Paola, G., Mastorakos, E., Wright, Y.M., and Boulouchos, K. (2008) Diesel engine simulations with multidimensional conditional moment closure. Combust. Sci. Technol., 180, 883–899. Turbulent Non-Premixed Flame Workshop. Web site: http://www.ca. sandia.gov/TNF/abstract.html. Bilger, R.W., Pope, S.B., Bray, K.N.C., and Driscoll, J.F. (2005) Paradigms in turbulent combustion research. Proc. Combust. Inst., 30, 21–42. Mastorakos, E. (2009) Ignition of turbulent non-premixed flames. Prog. Energy Combust. Sci., 35, 57–97. Lyons, K.M. (2007) Toward an understanding of the stabilization mechanisms of lifted turbulent jet flames: Experiments. Prog. Energy Combust. Sci., 33, 211–231. Buckmaster, J. and (2002) Edge-flames. Prog. Energy Combust. Sci., 28, 435–475. Chen, M., Herrmann, M., and Peters, N. (2000) Flamelet modeling of lifted turbulent methane/air and propane/air

101

102

103

104

105

106

jet diffusion flames. Proc. Combust. Inst., 28, 167–174. Renou, B., Samson, E., and Boukhalfa, A. (2004) An experimental study of freely propagating turbulent propane/air flames in stratified inhomogeneous mixtures. Combust. Sci. Technol., 176, 1867–1890. Kang, T. and Kyritsis, D.C. (2005) Methane flame propagation in compositionally stratified gases. Combust. Sci. Technol., 177, 2191–2210. Pasquier, N., Lecordier, B., Trinite, M., and Cessou, A. (2007) An experimental investigation of flame propagation through a turbulent stratified mixture. Proc. Combust. Inst., 31, 1567–1574. Drake, M.C., Fansler, T.D., and Lippert, A.M. (2005) Stratified-charge combustion: modeling and imaging of a spray-guided direct-injection sparkignition engine. Proc. Combust. Inst., 30, 2683–2691. Jimenez, C., Cuenot, B., Poinsot, T., and Haworth, D. (2002) Numerical simulation and modeling for lean stratified propane-air flames. Combust. Flame, 128, 1–21. Bray, K.N.C., Domingo, P., and Vervisch, L. (2005) Role of the progress variable in models for partially premixed turbulent combustion. Combust. Flame, 141, 431–437.

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9 Modeling and Simulation of Droplet and Spray Combustion Eva Gutheil

9.1 Introduction

Liquid fuels, which are frequently used in practical combustion systems such as internal combustion engines, gas turbines and liquid-fueled rockets, make a significant contribution towards present-day energy supplies. Normally, a liquid fuel is delivered into the combustion chamber as a turbulent spray, the character of which determines not only combustion efficiency and stability, but also pollutant formation. Hence, it is of major interest that turbulent spray combustion systems are well understood. The modeling and simulation of turbulent spray combustion is especially challenging, because it includes complex processes that involve turbulence, heat and mass transfer, phase change, and chemical reactions that are closely inter-related. Moreover, the reliable prediction of pollutant emissions requires the consideration of detailed chemistry, where nonequilibrium effects are to be taken into account. The modeling and simulation of technical sprays represent essential tools not only for the design of technical applications, but also for the study of their performance, efficiency and optimization with regard to both spray flows and flames. Droplet evaporation and combustion are the underlying processes of any technical spray application, and deserve special attention. Notably, droplets interacting with each other and with the (laminar or turbulent) flow field and the flame, as well as those which influence each other or the walls and films are of particular interest in spray systems. The most important processes in a spray flame include injection and atomization, droplet evaporation, and consideration of the separated flow and gas phase, including turbulence and chemical reactions. These interact strongly one with another, and form an essential part of all turbulent spray flame computations. Details of spray injection and atomization are provided in Chapter 10, and will not be considered here. Moreover, numerical methods and their computational features and efficiency will be described only very briefly, and in relation to distinct problems. The main challenge of numerical simulations is finding a compromise between computational efficiency (including the

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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simplification of mathematical models) and the need for a detailed mathematical description of those relevant processes that determine the entire performance. In this chapter, attention is focused on the mathematical basics of droplet and spray evaporation and combustion. In particular, single droplets, interacting droplets and sprays will be considered, within convective nonreacting and reacting environments. The essentials here are a coupling of droplet evaporation and turbulence, the chemical reactions as well as the description of the spray. The latter will include droplet interaction, break-up and collisions, as well as agglomeration, though none of these processes are addressed in the present chapter. Currently, several methods are available when considering droplets in a dilute spray; these include the discrete droplet model, population models, and also methods of moments. The major features, advantages and disadvantages of each method are discussed in this chapter. In particular, attention is focused on the modeling and simulation of turbulence, on turbulent mixing, and on the interaction of evaporation with the turbulent flow field and the chemistry involved. Hence, details of the principal approaches to modeling sprays in a turbulent flow field, including Reynolds-averaged Navier– Stokes (RANS) equations, direct numerical simulation (DNS) and large eddy simulation (LES) are addressed. Details of the Reynolds stress model (RSM) and of probability density function (pdf) method are also presented and discussed. In the case of reactive spray flows, it is important to consider the chemical reactions involved. In order to address environmental concerns, detailed chemical kinetics (see Chapter 7) must be included to account for the prediction of pollutant emission and its reduction. Methods such as direct closure and (spray) flamelet models for turbulent spray flames are discussed. Moreover, reduced chemical mechanisms are described that require less computational time and are, therefore, more appropriate for technical applications. Reduced schemes are less accurate, however, and they tend to fail in situations where the intermediates deviate from a chemical equilibrium and steady state, away from the main chemical reaction zone. In areas where both chemical reactions and spray evaporation occur, standard models transferred from gas-phase models fail to predict the major features of spray flames. These regimes are especially challenging because in these areas the coupling of evaporation and combustion is strongest, and this requires close attention. Comparison of numerical results, using a variety of models and experimental data, mainly obtained with research burners, will be made in order to estimate the capacities and shortcomings of the present models. Areas of future research interest are also identified.

9.2 Droplet Evaporation and Combustion

Droplet evaporation and combustion are basic to any nonreactive and reactive spray flow. Studies of single droplet evaporation and combustion conducted since the mid-1900s have led to basic findings, such as the d2 law [1, 2]. This states that the

9.2 Droplet Evaporation and Combustion

droplet surface decreases linearly with time, and is still used in complex technical applications such as diesel engine combustion [3]. The d2 law includes assumptions [4, 5], such as an infinitely fast droplet heating and combustion in a thin flame sheet, as well as quiescent, infinite oxidizing environment and constant liquid and gas properties – to name the most important. Some assumptions may be relaxed through simple approaches, such as the Sparrow’s 1/3 rule [6], to account for the temperature-dependent physical properties in the immediate neighborhood of the droplet. However, others are more difficult to address, an example being the use of detailed chemistry [7–10]. The equations governing droplet heating, evaporation, and motion may be written in the following form [5] for spherically symmetric droplets:
 cpF ðT
Tl Þ qTl _ ¼m Ml cpl
LV ðTl Þ  Q l ð9:1Þ BT qt f lnð1 þ BM Þ _ ¼ 2prf Df rl Sh m

ð9:2Þ

d~ v 3r1 ¼ ð~ u
~ vÞj~ u
~ vjCD þ~ g ~ Fv dt 8 rl rl

ð9:3Þ

where LV is the temperature dependent latent heat of vaporization. BM and BT denote Spalding mass and heat transfer numbers, respectively. The droplet evaporation rate,   dMl d 4 _ ¼ prl rl3 is computed for a droplet in a convective gas environment ¼ m dt 3 dt f [5]. Equation 9.1 describes uniform droplet using the modified Sherwood number Sh heating; that is, the temperature is assumed to be spatially uniform. This assumption is valid for fuels with a high volatility under low pressure, and also for small droplets with short droplet heating times compared to droplet life time. It can be relaxed through the use of Equation 9.4 [5] for a spherically symmetric droplet:   qTl al q Tl r2 ¼ 2 qt r qr qr

ð9:4Þ

where al ¼ ll =ðrl cpl Þ is the heat exchange coefficient of the liquid. Equations 9.1 and 9.4 are rate-limiting for the fastest and slowest droplet heating, respectively, and are employed depending on the requirements of the application. In complex applications, the liquid properties are most often taken to be constant, even though characteristic phenomena such as droplet expansion during heating are only predictable using variable transport properties. A relaxation of the assumption of spherical symmetry requires the use of volume of fluid methods [11], which enable the consideration of deviation from onedimensional (1-D) treatment. Droplets typically are released into a convective environment, and the effect of convection on both the droplet interior and the evaporation process itself has been studied extensively [5]. The effect of convection on the droplet interior is of particular

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Figure 9.1 Binary droplet collision for We ¼ 20 without (row a and row b) and with (row c) evaporation. Reproduced with permission from Ref. [16].

interest in multicomponent droplets, where differences in the physical properties of the liquid may lead to extraordinary phenomena such as microexplosions [12]. In droplet and spray-drying processes, any differences in liquid properties may lead to different structures in the resultant particle [13]. In technical applications, droplets do not exist in an infinite oxidizing environment, but rather are influenced by any neighboring droplets. Several investigations have been conducted with two droplets either side-by-side [14, 15] or in tandem [8]. The aim of these studies was to investigate the effects of hydrodynamic forces in the presence of neighboring droplets, where parameters such as droplet size and separation distance are monitored. The flow field around the droplet was seen to change with gas velocity, and flow separation occurred downstream of the droplets. In a convective flow field this may affect both the ignition time and the ignition position [8]. The effect of binary droplet collision is shown in Figure 9.1, where rows a and b represent computational studies without evaporation, and row c includes the evaporation of water droplets. There was a qualitative difference between the results shown in rows a and b in the middle of droplets with, in particular, the center droplets showing a reduction in width (row a) and an expansion (row b), whereas the difference between rows b and c was minor. Hence, it was concluded [16] that evaporation did not have a pronounced effect on droplet collision. Figure 9.2 shows the ignition position behind a methanol droplet in air at elevated pressure, where the ignition has been shifted away from the axis of symmetry due to flow separation in the droplet wake. Thus, the droplet separation seen in the figure had a pronounced effect on the droplet ignition time, and particularly on the position of droplet ignition.

9.3 Spray Evaporation and Combustion

In technical spray applications, spray evaporation and combustion typically occur in a turbulent gas flow, where the cold spray is injected into an either cold or hot oxidizing

9.3 Spray Evaporation and Combustion

Figure 9.2 Methanol droplet. Zoom of the gas-phase velocity at ignition. Reproduced with permission from Ref. [8].

environment. The spray injection may be achieved through pressure or twin-fluid atomizers (see Chapter 10), and stabilization of the flow may include bluff bodies, leading to swirl. Flame stabilization through the use of pilot flames is more common in pure gas flames. Thus, the numerical simulation of these flows will include an excellent understanding not only of the spray process but also of the turbulent gas and the interaction of the evaporation with the nonreactive or reactive flow field. 9.3.1 Euler–Euler Models

Spray models may be characterized depending on the density of the spray – that is, the ratio of the volume covered by the liquid and the gas. The insert of Figure 9.3 shows

Figure 9.3 Time-averaged liquid volume fractions along the axis of round, pressure-atomized water sprays in still air. Measurements and locally homogeneous flow (LHF) predictions from Tseng et al. [17]. Reproduced with permission of the Combustion Institute and the American Institute of Aeronautics and Astronautics, Inc.

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a sketch of the dense spray region for atomization break-up. Here, the dense spray core is surrounded by a dispersed flow region, which eventually becomes a round dilute spray [17]. The comparison of measurements with the locally homogeneous flow (LHF) model shows qualitative agreement with the experimental data. If the spray density is large, Euler–Euler methods [18] must be applied which essentially use two sets of Navier–Stokes equations for the gas and the liquid phase. These equations contain source terms for the exchange between the two phases, and an indicator for the phase volume fraction, a, ranging from zero to unity depending on the volume covered by the liquid, a, and the gas, 1
a. These methods have the disadvantage that the shape of the liquid or droplet is lost, such that a description of the evaporation becomes more difficult compared to the case of a dilute spray. More recent models not only treat the gas and the liquid phases as separate sets of equations, but the different components of the spray are also accounted for through the use of different sets of Navier-Stokes equations for the phases, as well as the droplet size classes [19]. Thus, there is one phase for the gas and n phases for the liquid, where n refers to the number of different droplet size groups [19] or to the different components of the liquid [18, 20]. These models require the definition of a liquid volume fraction, ak , for the volume covered by phase k, over the total volume [19]: n X dðak rk Þ þ rðak rk~ Ckl vkÞ ¼ dt l¼1;l 6¼ k

ð9:5Þ

P where the constraint ak ¼ 1 must be satisfied. Here, rk denotes density, ~ v k the velocity vector of phase k, n the number of different liquid phases, and Ckl is the interfacial mass exchange term. These equations must be solved together with the continuity, momentum, and energy equations for the gas and liquid phases, as well as equations of state, interface conditions and equations for interactions between the different size classes of the spray. 9.3.2 Euler–Lagrange Models

Models for dilute sprays typically use an Euler–Lagrange formulation, where the dispersed phase is described by Lagrangian equations and the gas phase by Eulerian equations, with source terms for mass, momentum, and energy, Lv ; Lm ; Le , to account for the interaction with the spray. The instantaneous governing equations for mass, momentum in i direction, i ¼ 1; 2; 3, for enthalpy, hg , and for the mass fractions of chemical species, Yi , i ¼ 1; . . . ; M, may be written as: qr q ¼
ðruk Þ þ Lv qt qxk

ð9:6Þ

N X qðrui Þ qðruk ui Þ qtki qp ¼


þ rn~ b n þ Lm qt qxk qxk qxi n¼1

ð9:7Þ

9.3 Spray Evaporation and Combustion N X qðrhg Þ q q_q ðruk hg Þ
Ek þ rn ð~ u n
~ u Þ~ bn ¼
qxk qxk qt n¼1 3 X 3 quj qp qðpuk Þ X þ þ
tij þ Le qt qxk qxi i¼1 j¼1

qðrYi Þ qðruk Yi Þ ¼
þ Mi w_ i þ diF Lv qt qxk

ð9:8Þ

ð9:9Þ

where ui , i ¼ 1,2,3, are the gas velocity components, t is the stress tensor, p pressure, r is the gas density, and ~ b the outer forces. Yi and vi denote the mass fraction and molar chemical reaction rate of species i, respectively. The phase exchange terms Lv ; Lm , and Le may be written as [4] Lv ¼

ðð

Lm ¼


ðð

Le ¼



F v f dr l d~ Ml ~ v


ðð ðð

ðð

ðð

_ f drl d~ m v

ð9:10Þ

_ u
~ mð~ vÞf drl d~ v

ð9:11Þ

4 p rl2 rl Rl f dr l d~ v¼


_ pF ðT
Tls Þ þ LV ðTls ÞÞf dr l d~ ðQ l þ m½c v Fv~ v v f dr l d~ Ml ~

ð9:12Þ

In Equation 9.11, Ml ¼ 4=3prl3 rl denotes the droplet mass. ~ F v is the drag force (c.f. Equation 9.3), and f ¼ f ðrl ;~ x ;~ v; tÞdrl d~ x d~ v denotes the distribution of the droplets in a spray – that is, the probable number of droplets in the radius range drl around rl within d~ x around ~ x with velocity d~ v at ~ v at time t. Equations 9.1–9.3 or Equation 9.4 for droplet heating, vaporization, and motion (as discussed in Section 9.2) enter Equations 9.10–9.12, and therefore, the gas and liquid equations are strongly coupled. Moreover, the distribution function f needs to be specified. This may be achieved through several approaches. The droplet distribution function within a spray may be considered through discrete droplet models [5, 22, 23] which are quite well established. Here, the droplets are distributed in several size classes, and the droplet number density is obtained (c.f. Figure 9.4). This procedure is quite feasible to account for droplet size distributions from experiments [24, 25] (c.f. Vol. 2 Ch. 2 and Ch. 3), since experimental methods such as particle image velocimetry (PIV) or phase Doppler particle analysis (PDPA) result in discrete droplet size distributions. The problem of this approach is the fact that it is unclear as to how many size classes are needed to sufficiently represent the entire spray. On the one hand, a certain number is needed to describe the polydispersity of sprays, but on the other hand, the computational time increases with droplet size class. Currently, there is no mathematically profound criterion to determine the optimum number of droplet size groups in discrete droplet modeling. The Lagrangian part of numerical codes

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Figure 9.4 Droplet distribution of a water spray in an air flow. Reproduced with permission from [21].

contributes tremendously to the computational time of a solver. Moreover, it is not only the number of size classes that may be a problem, but also the choice of width of the different size groups. Typically, equal-sized groups are considered. Another category of models to account for the spray processes within the Eulerian–Lagrangian description are PDF methods. Here, the droplet size distribution is assumed to be continuous, and the spray equation yields [4, 26] qf q qðvk f Þ qðFk f Þ ¼
ðRl f Þ

þ Q f þ Cf qt qrl qxk qvk

ð9:13Þ

2 _ In Equation 9.13, Rl ¼ drl =dt ¼
m=ð4pr l rl Þ is the change of droplet radius, rl , with time, while Q f denotes the velocity for the formation or destruction of particles through processes such as nucleation and break-up. The term, Cf , describes changes due to droplet collisions. These last two terms must be considered in nondilute sprays. The numerical solution of this equation is challenging, especially when used in dense spray computations. A novel approach of Fox et al. [27, 28], which was developed for aerosols, has a high potential to be applied in spray computations. This method belongs to the group of population balance theory, and is referred to as the discrete quadrature method of moments (DQMOM). This group of models does not consider the PDF of the spray distribution itself, but rather focuses on the first moments of the PDF. In this sense, the method is less precise than PDF methods, as higher moments are neglected. However, both methods have shortcomings, and therefore, care must be taken in evaluating their advantages and disadvantages. A review of the development of the DQMOM method is available in Ref. [27]. The transport equation of the moments of a particle size

9.3 Spray Evaporation and Combustion

distribution suffers from the fact that, in deducing the transport equations for the moments, higher-order moments appear which need to be closed using relationships that include lower-order moments; this so-called closure problem causes insecurity in the modeling of the moment equations. The DQMOM method assumes that the particle size distribution is written as a summation of the weighted, multidimensional Dirac delta functions [27]: f ðj;~ x ; tÞ ¼

N X

va ð~ x ; tÞd½j
hjia ð~ x ; tÞ

ð9:14Þ

a¼1

where N is the number of delta functions, va ð~ x ; tÞ is the weight of node a, and hjia ð~ x ; tÞ is the property vector of node a. This presumed functional form may be thought as N distinct dispersed phases, each of which is characterized by the weight factor and its property vector. This equation then is substituted into the population balance equation. With the definition of weights va and abscissas, za ¼ va hjia , the DQMOM transport equations may be written. The source terms of the transport equations of the weights, va and the N weight abscissas, za , can be defined through a linear system involving the 2N integer moments; that is, for N ¼ 3, the moments m0 ; m1 ; . . . ; m5 are obtained where these moments are defined as [27]: mk ¼

1 ð

jk f ðjÞdj ¼


1

N X

va hjika

ð9:15Þ

a
1

The accuracy of the method increases with the increase of N, and a value of N ¼ 3 is considered reasonable. It should be mentioned that, on occasion, the solution matrix becomes singular and the weights and abscissas do not represent an independent system of equations. The advantage of the DQMOM method [28] is the ease by which to include the processes of droplet interactions, such as nucleation, aggregation, breakage, and growth [29]. 9.3.3 Turbulence Modeling

Typically, technical applications of sprays occur under turbulent flow conditions in order to improve the mixing, and to reduce the length of the spray core. Turbulence causes statistical fluctuations of all variables that determine the flow field, and these require a specialized numerical treatment. The easiest and most precise – but most computer time-consuming – method is the DNS of turbulent (reactive) flows [30, 31]. In this category of models, all turbulent lengths and time scales are resolved directly, and no modeling of turbulent fluctuations is necessary. The disadvantage of the DNS is the high computational costs, and for complex technical applications this category of models is not yet suitable, although simplified model systems have been considered [31]. Large eddy simulation [32, 33] is a method where large turbulence scales are resolved directly, and a filter function determines which smaller time scales are modeled, using methods such as RANS equations or PDF models, or the RSM.

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Traditionally, the consideration of the liquid phase occurs within a Lagrangian framework, whereas the continuous phase is described in an Eulerian system. A novel approach considers an Eulerian formulation for the spray, which then is coupled with the LES solver for the gas phase [33]. Models which refrain from directly resolving the large scales are RANS models, RSMs, and PDF models. The RANS models can be characterized by a Reynolds averaging of the Navier–Stokes equations, where the instantaneous characteristic variables, W, first are replaced by the sum of their time or Favre-averaged values (W or ~ respectively), and their fluctuations, W0 and W00 . In turbulent reactive flows, the W, Favre (density) -averaged values must be taken into account because of the high density changes associated with chemical reactions. After Reynolds averaging of the set of Navier–Stokes equations, unclosed terms appear which have the form ru00k W00 , for Favre-averaged values; these are the so-called “Reynolds stress terms.” Here, r is the gas-phase density, uk the gas-phase velocity component in k direction, and W denotes a characteristic variable such as total mass, enthalpy, gas velocity component, mixture fraction, or mass fraction of chemical species. The Reynolds stress terms must then be closed by either a gradient flux approximation (this type includes the widely used k
e turbulence model) or by derived transport equations for these terms. The latter approach leads to the RSM where transport equations for the second moments are deduced and higher moments are closed using simplified assumptions, leading to a closure of order two. The major benefit of the RSMs compared to gradient flux models is their ability to describe recirculation regimes, as well as counter-gradient diffusion. Models of the k
e type additionally suffer from the assumption of isotropic turbulence. However, the gain in precision for the RSMs goes hand in hand with additional computational costs for solving the additional transport equations of the second moments. Details of the RSMs are discussed below. For turbulent spray flows, the correlation between the characteristic variables of the flow field and the evaporation rate requires special treatment in RANS models (for the k
e model, see for instance Ref. [23]). When considering turbulent mixing in a spray flow, using a mixture fraction of the gas phase, j (defined such that it attains a value of unity for pure fuel and zero for pure oxidizer), the transport equation for the 002 , of the mixture fraction, yields: Favre mean, ~j, and the variance, jf ! qðr ~jÞ qðr~ uk ~jÞ q geff q~j þ
ð9:16Þ ¼ Lv qxk qt qxk s~j qxk ! qðr~ j00 2 Þ q q geff q~ j00 2 00 2 ~ þ ðr~ uk j Þ
qt qxk qxk s ~j002 qxk ! !2 geff ~e 00 2 q~j 2 ¼2
2r ~ j þ 2j00 Lv ð1
jÞ þ j00 Lv ; ~k qxk sj002

ð9:17Þ

where the last two terms in Equation 9.17 denote the interaction with the spray, and Lv denotes the evaporated spray mass.

9.3 Spray Evaporation and Combustion

Figure 9.5 Fuel vapor mass fluctuations at 2 cm and t ¼ 0.8 ms [34]. Comparison of experimental data from laser-induced exciplex fluorescence (LIEF) [34] with numerical results from Demoulin and Borghi (DB) [35], Hollmann and Gutheil (HG) [23], and Rev
eillon and Vervisch (RV) [36]. Reproduced with permission from [34].

Several approaches have reported to close these terms, with a survey and an evaluation of current models being provided by Subramanian et al. [34]. Figure 9.5 compares three models for the closure of the interaction of the turbulent mixing, and the evaporation fluctuations, with experimental data. The model derived by Demoulin and Borghi [35] (DB) assumes that the vaporization is restricted to the droplet surrounding, and that the local mixture fraction is close to the saturation value. The model by Hollmann and Gutheil [23] (HG) assumes proportionality between the evaporated liquid mass and the mixture fraction, as well as their turbulence intensities. This is a more global model which considers the influence of evaporation not only in the near droplet surrounding. Rev
eillon and Vervisch [36] (RV) used an approximation of the conditional spray evaporation source term as a monotonic function of the mixture fraction. The numerical results obtained from these models can be compared with experimental data from laser-induced exciplex fluorescence (LIEF) [34]. These authors concluded that the evaporation source term in the mixture fraction variance equation could not be neglected [34] since, inside the core of the spray, its order of magnitude was close to the variance production and dissipation terms due to average mixture fraction gradient and scalar dissipation rate. Under the experimental conditions used [34], the HG model performed better than the DB and RV models, both of which overpredicted the variance due to evaporation. Modeling of the correlation terms of evaporation and gas velocity is required in the RSMs [37]; here, the Favre-averaged Reynolds stress terms, ru00i u00j , were modeled through additional transport equations, as discussed above. Originally, the RSM was successfully developed for turbulent recirculating gas flows [38], and was subsequently extended [39] for use in spray flows. Moreover, Beishuizen [40] derived the Reynolds stress equations with an emphasis on modeling of the pressure rate of the strain tensor.

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The major difference in gas-phase versus liquid/gas systems is the vaporization process, which must be considered if atomization and droplet break-up and coalescence are to be neglected. Thus, in this case a dilute spray is considered, while the turbulent fluctuations caused by the flow field in reactive flows will require the consideration of Favre-averaged equations [41]. The formulation here is given in three-dimensional coordinates, for generality. Equation 9.18 shows the transport equation for the Reynolds stress term [39, 42]: 0 1 00 u00    ~k qug q g q q 2 i j A 00 u00 ¼ 00 u00 @CS r ug þ Pij
dij r~e þ ru00i u00j r~ uk ug i j ~e k j qxl qt qxk qxk 3 0 1 0 1 00 u00 ug 2 1 i j
dij A
C2 @Pij
Pll A
C1 r~e@ ~k 3 3 0 1 ð9:18Þ 2 þ C3 @rgi u00j þ rgj u00i
dij rgl u00l A 3 0 1 1 ~k @ qp g q p 00 u00 A qr
u00 u00 þ ug 4:3r ~e qxi j l qxj j l qxl   ~i u00j þ u ~j u00i Lv þ u00j Lm;i þ u00i Lm;j
u00i u00j þ u where the terms in the last line describe the interaction of the turbulent flow field with the vaporization source terms in the mass conservation equations, Lv , and the momentum equation, Lm (c.f. Equations 9.10 and 9.11). All other terms are standard terms that also appear in the gas-phase equations, and are closed following the uj ui 00 u00 q~ 00 u00 q~
rug procedure discussed in Ref. [42]. In the above equation, Pij ¼
rug j

k xk

i

k xk

describes the production of Reynolds stresses through shear stress. The constants CS , C1 , C2 , and C3 are 0.22 [43], 3.0 [44], 0.33 [44], and 0.5 [45], respectively. dij denotes the Dirac–Delta function, and gi is the gravitational force in i-direction. The Reynolds stress equations are solved together with the conservation equations for the momentum, the energy, and mass fractions of chemical species. Special care must be taken when discretizing the source terms of Equation 9.18, in order to assure convergence [42]. Figure 9.6 displays the Reynolds stress terms of a turbulent free methanol/air spray jet, where the experiments of McDonell and Samuelson [24] are used. The qffiffiffiffiffiffiffi ~ 002 agreement of the axial velocity fluctuations, u was quite good, even though the

~, was considerably lower in the experiment compared Favre-averaged mean value, u to the simulation near the axis. Various reasons for this disagreement have been proposed, and these have been extensively discussed [23, 42, 46]. The major benefit of the RSM is its capacity to predict swirl; this is shown clearly in Figure 9.7, where eddies are shown that cannot be computed by using the k–e model [39]. Another category of turbulence model has been developed, which avoids any mathematical modeling of the interaction of evaporation and other scalar (as described above), since the spray source term appears in the closed term. These

9.3 Spray Evaporation and Combustion

Figure 9.6 Comparison of simulated (lines) [42] and measured [24] (symbols) Reynolds stress terms at x ¼ 25 mm. Reproduced with permission from Ref. [39]; Ó 2006, Springer-Science and Business Media.

PDF models were introduced by Pope in 1985 [47] for turbulent reactive gas flows. Their advantages are an exact treatment of convection, body forces, mean pressure gradient, and chemical reactions, if present (c.f. Section 9.3.4). More recently, the approach was extended to (nonreacting and reacting) spray flows, as the spray source term appeared also to be in closed form [48]. For a one-point, one-time Eulerian mass-weighted PDF for the mixture fraction ~f ðz; x; tÞ ¼ rðzÞhdðj
zÞ=hri, the transport equation can be derived following Pope’s studies [47], as given in Ref. [48]:
 

 qðhri~f Þ qðhri~f Þ qðhLv i~f Þ q q qj  þ Vi ¼
f þ CM z; g ~ qt qxi qz qz qxj qxj

ð9:19Þ

Here, the variable z is the sample space variable of the mixture fraction j. The terms on the left-hand side of Equation 9.19 appear in closed form, which is

Figure 9.7 Structure of a methanol/air spray flame [42]. Reproduced with permission from Ref. [39]; Ó 2006, Springer-Science and Business Media.

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particularly true for the spray source term, Lv , while the molecular mixing term on the right-hand side of the equation needs to be modeled. For modeling of the molecular mixing, several different approaches derived from gas phase modeling have been developed. The most common model is the interaction by exchange with the mean (IEM) model [47, 49], but this needs to be extended in order to account for spray evaporation [48]: ~e dj ðtÞ 1 Lv ¼
Cw ðj ðtÞ
~jÞ þ ~k hri dt 2

ð9:20Þ

where the last term is added to account for evaporation. Other models known for the modeling of molecular mixing include the Curl model [50] and the (modified) Curl model [51], as well as the Euclidean minimum spanning trees (EMST) model [52]. To the author’s knowledge, the latter two models have been used in gas flows, but not yet in spray flames. In gas flows, the Curl model appears to produce more exact results than the IEM model [51], while the EMST model produces the best results despite consuming the most computer time [52]. It may be interesting, therefore, to investigate both the modified Curl and EMST models by including evaporation effects for use in turbulent spray computations. Joint PDF methods can be used to simulate the entire turbulent spray flow, if all dependent variables are considered as dependants of the PDF, but unfortunately this procedure is very costly because of the huge computational effort involved. To the author’s knowledge, the maximum number of dependents so far is two, where in particular the effects of correlations between the two dependent variables have been studied for both nonreacting and reacting sprays (c.f. Section 9.3.4). Typically, flow characteristics are chosen that are particularly sensitive to the process, as well as to the models involved. The PDF method is suitable for use as a tool to improve critical model assumptions. Figure 9.8 shows different shapes of pdfs of the mixture fraction along the centerline using a one-dimensional pdf formulation as defined in Eq. 9.19. In gas flows, turbulent mixing is modeled through the assumption of presumed shapes of PDFs, such as the b function: PðjÞ ¼

Cða1 þ a2 Þ a1
1 j ð1
jÞa2
1 Cða1 ÞCða2 Þ

ð9:21Þ

where a1 and a2 are algebraic functions of ~ j and~ j00 2 evaluated from the transport Equations 9.16 and 9.17, and C is the gamma function. A study conducted by Miller and Bellan [53] showed that this presumed shape was not valid for spray flows. Subsequent studies performed by Ge and Gutheil [48, 54] showed that an extension of the b function PðjÞ ¼

Cða1 þ a2 Þ ðj
j Þ1
a1
a2 ðj
jmin Þa1
1 ðjmax
jÞa2
1 Cða1 ÞCða2 Þ max min

ð9:22Þ

where two new parameters jmin and jmax were introduced, appeared to overcome the shortcomings of the standard formulation (Figure 9.9). For the limits jmin ¼ 0 and jmax ¼ 1, the standard b function (Equation 9.21), was retrieved as a special case.

9.3 Spray Evaporation and Combustion

Figure 9.8 Probability density functions (PDFs) of the mixture fraction of a methanol/air spray along the centerline. Reproduced with permission from [48]; Ó 2006, Begell House, Inc.

The new parameters, jmin and jmax , shown in Figure 9.9, were close to the values qffiffiffiffiffiffiffi ~j  ~ j002 , although this definition did not always lead to a good approximation, and further research is required in this area. Hence, PDF methods constitute a valuable tool for the evaluation of basic model assumptions, as well as for the modeling of turbulent spray flows. 9.3.4 Chemical Reactions in Spray Flows

Chemical reactive flows have been the subject of many numerical studies, and the exponential temperature dependence of the Arrhenius rate expression (see Chapter 2)

Figure 9.9 Comparison of the b PDF (b1), modified b PDF (b2) and Monte Carlo (MC) PDF (right) [48]. Reproduced with permission from [48]; Ó 2006, Begell House, Inc.

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requires special consideration in both laminar and turbulent flames. Fuels such as kerosene or diesel comprise many components, the chemical reaction mechanisms of which are not well known. The detailed chemical reaction mechanisms, which consist of elementary reactions, are extensive, and the chemical reaction rate constants are not well known. For combustion problems, the CHEMKIN code [55] was developed by Sandia National Laboratories in California, USA. This database includes thermodynamic data (c.f. Chapter 6) as well as reaction rate constants of the elementary kinetic reaction steps for a wide variety of gas-phase fuels. Several model categories for gas-phase combustions can be extended to spray flames, where special attention must be paid to the interaction between evaporation and combustion. Although combustion in technical applications typically occurs under turbulent conditions, laminar spray flames are widely investigated to gain a better understanding of the chemistry, as well as its interaction with the spray. The counterflow configuration is a well-established set-up that has been studied extensively for both gas and spray flames. An excellent survey of studies up until the year 1997 was provided by Li [56], but several other groups subsequently reported information in this area [57–63]. Typically, the chemical reaction mechanisms are taken from gas-phase models such as the Gas Research Institute (GRI) mechanism [64] or Warnatz’s mechanism [65]. Laminar spray flame computations most often are carried out on two-dimensional (2-D) [57] or 1-D [59–63] grids, where the latter computations result from transformation using similarity transformations. That is, the 2-D equations are transformed into 1-D equations that account for similar velocity profiles at different points; these are then mapped onto one another by choosing suitable scaling factors [59]. This procedure makes the use of detailed chemical reaction mechanisms affordable. At the same time, these configurations have been used to reduce chemical reaction mechanisms by applying both steadystate assumptions of chemical species and assumptions of partial equilibrium of certain elementary reactions [66]. An overview of systematically reduced chemical reaction mechanisms has been provided by Peters and Rogg [67]. One recently developed method for reducing detailed chemical reactions is that of the intrinsic low-dimensional manifold (ILDM). This utilizes the fact that, in a typical reaction system, a large number of chemical processes occur so rapidly that they are not rate-limiting, and can be decoupled. ILDMs in the state space are identified with the property that, after a short relaxation time, the thermochemical state of the system has relaxed onto these attracting low-dimensional manifolds [68]. However, this approach has not yet been introduced for the simulation of spray flames. Flamelet models are widely used in gas flame computations [69, 70], and this approach has been extended to spray flames [25, 46, 48, 54]. Most studies include gas flamelets and ignore the effect of spray evaporation on the structure of the flamelet [23]. More recently, spray flamelets have been studied [59–63] to account for flame interaction with the spray; they have also been used in turbulent spray flame computations [25, 46, 54]. The basic concept of a flamelet model is that the combustion is determined by mixing through the mixture fraction, j, and its scalar dissipation rate, x, where the instantaneous scalar dissipation rate is defined as

9.3 Spray Evaporation and Combustion

x ¼ 2D



qj qxj

2

ð9:23Þ

All other variables, W, such as temperature and mass fractions of chemical species, are then evaluated from j and x through W ¼ Wðj; xÞ

ð9:24Þ

These dependencies are tabulated for use in turbulent flame computations. The effect of turbulent fluctuations on laminar flamelets is accounted for through the use of PDFs. In the case of a gas flame, the dependent variables of the PDFs are the mixture fraction, j, and its scalar dissipation rate, x (c.f. Equation 9.24). The turbulent ~ in the turbulent flow field is then evaluated (Favre-averaged) value of a variable W through integration over the laminar value, weighted by the Favre-averaged PDF ~ xÞ. For turbulent spray flames, this approach must be extended to account for the Pðj; dependence of the laminar spray flamelet on the initial droplet size, r0 , and velocity, v0 , and the equivalence ratio, Er ; that is: W ¼ Wðj; x; r0 ; v0 ; Er Þ:

ð9:25Þ

The Favre-averaged value in the turbulent flow field yields [46] ð1 ð1 ð1 ð1 ð1 ~ xst ; r0 ; v0 ; Er Þ ¼ Wðj; Wðj; xst ; r0 ; v0 ; Er Þ 0


1

0

0

0

ð9:26Þ

~ xst ; r0 ; v0 ; Er Þdj dxst dr0 dv0 dEr Pðj;

where the scalar dissipation rate, x, is replaced by its stoichiometric value, xst , which is the standard procedure in turbulent flamelet computations [46, 69]. The joint PDF, which depends on five variables, is then factorized, and simplified presumed shapes of unimodal PDFs are used to describe these marginal PDFs. For ~ the mixture fraction, PðjÞ, a b function or modified b function is used (c.f. Equations 9.21 and 9.22) [46, 54], whereas for the scalar dissipation rate at stoichiometry, xst , a logarithmic normal distribution with a standard deviation of 2 is used [71]. For the initial droplet size, two Dirac delta functions were used, and the PDFs of both the initial droplet velocity and equivalence ratio were taken to be Dirac delta functions with an initial droplet velocity and global equivalence ratio [46]. These assumptions, as well as an assumption of statistical independence, may not be very good choices, even though investigations have shown that the choice of one or two Dirac delta functions for the droplet size does not have any significant influence on numerical simulations [42, 46], whereas the consideration of the spray greatly improves the results [46] compared to pure gas flamelet computations. Another method that includes chemical reaction mechanisms is that of flameletgenerated manifolds [72]. In this case, the chemical reactions are parameterized through manifolds that are obtained from laminar flamelet computations [73], as described above. This is essentially a combination of ILDM and flamelet modeling, where the chemistry is taken from the flamelet computations and the tabulation method from ILDM. However, this method of considering chemical reaction

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mechanisms in spray flames has not yet been applied as a standard procedure, and further research is warranted in this area. If spray flames are turbulent, then the chemical rate expressions that appear in Arrhenius form, including exponential temperature dependence and the turbulent fluctuations of temperature, may greatly influence the rates of the chemical reactions. Direct closure methods use assumed shapes of the PDFs to account for turbulent fluctuations in the turbulent flow field, and their influence on the instantaneous chemical reaction rate, v_ i , for species i (c.f. Equation 9.9): ð ð w_ i ¼ . . . w_ i ðY1 ; . . . YM ; T; rÞPðY1 ; . . . YM ; T; rÞdY1 . . . dYM dTdr

ð9:27Þ

where PðY1 ; . . . YM ; T; rÞ is a multivariate joint PDF, although in practice this PDF is not feasible due to the numerical difficulties involved. However, if the simplified chemistry as discussed above is employed, the PDF will depend on only a few variables and, after factorization of the function (assuming statistical independence of the dependent variables) the presumed shapes of the PDF, such as the b function or modified b function, may be employed. Recent studies by Miller and Bellan [53], Selle and Bellan [74] and Ge and Gutheil [48, 54] have demonstrated that: (i) the presumed b function is not suitable for use in two-phase flows [48, 53]; and (ii) the variables such as evaporation rate and temperature [74] and mixture fraction and enthalpy [54] are not statistically independent. Figure 9.10 shows a comparison of the DNS-extracted PDF and the model PDF at the subgrid scale of a LES computation for the fluctuation of the gas temperature, T 00 , and that of the vapor mass fraction, Yv00 , for a nonreacting spray [74]. For pure gas flows the variables were uncorrelated, whereas for the two-phase flow a clear correlation was displayed [74]. The right-hand side of Figure 9.10 was obtained from a primitive of a modified Bessel function of the second type [74].

Figure 9.10 Comparison between the direct numerical simulation (DNS)-extracted PDF and the model PDF at the subgrid scale. Reproduced with permission from Ref. [74].

9.3 Spray Evaporation and Combustion

Figure 9.11 Contour plot of joint enthalpy-mixture fraction PDF at various positions. Reproduced with permission from Ref. [54].

Another way to check statistical independence is to use the joint PDF transport equation for two variables of interest. The joint PDF for the mixture fraction and enthalpy is considered in Refs. [25, 54], and correlation coefficients are evaluated at different positions in reacting turbulent methanol [54] and ethanol [25] sprays in air. Figure 9.11 shows contour plots of the mixture fraction and enthalpy in the region where evaporation has started but no mixing has occurred (left top); here, the variables are statistically independent. In areas where both evaporation and combustion occur (top right and bottom left), the variables are strongly correlated, and after evaporation has finished, a linear dependence is obtained (right bottom), which confirms the findings of Selle and Bellan [74] for reacting turbulent sprays. It is demonstrated that models for pure gas flames may not be transferred to spray flames, due to the impact of the liquid phase on the flame structure. Droplet and spray distribution can have a major influence on the entire flow field and combustion process; consequently, special care must be taken if gas-phase models are extended to sprays.

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9.4 Summary and Outlook

Today, a number of open issues remain with regard to droplet and spray modeling and simulation, including droplet interaction and the integration of such models into simulations of technical spray flames. Moreover, the statistical distribution of droplets within the spray has not yet been sufficiently described using current methods, and further investigations are warranted. The modeling of the effect of turbulence on spray flames has shown recent advancements, based mainly on the increasingly rapid operational speeds of modern computers, such that both DNS and LES have become more attractive. LES modeling suffers from subgrid models, where special attention must be paid to statistical dependence on characteristic variables, such as mixing and evaporation. Many open questions remain to be addressed, including the consideration of advanced chemical reaction schemes for use in spray flows. This also includes chemical reaction mechanisms for alternative fuels, notably biological fuels, although these are not addressed in this chapter.

References 1 Spalding, D.B. (1953) The combustion of

2

3

4

5

6

7

8

liquid fuels. Proc. Combust. Inst., 4, 847–864. Godsave, G.A.E. (1953) Studies of the combustion of drops in a fuel spray - the burning of single drops of fuel. Proc. Combust. Inst., 4, 818–830. Amsden, A.A. (1997) KIVA-3: A KIVA program with block-structured mesh for complex geometries. LA-13313-MS, UC-1412. Williams, F.A. (1985) Combustion Theory, 2nd edn, Benjamin/Cummings, Menlo Park, CA. Sirignano, W.A. (1999) Fluid Dynamics and Transport of Droplets and Sprays, Cambridge University Press, Cambridge. Hubbard, G.L., Denny, V.E., and Mills, A.F. (1975) Droplet evaporation: effects of transient and variable properties. Int. J. Heat Mass Transfer, 18 (9), 1003–1008. Marchese, A.J. and Dryer, F.L. (1996) Effect of liquid mass transport on the combustion and extinction of bi component liquid droplets of methanol and water. Combust. Flame, 105, 104–122. Aouina, Y., Gutheil, E., Maas, U., Riedel, U., and Warnatz, J. (2001) Mathematical modeling of droplet

9

10

11

12

13

14

heating, vaporization, and ignition including detailed chemistry. Combust. Sci. Technol., 173, 1–29. Strauch, R., Lipp, S., and Maas, U. (2006) Detailed numerical simulations of the autoignition of single n-heptane droplets in air. Combust. Flame, 145 (3), 533–542. Strauch, R. and Maas, U. (2008) The ignition of methanol droplets in a laminar convective environment. Combust. Flame, 153 (1/2), 45–57. Aulisa, E., Manservisi, S., Scardovelli, R., and Zaleski, S. (2003) A geometrical area-preserving volume-of-fluid method. J. Comput. Phys., 192, 355–364. Fu, W.B., Hou, L.Y., Wang, L., and Ma, F.H. (2002) A unified model for the micro-explosion of emulsified droplets of oil and water. Fuel Process. Technol., 79 (2), 107–119. Shabde, V.S., Emets, S.V., Mann, U., Hoo, K.A., Carlson, N.N., and Gladysz, G.M. (2005) Modeling a hollow micro-particle production process. Comput. Chem. Eng., 29, 2420–2428. Kim, I., Elghobashi, S.E., and Sirignano, W.A. (1993) Three-dimensional flow over two spheres placed side-by-side. J. Fluid Mech., 246, 465–468.

References 15 Srinivas, Y. and Gutheil, E. (2004)

16

17

18

19

20 21

22

23

24

25

26 27

Modelling of droplet interaction in convective gas flows. Prog. Comput. Fluid Dyn., 4 (3–5), 250–256. Schlottke, J. and Weigand, B. (2008) Direct numerical simulation of evaporating droplets. J. Comp. Physics, 227, 5215–5237. Faeth, G.M. (1996) Spray Combustion Phenomena. Proc. Combust. Inst., 26, 1593–1612. Vujanovi
c, M., Edelbauer, W., von Berg, E., Tatschl, R., and Dui
c, N. (2008) Enhancement and validation of an Eulerian-Eulerian approach for diesel sprays. Proceedings, ILASS Europe Conference, Paper 2–4, Como, Italy. Bayoro, F., Habachi, C., and Daniel, E. (2008) Numerical and physical basis of an Eulerian multi-phase flow model for the simulation of the liquid injection in internal combustion engines. Proceedings, ILASS Europe Conference, Paper 2-5, Como, Italy. Drew, D.A. and Passman, L.A. (1999) Theory of Multicomponent Fluids, Springer. Faeth, G.M. (1977) Current status of droplet and liquid combustion. Prog. Energy Combust. Sci., 3, 191–224. Stiesch, G. (2003) Modeling Engine Spray and Combustion Processes (Heat and Mass Transfer), Springer. Hollmann, C. and Gutheil, E. (1996) Modeling of turbulent spray diffusion flames including detailed chemistry. Proc. Combust. Inst., 26 (1), 1731–1738. McDonell, V.G. and Samuelsen, G.S. (1995) An experimental data base for the computational fluid dynamics of reacting and nonreacting methanol sprays. J. Fluids Engin., 117, 145–153. Ge, H.-W., D€ uwel, I., Kronemayer, H., Dibble, R.W., Gutheil, E., Schulz, C., and Wolfrum, J. (2008) Laser based experimental and Monte Carlo PDF numerical investigation of an ethanol/air spray flame. Combust. Sci. Technol., 180, 1529–1547. Williams, F.A. (1958) Spray combustion and atomization. Phys. Fluids, 1, 541–545. Marchisio, D.L. and Fox, R.O. (2005) Solution of population balance equations using the direct quadrature method of moments. Aerosol. Sci., 36, 43–73.

28 Fox, R.O., Lurent, F., and Massot, M.

29

30

31

32

33

34

35

36

37

(2008) Numerical simulation of spray coalescence in an Eularian framework: direct quadrature method of moments and multi-fluid method. J. Comput. Phys., 227 (6), 3058–3088. Laurent, F., Massot, M., and Villedieu, P. (2004) Eulerian multi-fluid modeling for the numerical simulation of coalescence in polydisperse dense liquid sprays. J. Comput. Phys., 194, 505–543. R
eveillon, J. (2007) Direct numerical simulation of sprays: turbulent dispersion evaporation and combustion, Multiphase Reacting Flows: Modelling and Simulation (eds D.L. Marchisio and R.O. Fox), Springer, New York. Wang, Y. and Rutland, C.J. (2007) Direct numerical simulation of ignition in turbulent n-heptane liquid-fuel spray jets. Combust. Flame, 149 (4), 353–365. Derksen, J.J. (2007) Lattice-Boltzmann method for multiphase fluid flow simulations and Euler-Lagrange large-eddy simulations, Multiphase Reacting Flows: Modeling and Simulation (eds D.L. Marchisio and R.O. Fox), Springer, New York. Boileau, M., Pascaud, S., Riber, E., Cuenot, B., Gicquel, L.Y.M., Poinsot, T.J., and Cazalens, M. (2008) Investigation of two-fluid methods for large eddy simulation of spray combustion in gas turbines. Flow Turbul. Combust., 80 (3), 291–321. Subramanian, G., Colin, O., Pires da Cruz, A., Vervisch, L., and Bruneaux, G. (2005) Modeling turbulent mixing of an evaporating spray. Proceedings, ICDERS, Montreal, Canada. Demoulin, F.X. and Borghi, R. (2002) Modeling of turbulent spray combustion with application to diesel like experiment. Combust. Flame, 129, 281–293. R
eveillon, J. and Vervisch, L. (2000) Spray vaporization in nonpremixed turbulent combustion modeling: a single droplet model. Combust. Flame, 121, 75–90. Launder, B.E., Reece, G.J., and Rodi, W. (1975) Progress in the development of a Reynolds-stress turbulent closure. J. Fluid Mech., 68 (3), 537–566.

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226

38 Landenfeld, L., Kremer, A., Hassel, E.,

39

40

41

42

43

44

45

46

47

48

49

50

and Janicka, J. (1997) Comparison of Reynolds stress closures for strongly swirling combusting jets. Eleventh Symposium on Turbulent Shear Flows, Grenoble. Ge, H.W., Urzica, D., Vogelgesang, M., and Gutheil, E. (2006) Modeling and simulation of turbulent non-reacting and reacting spray flows in reactive flows, Reactive Flows, Diffusion and Transport (eds W. J€ager, R. Rannacher and J. Warnatz), Springer. Beishuizen, N. (2007) PDF modeling and particle-turbulence interaction of turbulent spray flames, PhD thesis, TU Delft, The Netherlands. Libby, P.A. and Williams, F.A. (1994) Turbulent Reactive Flows, Academic Press, London. Vogelgesang, M. (2006) Reynolds stress modeling for turbulent spray flames, PhD thesis (in German), Heidelberg University, Germany. Daly, H.J. and Harlow, H. (1970) Transport equations in turbulence. Phys. Fluids, 13, 2634–2649. Gibson, M.M. and Younis, B.A. (1986) Calculation of swirling jets with a Reynolds stress closure. Phys. Fluids, 29 (1), 38. Launder, B.E. (1975) On the effects of a gravitational field on the turbulent transport of heat and momentum. J. Fluid Mech., 67 (3), 569. Hollmann, C. and Gutheil, E. (1998) Flamelet-modeling of turbulent spray diffusion flames based on a laminar spray flame library. Combust. Sci. Technol., 135 (1–6), 175. Pope, S.N.B. (1985) PDF methods for turbulent reactive flows. Prog. Energy Combust. Sci., 11, 119–192. Ge, H.-W. and Gutheil, E. (2006) PDF simulation of turbulent spray flows. Atomiz. Sprays, 16 (5), 531–542. Masri, A.R. and Pope, S.B. (1990) PDF calculations of piloted turbulent nonpremixed flames of methane. Combust. Flame, 81, 13–29. Curl, R.L. (1963) Dispersed phase mixing: I. Theory and effects in single reactors. AICHE J., 9, 175–181.

51 Janicka, J., Kolbe, W., and Kollmann, W.

52

53

54

55 56 57

58

59

60

61

62

(1979) Closure of the transport equation for the probability density function of turbulent scalar fields. J. Non-Equilib. Thermodyn., 4, 47–66. Masri, A.R., Subramanian, S., and Pope, S.B. (1996) A mixing model to improve the PDF simulation of turbulent diffusion flames. Proc. Combust. Inst., 26, 49–57. Miller, R.S. and Bellan, J. (1998) On the validity of the assumed probability density function method for modeling binary mixing/reaction of evaporated vapor in gas-liquid turbulent shear flow. Proc. Combust. Inst., 27, 1065–1072. Ge, H.-W. and Gutheil, E. (2008) Simulation of a turbulent spray flame using coupled PDF gas phase and spray flamelet modeling. Combust. Flame, 153 (1–2), 173–185. http://www.ca.sandia.gov/chemkin/ index.html. Li, S.C. (1997) Spray stagnation flames. Prog. Energy Combust. Sci., 23 (4), 303–347. Amantini, G., Frank, J.H., Smooke, M.D., and Gomez, A. (2007) Computational and experimental study of steady axisymmetric non-premixed methane counterflow flames. Combust. Theor. Model., 11 (1), 47–72. Dakhlia, R.B., Giovangigli, V., and Rosner, D.E. (2002) Soret effects in laminar counterflow spray diffusion flames. Combust. Theor. Model., 6 (1), 1–7. Gutheil, E. and Sirignano, W.A. (1998) Counterflow spray combustion modeling including detailed transport and detailed chemistry. Combust. Flame, 113 (2), 92–105. Schlotz, D. and Gutheil, E. (2000) Modeling of laminar mono- and bidisperse liquid oxygen/hydrogen spray flames in the counterflow configuration. Combust. Sci. Technol., 158, 195–210. Gutheil, E. (2001) Structure and extinction of laminar ethanol/air spray flames. Combust. Theor. Model., 5, 1–15. Gutheil, E. (2005) Multiple solutions for structures of laminar counterflow spray flames. Prog. Comput. Fluid Dyn., 5 (7), 414–419.

References 63 Continillo, G. and Sirignano, W.A. (1990)

64 65 66

67

68

69

Counterflow spray combustion modelling. Combust. Flame, 81 (3/4), 325–340. http://www.me.berkeley.edu/gri-mech/. Warnatz, J., Maas, U., and Dibble, R.W. (1999) Combustion, Springer, Berlin. Peters, N. (1985) Numerical and asymptotic analysis of systematically reduced reaction schemes for hydrocarbon flames, in Numerical Simulation in Combustion Phenomena, Lecture Notes in Physics, vol. 241 (eds R. Glowinski, B. Larrouturou and R. Temam), pp. 90–109. Peters, N. and Rogg, B. (1993) Reduced Kinetic Mechanisms for Applications in Combustion Systems, Springer, Berlin. Maas, U. and Pope, S.B. (1992) Simplifying chemical kinetics: intrinsic low-dimensional manifolds in composition space. Combust. Flame, 88, 239–264. Peters, N. (2000) Turbulent Combustion, Cambridge University Press, Cambridge, UK.

70 Bastiaans, R.J.M., Martin, S.M., Pitsch,

71

72

73

74

H., van Oijen, J.A., and de Goey, L.P.H. (2005) Flamelet analysis of turbulent combustion. Lecture Notes in Combust. Sci., 3516, 64–71. Peters, N. (1986) Laminar flamelet concepts in turbulent combustion. Proc. Combust. Inst., 21, 1231–1250. Bastiaans, R.J.M., van Oijen, J.A., and de Goey, L.P.H. (2006) Application of flamelet generated manifolds and flamelet analysis of turbulent combustion. Int. J. Multiscale Computat. Engin., 4 (3), 307–317. Bongers, H., van Oijen, J.A., Somers, L.M., and de Goey, L.P.H. (2005) The flamelet generated manifold method applied to steady planar partially premixed counterflow flames. Combust. Sci. Technol., 177 (12), 2373–2393. Selle, L.C. and Bellan, J. (2007) Evaluation of assumed PDF methods in two-phase flows using direct numerical simulation. Proc. Combust. Inst., 31, 2273–2281.

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10 Conventional and Innovative Spray Generation for Combustion Applications Raffaele Ragucci and Brian Milton

10.1 Introduction

Primary energy derived from the combustion of liquid fuels provides approximately one-third of current global energy requirements [1]. While this overall percentage has been declining in recent years, liquid fuels are still by far the main energy source for transportation and a significant source for electric power generation, and will remain so for a considerable time. Hence, research and development (R&D) aimed at the optimization of liquid fuel combustion systems is still of paramount relevance to combustion science and technology. The atomization and dispersion of a fuel in an oxidant flow is the first stage in any combustion process involving liquid fuels. The need for efficient and versatile atomizing and mixing devices stems from the observation that a key role in the liquid fuel combustion is due to the heat and mass exchange mechanisms (e.g., evaporation, stirring, and molecular diffusion) that occur mostly before the ignition and burning stages. These mechanisms are extremely sensitive to the available liquid–gas interface, and also to the degree of dispersion of the fuel in the oxidant. Fuel atomization must fulfill the basic requirements of producing a dramatic increase in the available liquid–gas interface, while achieving the best placement of fuel according to the specific flow-field configuration of the combustion device. The two main motivating forces for advancement in the knowledge and technologies of atomization are the limitation of energy resources, and the requirement for lower pollutant emissions. Both, a minimization of fuel use and a reduction in carbon dioxide (CO2) emission require an increase in the efficiency of liquid-fuelled combustion systems. The reduction of other pollutants, (e.g., particulates, NOx, organic compounds) is closely connected to the burning mixture quality, and to the achievement of a stable combustion process. In some cases – for example, in diesel engines – the improvement of fuel atomization devices and control systems can even cause a noticeable boost in performance while simultaneously reducing emissions.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Another aspect requiring further R&D effort is the often-desired use of heavier liquid fuels and nonfossil fuels as substitutes for the lighter, expensive fuels that are currently used fuels and which are, or very soon will be, in shorter supply. In this case, the design of fuel atomization systems to be substituted for those currently available becomes a major challenge. Atomization studies of liquid jets were commenced at the start of the eighteenth century with the experiments of Laplace and Young, although it was not until 1878, when Lord Rayleigh published his work on the breakup of liquid jets [2], that the onset of oscillation at the interface of the liquid and the consequent breakup were described mathematically in the framework of the method of linear stability. In the twentieth century, progress in atomization science was remarkable. In spite of the intrinsic difficulties in describing the atomization mechanism in a mathematical form, significant progress occurred in numerical modeling. After an initial phase, when the breakup of jets and droplets was described mainly by analogical simulations, the availability of new numerical tools and increasing computational capabilities allowed the implementation of numerical models based on the resolution of the fluid mechanics equations. Furthermore, the availability of sophisticated diagnostics in the last part of the twentieth century resulted in an unprecedented capability of experimentally characterizing the atomization process to support modeling activities. Improved design capabilities and the increasing need for more eco-compatible liquid fuel combustion systems promoted a massive R&D activity, and as a result the approach to atomization research moved from the optimization of standard systems to the design of advanced atomizers exploiting new and unconventional geometric configurations, working conditions, and physical effects These are now either in the developmental stage or in the process of being practically implemented, and represent the forefront of the current R&D in the field of atomization technology. A detailed presentation of the multitude of atomizers and atomization strategies in use is beyond the aims of this chapter, as whole books have been devoted to this task. The approach in this case will be to present the basic principles of the atomization process and possible implications. The effect of liquid fuel properties on atomizer performance, and the influence of the main parameters in typical combustion systems and conditions, will also be presented. A brief outline of selected practical combustion system atomizers will be provided, together with details of current trends in atomizer development and a selected example of an innovative atomization methodology. For a more general and exhaustive presentation of the many physical and technological facets of atomization, readers are referred to available literature in the field. Among the cited textbooks is that of Lefebvre [3], which provides a comprehensive compendium of the atomizing technologies used in the past century from a user’s point of view. It is therefore useful in the design of atomization systems for most standard conditions. Bayvel and Orzechowsky [4], in their book, have a more rigorous approach, and report part of the vast atomization knowledge developed in Eastern-European countries. Both books are considered essential support for engineers in the design of systems requiring of atomization techniques. Also cited is the book of Nasr et al. [5], for its detailed presentation of many practical injection devices

10.2 Basic Concepts of Atomization

and of their use in industrial applications. This latter is not focused on the atomization of fuels for combustion devices per se, but provides useful information on some classes of practical atomization devices used in combustion plants. More recently, Tropea et al. [6] thoroughly described available diagnostic techniques in the field of fluid-mechanics, including the most common techniques used in spray characterization. For those wishing to gain a deeper insight into the elementary physical subprocesses involved in droplet and spray behavior, the book of Sirignano [7] is authoritative (there is no comprehensive book on spray modeling), while that of Stiesch [8] reviews the most widely used models in the field of internal combustion engines. Another previously cited is that of Sirignano [7]. This lack of reliable references on spray atomization, in particular for high-speed sprays, reflects the unclear definition of the mechanisms leading to the primary breakup. Most available models require an appropriate choice of adjustable parameters, and when these cannot be made to fit the experimental data, the results are unsatisfactory [9]. In conclusion, physical models of primary atomization are still lacking, despite years of research in this area [9]. More recent approaches to the modeling of sprays aim to describe the evolution of the interface by means of advanced numerical algorithms. However, even those with a very high computational effort and promising progress are still mainly confined to simple cases, and cannot be currently used to model typical spray configurations in a reliable and predictive way [10].

10.2 Basic Concepts of Atomization

Atomization is the process of fragmenting a bulk liquid into a cloud of small droplets (literally, atomization implies the subdivision of liquid down to parcels of atomic size). Following the above considerations, in combustion systems this process is used to increase the fuel–gas interface sufficiently so as to make the occurrence of the evaporation and subsequent mixing phases possible in times compatible with the combustion process. This requirement implies the formation of a large number of droplets with radii as small as possible. In fact, the quality of an atomization process is mostly measured by the final size distribution of the droplets formed – the smaller the droplet, the better the atomization. In many cases it is also desirable to have a narrow size distribution in order to avoid the presence of very large droplets that may adversely affect both pollutant formation and combustion efficiency. Along with droplet size distribution, it is also desirable to know the joint probability density function of the droplet velocities. Droplet size distribution is determined by the mechanism via which the original liquid structure is fragmented (primary breakup), the possible subsequent rupture of the primary droplets (secondary atomization), and the possible coalescence phenomena due to impact of the droplets. The fragmentation of a liquid structure is the initial step in any practical atomization process. The definition of a physical model for primary atomization is very difficult, and only conceptual and or statistical approaches can be exploited to determine the

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size and velocities of the droplets formed. The most common approach is to model the fragmentation of liquid as a sequential cascade of droplet breakups, with the sizes becoming progressively smaller (according to the scheme originally proposed by Kolmogorov [11], with similarities to the continuous grinding of solid particles). The final limit of the cascade process is determined by the achievement of a balance between the disrupting forces (that are mainly due to aerodynamic stresses induced by the relative velocity of the gas–liquid interface phases) and the capillary pressure arising at the interface. The parameter used to quantify this balance of forces is the Weber number: We ¼

rv2 D s

ð10:1Þ

where D is the droplet diameter, v the relative velocity, r the gas density, and s the surface tension. When the size of the droplets reach a value corresponding to a Weber number (computed on the current droplet size) which is smaller than a critical value, the cascade process can be considered terminated. In this process, the resulting final distribution can be demonstrated to be of a log-normal type [11–14]: f ðDÞ ¼


ng ÞÞ2 ðlnðDÞ
lnðD 1 2s2 pffiffiffiffiffiffi e
Ds 2p

ð10:2Þ


ng is the number geometric mean droplet diameter, and s is the correspondwhere D ing standard deviation [7]. In this scenario, the final size of the droplets depends on the Weber number of the original droplet, since in the cascade process both the average drop size and its variance vary exponentially with the index of the iteration in the cascade [14]. For these reasons, the Weber number computed on the original droplet size is commonly used as an indicator of the possible atomization quality. As a general statement, a greater Weber number produces smaller final droplet average sizes and narrower size distributions. Although the cascade model is widely used in modeling jet breakup, it produces acceptable results in most practical situations. Nevertheless, other mechanisms have been suggested based on either phenomenological models [7, 12–14] or on numerical approaches [15, 16]. Additionally, there is recent experimental evidence [17] of the existence of a substantial similarity between the primary breakup occurring at the interface of a jet and the secondary breakup of a drop. In conclusion, the definition of a primary breakup model of general validity, as well as the feasibility of a direct numerical simulation, seems to be beyond the current state of the art of the research in the field. A simplified approach to the problem of liquid fragmentation can help in understanding the characteristic dimension and the role of liquid properties in the process. From a physical point of view, the detachment of liquid portions from the liquid–gas interface of a liquid structure (drop, ligament, sheet, etc.) is due to the onset, on the interface, of surface waves that, under opportune conditions, can be amplified and eventually lead to rupture of the interface.

10.2 Basic Concepts of Atomization

Excitation of these waves is due either to the existence of a slip velocity at the interface, or to other mechanical forcing purposely supplied. Restoring forces can be due either to gravitation, or to the onset of a capillary pressure which in turn is caused by the surface tension and the interface stretching. Long-wavelength waves are dominated by gravitation, while shorter ones are controlled by capillarity. It is possible to determine a characteristic length corresponding to the transition from gravitational to capillary waves; this is termed the “capillary length,” and is expressed by: rffiffiffiffiffi s LC ¼ ð10:3Þ rg The capillary length is about 1 cm for a typical liquid fuel at ambient temperature and pressure. Thus, the waves involved in the fragmentation of a liquid structure are essentially “capillary” or “surface tension” waves. The phase velocity of a capillary wave is equal to [18]: vphase

sffiffiffiffiffiffiffiffi 2ps ¼ lr

ð10:4Þ

while its group velocity is 3/2 of the phase velocity. It is worth noting that the smaller the wavelength of a capillary wave, the faster it propagates. This is the reason why ripples around a jet propagate faster than the jet, thus forming the typical annular perturbations around the jet that eventually contribute to its breakup [19]. One limiting effect of the prevalence of smaller wavelength perturbations is connected to the effect of viscous forces. In fact, considering that the viscous and surface tension forces are proportional to mvL and sL (where L is a characteristic dimension and m is the liquid viscosity) respectively, it follows that a surface tension wave is not damped by viscosity if its wavelength is sufficiently greater than 2 lDC ¼ 2pm sr . For shorter wavelengths, viscosity increasingly damps surface tension waves. In other words, the effect of higher viscosities is the progressive nonappearance of smaller droplets. For values of viscosity such that lDC approaches lC ¼ 2pLC , the excitation and propagation of surface tension waves becomes progressively impossible. A practical indicator of this condition is the Ohnesorge number (Oh or Z): m Oh ¼ pffiffiffiffiffiffiffiffi rsL

ð10:5Þ

For values of Oh greater than unity, a reasonable degree of atomization is difficult to attain. From these considerations, it is quite clear that the main liquid properties affecting the atomization are its surface tension, viscosity, and density, the first two of which sensibly decrease with increasing temperature. Fuel preheating, particularly when its viscosity can significantly affect the quality of atomization, is common practice in many combustion systems. The dependence of liquid density on temperature is generally less relevant to the atomization process.

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The effect of environmental pressure is mainly accounted for by the dependence on the Weber number in subcritical conditions [20]. At high pressure and supercritical ambient conditions the situation is less clear, and several effects can alter the atomization process, along with other phenomena associated with droplet behavior, such as vaporization and droplet interactions. At close to critical conditions, the hydrodynamic effects become increasingly more relevant due to both the decrease in surface tension and the increase in gas pressure at the droplet surface that allow surface instabilities to be established at lower velocities [21]. It is generally accepted that a secondary fragmentation of the droplets formed in the primary breakup may occur if the residual Weber number of these droplets exceeds a critical value (around 12, when viscous effects can be neglected). Secondary breakup is very common in most practical situations when the spray interacts with secondary air streams and can significantly modify the final drop size. A detailed illustration of drop breakup phenomena, associated with the onset of oscillation on a liquid–gas interface, was provided by Pilch and Erdman [22], in which bag type, umbrella, stripping and catastrophic regimes were defined by their Wevalues. Many interesting images and a more accurate analytical approach was reported [23], that also outlined the lack of some atomization regimes in the very popular classification proposed by others [22]. That is, caution should be used when evaluating the dependence of the different mechanism onset to particular Weber number values based only on the liquid properties. This is mainly connected to the different temporal evolution of surface perturbations that depend on the liquid properties (e.g., viscosity) and which can significantly shift or even prevent the appearance of a breakup regime. This is particularly relevant in the elaboration of numerical models of secondary breakup, when a Weber-based criterion is used to determine the occurrence of drop fragmentation. In some cases [24] it has been indicated that an efficient model for secondary atomization can produce reliable results on the final drop size and velocity distributions. However, it is important to reiterate that the accurate modeling of primary breakup is of paramount importance, because either primary atomization dominates and the effect of secondary atomization can be neglected (as in some industrial processes), or the primary drop size distribution is needed as an input to models that predict the secondary atomization [12]. With regards to this latter point, it should be noted that aerodynamic forces acting on a droplet depend on its size in a functional manner that is different from the dependence of droplet mass on the size. For this reason, droplet dynamics and vaporization rates change significantly with droplet size. Hence, accuracy in the initial droplet size and velocity distributions is crucial when determining spray behavior [7]. The effect of coalescence on the final drop size distribution cannot be simply estimated. Coalescence can occur following an impact between two droplets, and will depend on the relative velocity, the droplet diameter, and the collision angle [3]. Coalescence is most likely to occur in the near field of the spray where the droplet density is higher [25]. In less-dense regions of the spray, coalescence between droplets is less probable for typical combustion systems.

10.3 Liquid Fuel Atomization Applications

From the above considerations it is clear that atomization is a complex process of energy transfer that results in an increase in interface extension and also in a droplet’s kinetic energy. The process efficiency of the increase in surface energy is very low for all common atomizing devices, although the residual droplet kinetic energy can be used to optimize drop placement with respect to the combustion environment. The main difference between practical atomizers is their appropriateness to specific situations, both in terms of final drop size distribution and of liquid distribution. When it was suggested that it might be possible to classify atomizers based on the mechanism by which the energy is transferred to the liquid [4], mainly three types of atomizer became apparent: .

.

.

Atomizers where the energy is supplied directly to the liquid. In this case, liquid pressure is transformed to kinetic energy, accelerating the liquid and causing its disruption. This class includes jet atomizers, swirl atomizers, and jet swirl atomizers. The main difference between these atomizers relates to the different liquid distributions that ensue. Pneumatic atomizers, where energy is supplied to the liquid by a gas stream. Either a high-velocity, low-flow rate, or a relatively low-velocity, high-flow rate gas stream can be used to promote atomization. Mechanical atomizers, where the liquid is accelerated by a mechanical actuator; a rotating device is typically used for this purpose.

The other available atomizers are based either on acoustic, piezoelectric, or ultrasonic induction of vibrations to a liquid jet. Finally, atomizers exist which are based on an enhancement of the atomization process by means of an electrostatic field.

10.3 Liquid Fuel Atomization Applications 10.3.1 Power Generation Systems

In this section, the details are presented of the most common atomizers used in industrial applications, mainly in power generation systems. The nomenclature used is that of Lefebvre [3], which is widely employed in technical reports; the aim of this is to give a general perspective of the evolution of devices, and of the strategies used in recent years. As noted above, many advances in atomizing systems have been triggered by the need to reduce pollutant emissions from combustion devices, while other motivations have been connected with the nature of available fuels (often in relation to their economic advantages), and also to changes in the industrial model that is transforming the power generation system towards a more distributed scheme. There is also a need to recycle the in situ byproducts of industrial plants (typically refinery waste fuels and bioderived fuels), thus reducing the cost of their disposal and recovering energy to be reused in the productive cycle.

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Along with the evolution of atomizing devices, another important need is for a stronger coupling between the nozzle and the complete burner. In fact, today it is widely accepted that a good preparation of the fuel/air stream before the burning section can greatly help to reduce emissions while increasing the efficiency and robustness of the whole plant. Whilst this approach is of general validity, it has shifted the R&D studies towards the simultaneous design of an atomizing system and burner, with the aim of optimizing the subsequent combustion process. 10.3.1.1 Atomization of Conventional Liquid Fuels in Steady Plants Many atomizer designs are currently used in steady burners for energy production, process heat or steam generation, and to disperse a wide variety of oil-derived liquid fuels. A preferential requirement in the recent past has been the flexibility of the atomizing system so as to utilize different fuels with high viscosity, and high flow rates. After an appropriate preheating of the fuel to reduce its viscosity, this task has generally been accomplished by means of simple swirl nozzle. It is well known that the main problem affecting these atomizers is the small range of flow rates at which they produce an acceptable atomization quality. A common strategy is to use multiple nozzles and perform the turndown by switching off some nozzles while keeping the feeding pressure almost constant at an optimal value. The need for a more efficient coupling between the fuel spray and the combustion air, along with the aforementioned limitations of the swirl atomizer, has led to the creation of many alternative nozzles that use air or steam flows to improve the atomization quality and extend the range of useful flow rates. The basic working principle of these nozzles is a mixing section where the liquid interacts with a gas (or steam) flow at high velocity, generating a turbulent dispersion of droplets leading to good quality atomization, independent of the fuel flow rate. In some cases the liquid/ gas mixture is allowed to interact further in a mixing chamber before its passage through an outlet nozzle. A typical example is the Y-type atomizer, in which the liquid fuel is injected into a gas–steam flow at a suitable angle in order to allow penetration and partial disruption of the liquid. Multiple Y-ducts are generally mounted on a single nozzle head in order to achieve the optimum spray angle and optimize liquid dispersion in the combustion chamber. One limitation of this type of atomizer is the possible segregation in the final part of the orifice, caused by a nonuniform distribution of the liquid in the gaseous stream. To overcome this problem, atomizers with a turbulent mixing chamber, that homogenize the liquid–gas mixture prior to its injection into the combustion chamber, have been used. These nozzles are capable of achieving smaller mean droplet sizes, but are more expensive as they generally require high gas/steam flows and are more complex than simple Y-type nozzles. The concept of mixing the two phases in a duct or a chamber is widely used in practical devices, and many different configurations have since been developed; these are mainly variants of the Y- and turbulent-chamber type, to suit the particular requirements of the combustion system and fuel characteristics [3–5]. Another class of atomizers used in stationary plants is based on rotary nozzles. The main reason for using these is the need to atomize very viscous fluids or fluids that contain significant amounts of solid particles that might obstruct the ducts of

10.3 Liquid Fuel Atomization Applications

pressure- and air-assisted nozzles. In practical use, the rotary atomizer is coupled to a gas or steam flow to realize an air-blast rotary atomizer. This design is very efficient and capable of significant turndown, as its performance does not depend heavily on the fuel properties. The main drawbacks of these atomizers are their complexity and the presence of moving parts, which not only makes them more expensive than previous injector types but also leads to a need for more frequent maintenance. 10.3.1.2 Atomizers for Terrestrial Gas Turbines One common requirement of atomizing systems in terrestrial gas turbine plants, either for heat, electric or mechanical energy generation, is the possibility of using more than one type of fuel. Very often, these systems use a gaseous fuel (either natural gas or synthetic mixtures available as byproducts of other processes) as the main source, with a liquid fuel as backup. In some cases, three fuels are used either alternatively or together to permit the use of low calorific value (LCV) gases. In smaller systems, where the combustion process is essentially of a diffusive nature, the atomizers are common plain jets (mainly used for gas fuels) and simple or air-blast atomizers for liquid fuels. These are mounted in opportune positions in the inlet duct so as to optimize the combustion process. In some cases (mainly in larger systems), dual-fuel injectors that are capable of injecting two fuels (e.g., a LCVgas and a light oil) simultaneously are used. For this, one or more air-blast atomizers are combined with a gas distributor in order to mix the liquid fuel and the gas with a swirled air-flow. On occasion, the injector may also be capable of supplying watersteam to reduce pollutant formation in the combustion chamber. However, whilst this makes the atomizers a very valuable component of the system, they are also very complex. More advanced gas-turbines are designed to burn premixed mixtures in lean conditions to reduce pollutant formation. This requires the use of a premixing duct where the fuel is injected and mixed with the combustion air. Very often, the use of lean premixed and prevaporized strategies is used to achieve very low emission levels. The design of the atomizing system and of the premixer must be made in close conjunction in order to realize the liquid atomization, vaporization, and mixing with air in times that are compatible with the residence time, while avoiding back-ignition. The greater complexity of the whole mixture preparation system is partly relaxed by the use of relatively simpler atomizers. In these systems, either jet or prefilming airblast atomizers are used, exploiting a swirled air-flow to realize air-blast atomization systems. 10.3.2 Propulsion Systems

Fuel preparation essentially controls the subsequent ignition and combustion process, the burn rate (which is a factor in determining the thermodynamic efficiency), and the exhaust emissions. In reciprocating engines, such as gasoline and diesel types, the time during which fuel preparation, combustion and expansion take place is severely limited in the rapidly repeated rotational cycle. In aircraft gas

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turbines, the continuous and often steady flow removes some restrictions, although the fuel supply rate must vary enormously as the power requirements change; a simple nozzle would then require large pressure changes across it to control this effect. 10.3.2.1 Automotive Spark-Ignition (SI) Engines In SI engines, the fuel flow is a continuous stream in the case of carburetors and the early types of fuel injection systems, although in more modern systems the fuel is introduced as a series of discrete pulses. At low power settings, the latter are short and well spaced, whereas at higher power they are longer and more closely spaced, allowing more fuel to enter the engine. Thus, the time available for the fuel injection in each cycle of a four-stroke engine is two engine revolutions. The fuel flow in these original systems was controlled by either the nozzle pressure ratio or nozzle flow area. Today, modern port injection systems open and close the injector in each engine cycle, such that the injector opening time becomes the essential control variable. The forthcoming gasoline direct injection (GDI) or direct injection spark-ignition (DISI) engines are more limited, as the power and exhaust strokes are excluded from the fuel introduction process. Carburetors basically consist of fixed or variable venturi types, the latter being termed constant-depression. The former system used the venturi pressure drop to provide the nozzle pressure ratio, which falls as the air flow rate reduces and more or less proportionally reduces the fuel flow rate. Primary atomization was poor at low speed or load, which made a secondary atomization at the partially closed throttle valve and the engine inlet valves essential. The latter system used a variable area venturi connected to a needle that regulated the fuel supply aperture area, thereby giving a more consistent primary atomization. In both systems, the nozzle pressure ratio for primary atomization was only about 2 or 3 : 1. Multipoint (port) injection, which is now most common, uses pressure ratios of 4 or 5 : 1 and pintle (external needle) -type injectors which provide a large area, hollow cone spray. Because these are downstream of the throttle plate, secondary atomization occurs only at the inlet valves. The fuel quantity is determined by the injector opening time, which can range from almost two complete engine revolutions at maximum speed and load down to a fraction of that value as the air flow is reduced proportionally by throttling and a lower engine speed. The injection may be either batch-fired (i.e., all injectors operating together) or sequential, with each cylinder having its own individual timing. The latter approach is mainly beneficial away from the maximum fuelling requirements, where the full two engine revolutions are not required for fuelling. The injection pressure is maintained at the maximum value at all times, and hence the primary atomization is superior to that of carburetors. The new in-cylinder injection (GDI) engines are limited to between 0.25 and 0.5 of the engine cycle. GDI injectors have to cope without the secondary atomization, and may be injecting into a high-pressure in-cylinder air during compression. Hence, they require high pressures to provide an appropriate mixture, these being up to about 12 MPa. The injector spray is directed towards the spark plug so as to obtain an ignitable mixture there, and may be wall-guided by a shaped wall, air-guided by the air

10.3 Liquid Fuel Atomization Applications

flow patterns, or spray-guided by the fuel spray patterns. The air- and spray-guided systems are expected to provide further improvements, as wall impact causes a fuel deposition that interferes with the rapid combustion process. 10.3.2.2 Automotive (Car and Truck) Compression-Ignition (CI) Engines Diesel (i.e., CI) engines provide the most demanding injection requirements. First, the spray is into very high-pressure air, due to the diesel engine compression ratios and to the fact that it can only start towards the end of compression. Second, the timing of the injection directly controls the onset of ignition, and so must be precise. The duration of injection is short, as the combustion must take place when the piston is near top dead center, with correspondingly short atomization and mixing times. To provide rapid combustion and low levels of particulate matter (which includes “smoke”), the spray must atomize to a fine degree and be distributed widely within the combustion space with minimum wall impact. Control of the other major diesel engine emission, NOx, requires some flexibility in the ignition timing, which in turn means that it is desirable for the injection to be decoupled from the engine’s rotation. Finally, as diesel engine power is controlled by the fuel flow rate independently of the air flow, the fuelling system must be capable of rapid, accurate, and variable fuel metering. All of these points highlight the need for a very high-pressure injection, and also to limitations on the use of mechanical plunger-type pumps driven directly from the engine crankshaft. The early diesel engine injectors operated at approximately 30 MPa pressure, with large nozzle areas to allow a sufficient fuel flow. The divided combustion chamber types on small- to medium-sized engines (called indirect injection) used pintle nozzles, and relied on turbulence created in the secondary chamber to promote atomization. However, modern engines are predominantly single chamber (direct injection), and primary atomization is more important. Developments from the middle of the twentieth century led to injection pressures being increased to about 70 MPa for distributor pumps (single plunger types with a rotating “distributor” to feed each cylinder), and up to 110 MPa for in-line pumps, where each cylinder had its individual plunger. In very large engines, such as stationary or marine types, these mechanically driven pumps were often located at each injector and were known as “unit injectors.” They were capable of operating at injector pressures in excess of 200 MPa, but were too cumbersome for medium or small engines. Most car and truck engines therefore used the separated in-line pump, with high-pressure lines from it to the injector. During the last quarter of the twentieth century, the need to improve efficiency and power output for diesel engines became apparent, while legislation required that exhaust smoke and NOx be limited. Thus, new types of injectors with flexible timing were developed which could operate at high pressures [26]. The first of these were termed electronic unit injectors (EUIs), and used an escape line that was opened and closed by a solenoid for the fuel in the volume below the mechanically operated plunger. These injectors usually operate at about 160 MPa, but pressures above 200 MPa have been demonstrated. Unfortunately, the problem of their large bulk persisted, which made them less acceptable.

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The problem was solved by the introduction of hydraulically actuated injectors called a high-pressure common rail (HPCR), where the fuel was brought to a high pressure within a common rail that directly fed the solenoid-controlled injectors. Although these injectors usually operated at between 140 and 180 MPa, advances in the use of piezoelectric stacks to replace solenoids meant that pressures in excess of 200 MPa could now be achieved. These types of injector are currently used widely in automotive and truck applications. 10.3.2.3 Aircraft Gas Turbines The earliest types of injector nozzle were designated as “Simplex”; that is, they had a single nozzle hole surrounded by a concentric air vent, so as to prevent carbon buildup. In order to cope with the huge range of fuel delivery rates required to match the air flow and power variations, these injectors required a pressure ratio that varied enormously. However, to obtain good atomization, the minimum pressure ratio had to be high, which in turn pushed the maximum pressure ratio to unreasonable values. The solution to the problem was the “Duplex” nozzle; this had an additional annular nozzle around the central nozzle, that could be brought into action when necessary to provide greater fuel flow rates. This was later replaced by the “air-blast atomizer,” which used a very low-pressure fuel delivery into a very high-velocity air stream fed transversely across the nozzle. In this way, any increase in the pressure ratio so as to allow a higher fuel flow rate did not present a problem. Fuel atomization was achieved by the very high shear between the high-velocity air stream and the low-velocity fuel stream, rather than the reverse which is typical of most other injectors. Today, air-blast atomizers are the most widely used atomizers in aero-propulsion applications. Following the introduction of air-blast atomizers, the design of the atomizing system became an integral component in the design of the whole combustor. The atomizer is usually composed of an air swirl premixing duct incorporated into the liner and a fuel injector (typically of the simple or swirl type), mounted in the premixer. The requirements for lower pollutant emissions increased this trend, and much of the ongoing research aimed at developing ultra-low-emission engines relies on the realization of efficient atomizing and premixing systems. The electronic control of injectors can be useful for suppressing any unwanted dynamic effects in the combustors, and may also help to reduce combustion noise and instabilities [27].

10.4 Outlook on Innovative Atomization Techniques

Although, in the previous section the details of current injectors and injection systems were presented, it might be useful to consider some new concepts and devices that, although still in the development stage, may be regarded as new concepts and methodologies aimed at improving atomization quality. More importantly, they might also help in complying with the more stringent future pollutant emission regulations that are being applied to propulsion systems. Fulfilling the emission limits of a stationary, ground-based combustion plant does not necessarily require an

10.4 Outlook on Innovative Atomization Techniques

intrinsically clean combustion process, because the after-treatment of exhaust gases, coupled with sophisticated injection systems, can help to achieve such a goal. However, in propulsion systems – and particularly in those of aero-propulsion – the problem is made more challenging by the weight and size limitations of the combustion devices, and also by a need for superior robustness. Close coupling between the fuel atomization stage and some premixing with the combustion air is a common requirement of modern internal combustion engines and gas-turbines. This must occur not only within a very short time (in the order of milliseconds), but also in a small volume chamber. Hence, to achieve a good mixture preparation represents a major challenge that calls for new strategies in the design of atomization systems. Good mixing processes can be developed either by exploiting new configurations of standard atomizing systems, or by operating with different conditions. Both of these approaches require theoretical and experimental investigations to define new concepts and devices for use in the design of innovative atomization systems. The traditional approach to studying an atomization system generally assumes that the different stages in the process of forming an ignitable mixture are composed of elementary subprocesses – fragmentation, secondary break-up, dispersion, evaporation and mixing – which take place sequentially. This is due predominantly to difficulties in evaluating the mutual interactions of the subprocesses, and to their substantial independence in many practical systems. As a consequence, the designers have in many cases aimed at decoupling these different stages in an attempt to obtain more controllable and more easily scalable devices. Unfortunately, this approach is largely inappropriate for describing the complete process when the characteristic times of the subprocesses involved become comparable, as occurs in many modern combustion systems. At that point, it is better to exploit the inherent complexity of the interacting processes, so as to achieve an improved performance. 10.4.1 Atomizing/Premixing Systems Based on Cross-Flow Injection

Improving the premixing of the fuel with air prior to its ignition has become a very popular strategy for reducing pollutant emission and increasing system performance. In addition, stability problems must be avoided by ensuring a good, spatial uniformity of the mixture. Finally, in poorly accessible combustion systems, such as those in gas-turbines, the robustness of the system is a key parameter in determining the system efficacy. Thus, finding technical solutions to these requirements calls for a “rethink” of the global process. Liquid breakup is essentially the result of a local trade-off between the disrupting forces and the capillary restoring action. The latter are, according to the Young–Laplace equation, inversely proportional to the sum of the inverse of the principal radii of curvature at the point considered; that is:   1 1 ð10:6Þ DP ¼ s þ R1 R2

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For a spherical droplet, the two radii are equal and, as a consequence, the capillary forces are essentially 2s R . Incidentally, this is the reason for the intrinsic stability of spherical droplets, because if one of the two radii is significantly larger than the other (as for a cylindrical shape), then the capillary pressure is equal to Rs . However, when considering a jet injected into steady or co-flowing air, the radius of curvature relevant to the process is that relative to the onset of the small perturbations that are responsible for the subsequent liquid detachment and, eventually, of the jet breakup. This increases the relevance of capillary pressure in the balance of forces at the liquid–gas interface. In practical injection systems, the atomization energy can be supplied by either the liquid or the gas phase. This can be roughly identified by considering the ratio of the liquid to the gas velocity. Pressure atomizers are characterized by a very high value of this ratio, while for air-assisted systems the value of the ratio is close to 0. A large proportion of current atomization systems fall between these two extremes (see Figure 10.1). There are some drawbacks in these configurations, however. In the case of low or negligible liquid velocity, aerodynamic interaction may produce good atomization, but very often the droplets so formed will follow the gas flow streamlines as they promptly assume the gas velocity. Hence, a substantial segregation of the liquid diffusion processes will mostly determine the mixing of the fuel with air, unless very complex air-flow stirring methodologies are realized. On the other hand, in the case where the fuel velocity prevails, the quality of atomization is heavily dependent on the liquid flow rate. As noted above, this produces poor atomization and mixing at lower fuel flow rates, but the use of higher liquid velocities to achieve an acceptable atomization quality may produce overpenetration in the

Figure 10.1 Classification of atomizing systems based on liquid to gas velocity ratio and dependence of pressure jump at the interface.

10.4 Outlook on Innovative Atomization Techniques

Figure 10.2 Example of jets in crossflow.

premixing duct, with possible impact on the walls or the presence of liquid in the burning region. A possible alternative approach would be to exploit the conditions of gas and liquid velocities of the same order of magnitude. Indeed, this is the case for fuel jets injected transversely into a gas flow when a value of the ratio of the moments of the two phases, q ¼ ðrl v2l Þ=ðrg v2g Þ, is of the order of a few deciles. Typical examples of jets in a crossflow are reported in Figure 10.2, where the jet has been deflected due to the intense drag induced by the airflow, and its cross-section is deformed. On the leeward side, a cloud of droplets is formed with characteristics which depend on the stripping mechanism occurring at the liquid interface. It has been determined [28] that at moderate Weber numbers (computed using the gas velocity), the dominant breakup modes are bag and multimode breakup (Figure 10.2a), which are connected to Rayleigh–Taylor (R-T) instabilities excited by interface acceleration. At higher Weber numbers, “shear” or “stripping” breakup mainly connected to Kelvin–Helmholtz (K-H) instabilities [29] appear, and these become progressively dominant as the Weber number increases. Due to the high level of energy available for surface growth it can be assumed that, at the exit of the nozzle, a wide spectrum of wave frequencies is excited. The peculiar feature of crossflow injection is that both shear (K-H) and acceleration (R-T) instabilities are effective because of the simultaneous presence of aerodynamically enhanced tangential stresses and normal dynamic pressure. At high-energy injection conditions, high-frequency low-amplitude waves are excited faster (i.e., closer to the injection point), probably as a result of K-H instabilities developed in the shearinduced boundary layer. Low-frequency high-amplitude oscillations, connected to acceleration R-T instabilities, need more time to develop. Progressive erosion by stripping, along with jet distortion and coarse ligament detachment, causes the jet to reach a critical cross-section area, where capillary forces succeed in breaking up the continuous liquid jet. The exact occurrence and placement of the column fracture is nondeterministic and unpredictable but, at least on a statistical basis, a breakup parameter can be defined and investigated [30]. Nevertheless, a quite complete characterization of the bent profile of ensembleaveraged shadowgraphs of the jet in terms of semi-empirical interpolating correlations is available for a broad range of external parameters [31]. This model is able to provide a realistic representation of the upstream profile of the liquid jet in a spatial

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domain that is limited along the curvilinear profile by the breakdown position, where the coherence of the liquid jet is lost [31]. According to the result of the model, penetration in the airflow of the upstream profile of the jet is of the type, q0:4 D0:66 [31], where D is the orifice diameter. By means of a statistical elaboration of the shadowgraph images used to derive the previous model, it has been found that the spray opening average angle is proportional to q0:3 Wel0:3 D1:3 [32]. In a practical system such as a gas turbine, an increase in the fuel flow rate (i.e., of liquid velocity) corresponds to an increase in the air velocity. Because the ratio of liquid to air mass flow rate is essentially fixed, this implies that the change in the q-value due to an increase in liquid flow rate (and of the consequent change in air flow rate) is proportional to vl =vg . In general, this ratio is appreciably lower than unity, as air density increases with increasing compressor speed. From previously reported dependencies, an increase in the liquid flow rate produces only a minor increase in jet deflection, and a moderate widening of the jet with a stronger stripping from its surface. In other words, jet placement is not severely affected by the change in operating conditions (as it is fixed by the air/fuel ratio and the nozzle geometry), and atomization of the liquid is promoted at a higher fuel mass flow rate. Hence, impact on the walls is not favored and the sizes of the droplets will be expected to become smaller at higher liquid flow rates. This allows a wide operating range to be obtained in which the essential characteristics of dispersion of the fuel, evaporation and mixing in the airflow does not change significantly. The main feature of this configuration is the progressive distribution of the liquid in the airflow, as realized by the continuous stripping of liquid from the interface. On the other hand, the presence of a liquid jet allows the extension of the droplet injection region at a spatial position relatively distant from the nozzle. By properly tuning the orifice diameter, it is possible to keep a reasonable uniformity of the droplet plume in the whole cross-section of a premixing duct. Finally, the use of simple nozzles such as plain atomizers can be useful in achieving a satisfying level of robustness. 10.4.2 Supersonic Atomization

As noted in Section 10.3.2.2, diesel engine injectors provide the most challenging problems. The current trends are to further improve atomization and distribution of the fuel, while enhancing the ability to control the smoke and NOx emissions and to reduce diesel knock. The latter improvement is made possible by using multiple injections – that is, one or two preliminary injections before the main injection – so as to provide a more progressive burn. An alternative would be to shape the injection delivery curve to provide an even smoother ignition and initial combustion pressure rise, but this would be more difficult to achieve. One or two post injections would also help to burn off any smoke particles during the in-cylinder expansion process. The other avenue would be to include an even higher injection pressure so as to enhance fuel atomization, although this must be considered in association with the

10.4 Outlook on Innovative Atomization Techniques

cylinder size and air swirl patterns within the cylinder. This is because a too-high velocity of a coherent fuel jet can impact on surfaces, thereby providing a wall layer that must be evaporated before combustion. The high-velocity impact of large droplets may also cause erosion damage. The injector factors that influence atomization include: . . . .

Nozzle diameter, the nozzle hole L/d, nozzle hole shape Internal geometric factors that influence the flow into and through the nozzle sac Internal flow velocity (turbulence, cavitation) External jet velocity (aerodynamic breakup)

The advantages of a higher injection pressure are twofold. First, the higher pressure increases the velocity through the nozzle holes, allowing their diameter to be reduced. The smaller the ligament stream issuing from the nozzle, the smaller will be the initial fuel clumps that are formed by the aerodynamic instabilities that ensue from the shear layer interactions between the jet stream and the air. This direct effect can be approximated from a quasi-steady analysis, as determined by Equation 10.7: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi _ f ¼ Cd An 2rf DPn ð10:7Þ m _ f is the mass flow rate, rf is the fuel density, and Cd is the discharge where m coefficient. That is, for a given fuel mass flow rate, fuel and nozzle discharge coefficient, the nozzle diameter can be reduced proportionally to DPn
0:25 . For example, a doubling of the injector pressure will reduce the hole diameter by 16%. Coupled with the use of a larger number of injector holes, higher pressure has been a factor in the improved atomization. The second – and most important – effect is on the fluid dynamics causing the atomization. The higher velocities through the nozzle increase both the turbulence within the injector and the flow separation and perhaps cavitation) as the flow enters the nozzle hole. The latter must be matched to the hole length/diameter (L/d) ratio, so that the separated region does not extend beyond the nozzle and cause “hydraulic flip,” where the spray may change from an increasingly more atomized form to a coherent and directed form. Once the flow emerges from the nozzle, the higher spray velocity proportional to DPn0:25 increases the shear with the surrounding air, which will in turn result in more intense surface (K-H) instabilities. 10.4.2.1 Towards Higher Pressures and Supersonic Injection Velocities The achievement of higher injection pressures in a rapidly repeating, short-duration, common rail process, such as that in a diesel engine, is extremely difficult. A major problem for the HPCR-type engines is that the valve controlling the flow must open very quickly against the very high common rail pressure; this requires a solenoid (or piezo-electric) actuator which produces a higher force and is more difficult to develop. The problem can be overcome by using an amplifying injector, where an

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intermediate rail pressure is first employed and then multiplied within the injector by the action of a stepped piston. These amplifying injectors have been under development since 1993, with the development of the Caterpillar HEUI; this used lubricating oil as the driver for the 7 : 1 area ratio stepped-piston that drove the diesel fuel [33]. Another type which used diesel fuel as both the driver and driven fluid, with a 9 : 1 amplification, has also been developed [34]. To date, testing has been carried out up to 230 MPa injection pressure in an engine, and up to 260 MPa on a bench facility. Under these pressures, the injection velocity is very high, with theoretical spray velocities ranging from about 450 m s
1 at 200 MPa to 550 m s
1 at 300 MPa. Under diesel engine conditions, the air in the combustion chamber at ignition is at least 700 K and with an acoustic velocity of about 530 m s
1, while the emerging spray is in the high subsonic range. Indeed, it would become supersonic with only a small further increase in pressure. A liquid jet injected at high pressure into air drives a wave ahead of it, in similar fashion – although not identical to – the wave formation ahead of a solid body such as a high-velocity aeroplane. In this situation, when the body is subsonic, such a wave motion progressively builds up pressure over a finite distance, the compression being substantially isentropic. Then, when the body reaches the sonic velocity, a leadingedge shock wave begins to be formed. With a further velocity increase, a detached shock stands off from the body and moves at the body’s velocity. On the stagnation streamline, this provides a nonisentropic pressure rise, followed by a further isentropic compression to the front of the projectile. As the projectile velocity is increased further, the detached shock becomes stronger and the stand-off distance shortens, until it becomes attached to the body. Any further increase changes it to an oblique conical shock from the body leading edge. This final transition is enhanced by the shape of the body, with a pointed front aiding the formation of a shock cone. The shock wave results in a higher gas temperature due to the irreversibility of the process, this being progressively more pronounced as the shock system becomes stronger. A liquid jet is likely to have some similarities and some differences to the above situation. First, a liquid jet will begin to disperse and evaporate as it is injected, thus modifying the wave structure; this will also cause an attenuation of the jet as the leading edge progresses. Second, the liquid leading edge body profile can be altered by the wave itself with an interactive feedback to the shock detachment. Third, an injected liquid rapidly brought up to a high pressure in a preinjection volume or “sac” must have an internal wave motion within the liquid which, on reflection and rereflection, will cause pulsations in the jet delivery. This is more pronounced as the injection pressure becomes higher. The higher temperatures but slightly lower pressures resulting from shock, as compared to isentropic compression, will shorten the diesel ignition delay. This can be estimated from a conventional Wolfer correlation. An estimation indicates that a jet at Mach ¼ 1.2 will lower the ignition delay by a small 0.5% below that expected by isentropic compression alone, but at Mach ¼ 1.6 the reduction is 4%. Additional

10.4 Outlook on Innovative Atomization Techniques

shocks that exist at the side of the spray due to pressure pulsing within the nozzle sac help to eject clumps of fuel to initiate the breakup. The faster jet velocity will also increase fuel atomization via the normal stripping mechanisms, while the higher temperatures due to the shock entropy increase should enhance the combustion process. It has even been suggested that a fuel jet injected at a very high supersonic velocity into ambient air may autoignite [35]. 10.4.2.1.1 Supersonic Liquid Jets In order to examine the possibilities for supersonic diesel fuel injection, a cooperative investigation has been carried out at the University of New South Wales, Australia and the Shock Wave Laboratory, Tohoku University, Japan. This requires injection pressures above about 300 MPa. These fundamental studies used injection durations similar to those in diesel engines, but were single-shot rather than repetitive, so that the injection could range from low supersonic to extreme velocities of over 2000 m s
1, the latter being to study the limits of injection pressure increase. The fuel jets were created by projectile impact from either a powder gun [36] or a two-stage light gas gun [37]. Flow visualization techniques were used [38], the pressures inside the nozzle sac were determined from both measurements, and a theory was developed for that purpose [39]. Simulation studies using the Fluent [40] and Autodyne codes [41] were implemented. Studies examined the shock patterns that were formed, the shape and pulsation of the jets, the effect of nozzle shape, the jet attenuation and penetration, the possibility of autoignition at low temperature, and the effect of different fuels [42]. Comparative shadowgraph pictures of diesel fuel injected into ambient air at a low and high subsonic velocity are shown in Figures 10.3 and 10.4, respectively. The former set-up has a detached shock and, at 600 m s
1, a possible practical application. The latter set-up, at 1800 m s
1, was used to explore the limits of the injection process and, whilst the velocity was probably too high for most current uses, it showed that the shock with reshaped jet leading edge had become attached. Secondary shocks appeared on the sides of the spray; these (which are clearly seen in Figure 10.4) can be distinguished as stemming from the pressure pulsing within the nozzle

Figure 10.3 Low-Mach number supersonic diesel jets at 600 m s
1.

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Figure 10.4 High-Mach number supersonic diesel jets at 1800 m s
1. The pulsing behavior and secondary shocks are evident.

caused by the rapid application of the driving mechanism, in conformity with the theory [39]. In this situation, consecutive pulses drive new flow with associated shock waves down the attenuating spray, and these overtake the original flow. The attenuation profile is quite similar to that determined from conventional formulae [43]. Initially, the velocity drops quite rapidly. For a 1800 m s
1 jet, the initial decline is almost linear and the velocity has decreased to about one-third of its initial value in about 0.4 ms; this corresponds to a penetration distance of about 900 mm. Using these data for further predictions indicates that the 600 m s
1 jet would attenuate to 200 m s
1 in 0.4 ms, at which stage it would have penetrated about 300 mm. As these experiments were carried out in ambient air with density 1.2 kg m
3, in a diesel engine where the air density is greater the 600 m s
1 jet would attenuate a little more rapidly. Further measurements at high pressure are required to verify these findings. Tests for autoignition were carried out in ambient pressure air at temperatures ranging from 300 to 373 K, and with fuels ranging from a typical diesel with cetane number of about 50 and diesel–cetane blends giving increasing cetane numbers up to that of pure cetane (cetane number 100). Autoignition was not achieved, although the tests did indicate that complete fuel vaporization was rapidly obtained with the 1800 m s
1 jet. This suggests earlier assumptions that the supersonic jet unaided by a high air temperature would autoignite were dubious, but that combustion would be enhanced. Further studies are required on this aspect.

References

10.5 Summary

This chapter has introduced the basic concepts of liquid atomization, and provided an overview of the dependence of the relevant processes on combustion system parameters and liquid properties. The main implications of theory on the practical aspects of atomization have been reviewed and discussed, and a conceptual classification of atomizers used in combustion systems was presented. Practical atomization techniques and devices were presented related to the most important applications for energy production and propulsion. Finally, a brief resume was provided of selected innovative atomization techniques, with particular emphasis on high-pressure combustion systems used in propulsion applications.

References 1 US Department of Energy: Energy

2 3 4 5

6

7

8 9

10

11

Information Administration (2007) Annual Energy Review 2006, DOE/EIA0384-2006. US Department of Energy: Energy Information Administration, Washington, DC. Rayleigh, L. (1878) On the instability of jets. P. Lond. Math. Soc., 1, 4. Lefebvre, A.H. (1989) Atomization and Sprays, Hemisphere. Bayvel, L. and Orzechowski, Z. (1993) Liquid Atomization, Taylor & Francis. Nasr, G.G., Yule, A.J., and Bendig, L. (2002) Industrial Sprays and Atomization: Design, Analysis and Applications, Springer. Tropea, C., Yarin, A.L., and Foss, J.F. (2007) Handbook of Experimental Fluid Dynamics, Springer-Verlag, Berlin, Heidelberg. Sirignano, W. (1999) Fluid Dynamics and Transport of Droplets and Sprays, Cambridge University Press. Stiesch, G. (2003) Modeling Engine Spray and Combustion Processes, Springer. Schmidt, D. (2006) Direct simulation of primary atomization, Combustion Processes in Propulsion: Control, Noise and Pulse Detonation (ed. G.D. Roy), Elsevier Butterworth-Heinemann, Oxford UK. Scardovelli, R. and Zaleski, S. (1999) Direct numerical simulation of freesurface and interfacial flow. Annu. Rev. Fluid Mech., 31, 567–603. Kolmogorov, A.N. (1941) On the lognormal distribution of particles sizes

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14 15 16

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during break-up process. Dokl. Akad. Nauk SSSR, 31, 99–101. Babinsky, E. and Sojka, P.E. (2002) Modeling drop size distributions. Prog. Energy Combust. Sci., 28, 303–329. Gorokhovski, M. and Herrmann, M. (2008) Modeling primary atomization. Annu. Rev. Fluid Mech., 40, 343–366. Villermaux, E. (2007) Fragmentation. Annu. Rev. Fluid Mech., 39, 419–446. Lin, S. (2003) Breakup of Liquid Sheets and Jets, Cambridge University Press. Sirignano, W.A. and Mehring, C. (2000) Review of theory of distortion and disintegration of liquid streams. Prog. Energy Combust. Sci., 26, 609–655. Wang, Y., Im, K., and Fezzaa, K. (2008) Similarity between the primary and secondary air-assisted liquid jet breakup mechanisms. Phys. Rev. Lett., 100, 154–502. Feynman, R.P., Leighton, R.B., Sands, M., and Hafner, E.M. (1965) The Feynman Lectures on Physics; vol. I. Am. J. Phys., 33, 750. Lasheras, J.C. and Hopfinger, E.J. (2000) Liquid jet instability and atomization in a coaxial gas stream. Annu. Rev. Fluid Mech., 32, 275–308. Lee, C. and Reitz, R. (2000) An experimental study of the effect of gas density on the distortion and breakup mechanism of drops in high speed gas stream. Int. J. Multiphase Flow, 26, 229–244.

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21 Umemura, A. and Wakashima, Y. (2002)

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Atomization regimes of a round liquid jet with near-critical mixing surface at high pressure. Proc. Combust. Inst., 29, 633–640. Pilch, M. and Erdman, C.A. (1987) Use of breakup time data and velocity history data to predict the maximum size of stable fragments for acceleration-induced breakup of a liquid drop. Int. J. Multiphase Flow, 13, 741–757. Joseph, D.D., Belanger, J., and Beavers, G.S. (1999) Breakup of a liquid drop suddenly exposed to a high-speed airstream. Int. J. Multiphase Flow, 25, 1263–1303. Beale, J. and Reitz, R. (1999) Modeling spray atomization with the KelvinHelmholtz/Rayleigh-Taylor hybrid model. Atomization Sprays, 9, 623–650. Ruger, M., Hohmann, S., Sommerfeld, M., and Kohnen, G. (2000) Euler/La Grange calculations of turbulent sprays: the effect of droplet collisions and coalescence. Atomization Sprays, 10, 47–82. Charlton, S.J. (1998) Chapter 11, Handbook of Air Pollution from Internal Combustion Engines (ed. E. Sher), Academic Press, London. Lefebvre, A. (2000) Fifty years of gas turbine fuel injection. Atomization Sprays, 10, 251–276. Mazallon, J., Dai, Z., and Faeth, G. (1999) Primary breakup of nonturbulent round liquid jets in gas crossflows. Atomization Sprays, 9, 291–312. Ranger, A. and Nicholls, J. (1969) Aerodynamic shattering of liquid drops. AIAA J., 7, 285–290. Ragucci, R., Bellofiore, A., and Cavaliere, A. (2007) Trajectory and momentum coherence breakdown of a liquid jet in high-density air cross-flow. Atomization Sprays, 17, 47. Ragucci, R., Bellofiore, A., and Cavaliere, A. (2007) Breakup and breakdown of bent kerosene jets in gas turbine conditions. Proc. Combust. Inst., 31, 2231–2238. Bellofiore, A., Ragucci, R., Di Martino, P., and Cavaliere, A. (2006) Morphology and fluctuations of a liquid jet in high-density air crossflow. International Conference on

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Liquid Atomization and Spray Systems (ICLASS) 2006, Kyoto, Japan. Glassey, S.F., Stockner, A.R., and Flinn, M.A. (1993) HEUI - A new direction for diesel engine fuel systems, SAE-Paper 930270. Milton, B., Yudanov, S., Casey, R., and Behnia, M. (2004) Smoke and NOX diesel engine emission tests of a single fluid HEUI injection system. FISITA 2004 World Automotive Congress, Barcelona, Spain, p. F2004V2190. Field, J.E. and Lesser, M.B. (1977) On the mechanics of high speed liquid jets. Proc. R. Soc. Lond. A, 357, 143–162. Pianthong, K., Zakrzewski, S., Behnia, M., and Milton, B. (2002) Supersonic liquid jets: Their generation and shock wave characteristics. Shock Waves, 11, 457–466. Pianthong, K., Takayama, K., Milton, B., and Behnia, M. (2005) Multiple pulsed hypersonic liquid diesel fuel jets driven by projectile impact. Shock Waves, 14, 73–82. Milton, B. and Pianthong, K. (2005) Pulsed, supersonic fuel jets—A review of their characteristics and potential for fuel injection. Int. J. Heat Fluid Flow, 26, 656–671. Pianthong, K., Milton, B., and Behnia, M. (2003) Generation and shock wave characteristics of unsteady pulsed supersonic liquid jets. Atomization Sprays, 13, 475–498. Zakrzewski, S., Milton, B., Pianthong, K., and Behnia, M. (2004) Supersonic liquid fuel jets injected into quiescent air. Int. J. Heat Fluid Flow, 25, 833–840. Pianthong, K., Matthujak, A., Takayama, K., Saito, T., and Milton, B. (2006) Visualization of supersonic liquid fuel jets. J. Flow Visualization Image Processing, 13, 217. Pianthong, K., Matthujak, A., Takayama, K., Milton, B., and Behnia, M. (2008) Dynamic characteristics of pulsed supersonic fuel sprays. Shock Waves, 18, 1–10. Hiroyasu, H. (1994) Fundamental spray combustion mechanism and structures of fuel sprays in diesel engines, Mechanics and Combustion of Droplets and Sprays (ed. C. Chigier), Begell House, New York, pp. 291–306.

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11 Light Emission from Flames Stephen A. Ciatti

11.1 Introduction

Ever since prehistoric humans first discovered the ability to create fire, societies throughout the globe have utilized this most basic combustion reaction to generate heat and light. Warmth, cooking, illumination and protection are but a few of the basic tasks that fire can accomplish. Specifically, when used for lighting purposes, a variety of devices have been developed to provide a powerful tool, with candles, torches, oil lamps and related products having provided the required light for millennia (see Chapter 1). In engineering and scientific applications, it is useful to explore exactly what combustion is, and why these reactions emit radiation. Fire, or combustion, is a chemical reaction that requires heat, fuel, and an oxidizer (see Chapter 2). When combustion reactions occur, they generally are highly exothermic and create high temperatures in the reaction zone. With this high degree of thermal energy, the radiation emitted from the reaction can be characterized in several different ways. The easiest and most common type of radiation emitted from combustion is luminosity. Many traditional flames emit a significant amount of luminosity, such as solid combustion from wood or coal, candle combustion from oil or wax, and diesel combustion inside an engine. These types of flames are known as “mixingcontrolled” or “diffusion” flames, because the fuel and oxidizer reaction is controlled by the mixing of one with the other. In mixing-controlled combustion, soot is created in the rich regions of the flame, and it is this soot which, when heated by the reaction zone, emits radiation in the form of incandescence as a result of the heating (see Vol. 3 Ch. 14). Soot behaves similar to a black-body, and the equations governing this radiation are based upon black-body radiation. The characterization of soot luminosity represents one of the most powerful approaches available for investigating combustion. Premixed flames, which do not create large amounts of soot, require different measuring principles. Instead of soot becoming so hot in the reaction zone that it radiates, there is a need to rely on different physical processes to characterize the light

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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emission. Chemiluminescence, or the luminescence created by chemical reactions, can be used to characterize many types of light emission from flames. However, the intensity of chemiluminescence is roughly three orders of magnitude lower than soot radiation, for example, in a diesel flame [1]. The soot radiation, as a broadband emitter, tends to overwhelm any chemiluminescence in a sooting flame. For flames with little or no soot radiation, the chemiluminescence portion of the light emission becomes dominant, and can then be characterized and analyzed. Hydrogen flames and premixed flames tend to fall in this category. Utilizing the light emitted from flames is called “passive imaging”; this means that the light emission occurs as part of the combustion event itself, without other light sources being brought into the measurement area. For example, lasers or stroboscopic lights are not required to make measurements with naturally occurring lightemission techniques. Techniques such as laser-induced fluorescence (LIF; see Vol. 2 Ch. 7), laser-induced incandescence (LII; see Vol. 2, Ch. 15) and Schlieren systems all utilize external light sources. When studying difficult-to-access devices, such as internal combustion engines, very often the main difficulty is first to get light into the measurement area, and then to get it out again. When using passive imaging techniques, a reliance is placed on the combustion event to provide the light; the task then merely becomes one of extracting the light from the measurement area. The level of required access is considerably reduced, which makes it much easier to study internal combustion engines and similar devices.

11.2 Theory

One of the most popular uses of soot radiation in combustion analysis is that of soot luminosity to estimate combustion temperatures. The technique of two-color optical pyrometry was first developed in the early 1930s by Hottel and Broughton [2], who applied the technique to the study of sooting flames. The technique was further refined by Uyehara and Myers from the University of Wisconsin in 1946 and 1947, to study luminous flames in diesel engines [3]. Estimates for combustion temperature can also be acquired using chemiluminescence and molecular emission spectra [4]. Several chemists, including Gaydon and others, have used this type of technique to characterize flames for decades. Instead of characterizing soot radiation, emission spectroscopy uses quantum physics and the storage of molecular energy to predict the quantized energy storage and emission characteristics of a known, useful molecule – such as OH or CH. 11.2.1 Soot Incandescence or Soot Radiation

When considering the case of two-color pyrometry using soot radiation, Figure 11.1 shows the typical emission spectra for black-body radiation. As the temperature of a black-body increases, the peak intensity of the emission increases while the

11.2 Theory

109 Visible spectral region 5800 K

107 106 105

4000 K 3500 K

2500 K 2000 K 1500 K

3000 K

,b

Spectral Emissive Power, E , W/m2 - m

108

104 103 102 101 100 10-1 10-2 10-3 0.1

1

10

Wavelength, , m Figure 11.1 Log spectral emissive power versus log wavelength for black-body radiation.

wavelength of peak emission intensity decreases. Also seen in this figure is the changing ratio of light intensities based upon wavelength. For example, at 1500 K the intensity of black-body emission at the red wavelengths (700 nm) is three orders of magnitude higher than the intensity emitted at the blue wavelength (400 nm). However, at 5800 K the intensities of the red and blue emissions are almost equal. If soot were a pure black-body, then all that would need to be measured is the light intensity at one wavelength, and this could then be compared to a chart, as shown in Figure 11.1 and Figure 11.2. However, soot is not a perfect black-body radiation emitter, and it is therefore necessary to account for the deviation from pure black-body behavior. In Figure 11.1, the solid lines represent pure black-body behavior at various temperatures, while the dashed line represents a spectral sweep of actual soot radiation intensity at 3000 K as a function of wavelength. Note that if only the green wavelength (500 nm) were used, the temperature estimate would be 2900 K, whereas if the red wavelength (640 nm) were used the temperature estimate would be 2824 K. However, if these temperatures are defined as “apparent” temperatures that have a spectral dependence, then radiation theory can be applied to provide a better soot temperature estimate. As the soot absorption coefficient (K) and optical pathlength (L) are not wavelength-dependent, the product KL factor is constant for all wavelengths of light. Using radiation theory, and the assumption that the KL factor is not spectrally dependent, the “apparent” temperatures measured in Figure 11.1 can be converted into a “true” temperature of the soot, solving two equations and two

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3.5x106

,b

Spectral Emissive Power, E , W/m2 - m

3.0x106 2.5x106 2.0x106 1.5x106

Visible spectral region 3000 K Solid curves represent spectral radiation distributions from a blackbody at the given temperature. Dotted curve indicates radiation from a non-blackbody at 3000 K

2900 K 2824 K

1.0x106 Ta = T

5.0x105

2

0.0

1 2

-5.0x105 0.0

0.1

0.2

0.3

0.4

0.5

Ta = T

= 0.50

1

= 0.64

0.6

blackbody @ 2824 K

0.7

blackbody @ 2900 K

0.8

0.9

1.0

Wavelength, , m Figure 11.2 Spectral emissive power versus wavelength for visible wavelengths and typical combustion temperatures.

unknowns. The “true” temperature of the soot can then be used to back-calculate the KL factor, providing a measure of soot concentration. Qualitatively speaking, the temperature calculation is more robust than the KL factor calculation, as it relies on a ratio of light intensities at two different wavelengths to provide the “true” temperature. If the light intensities were altered due to window fouling by soot, the fouling would tend to attenuate both wavelengths equally, and the ratio of light intensity would be relatively unchanged. However, the calculated soot concentration depends upon absolute light intensity, which means that any factor influencing the absolute measurement of light intensity (window fouling, reflections, transmissivity of the windows, optical fibers, spectral dependence of the camera, etc.) would greatly affect the soot concentration estimate. When used in a diesel engine environment, this technique allows for a qualitative assessment of the combustion temperature, and soot production of the diesel flame. Factors that influence soot temperature and concentration, such as the nozzle configuration (or other injector characteristics), fuel chemistry, along with engine speed and load, can be measured relative to their influence upon soot and NOx production. The equations that govern two-color optical pyrometry are shown as [2]:
KL el ¼ 1
eð la Þ

ð11:1Þ

11.2 Theory

where e ¼ light intensity (W m
2) KL ¼ soot concentration l ¼ wavelength of light (m) a ¼ 1.39 (spectral range constant). The spectral emissive power (e) is a function of soot concentration (KL), the light wavelength (l) and the spectral range constant (a). KL is composed of K, which is an absorption coefficient proportional to the number density of soot particles, and L, which is the thickness of the flame along the axis of the detection device [5]. Solving for KL in the above equation for each wavelength:     C2 1 1 ð11:2Þ KL ¼
la ln 1
exp

l Ta T where C2 ¼ empirical constant Ta ¼ apparent temperature (K) T ¼ true temperature (K) Setting the KL factor equal for both wavelengths, and solving for true temperature (T ):    la1 1    la2 2 C2 1 1 C2 1 1 1
exp
¼ 1
exp

l1 Ta1 T l2 Ta2 T

ð11:3Þ

where Ta1 ¼ apparent temperature at wavelength 1 (K) Ta2 ¼ apparent temperature at wavelength 2 (K) It is then possible to back-substitute T into the KL equation to solve for soot concentration. 11.2.2 Electron-Shift Emission of Photons

There are several ways in which a molecule can emit photons. The fundamental concept is related to the fact that molecules absorb, store, and emit energy in a variety of ways. Molecules can experience excited energy states by raising their temperature, absorbing photons, undergoing chemical reactions, and by other mechanisms. Molecules can only store as much energy as quantum physics allows them to store, and in quantized amounts. In order to maintain their energy equilibrium, molecules will move electrons from one shell to another. Consequently, when a molecule absorbs energy they will often move electrons from a low-energy state to a highenergy state to achieve this. When the energy is removed from the molecule, this allows the electron to fall back into a lower-energy state, and when this happens a photon is emitted as the energy is released from the molecule falling back to a lowerenergy state. Photons emitted by this mechanism are termed “electron-shift photons”.

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One method of producing electron-shift photons is that of chemiluminescence; this is the radiation emission due to a chemical, whereby excited atomic or molecular species are produced during the chemical reaction, or enter excited states by absorbing the energy released during certain reactions. These excited species emit photons at discrete wavelengths as they move from higher excited energy states to lower energy states, based upon their electronic, vibrational, and rotational energy level structures. For example, a standard engine emissions measurement bench utilizes a chemiluminescence analyzer to measure nitrogen oxide (NO). The NO is reacted with ozone (O3) to produce an excited state of nitrogen dioxide (NO2). This pathway is described in Equation 11.4 [6]. NO þ O3 YNO2 þ O2 YNO2 þ O2 þ hu

ð11:4Þ

As shown in this equation, the reaction of NO with O3 produces NO2, O2, and a quantized photon (hu). Temperature is not part of this process, which means that the chemical reaction provides the input energy to the photon emission. In fact, chemiluminescence has been called “cool light,” as it does not require a high temperature to occur. Chemiluminescence signals are very useful as indicators of where, how many, and how rapidly the chemical reactions are occurring. In Equation 11.4, the oxidation of NO to NO2 via the ozone pathway occurs at a fairly low temperature. This principle is used extensively in the fabrication of NO analyzers for engine exhausts, which are effective at detecting the presence of NO on the ppb level. Several non-combustion-related reactions produce chemiluminescence, some of which are even biological in nature! For example, a firefly utilizes chemiluminescence to attract other fireflies, while many species of jellyfish and certain types of plant utilize chemiluminescence for a variety of reasons. In fact, so many examples are found in Nature that this characteristic is referred to as “bioluminescence”. In addition to chemiluminescence, there are also other mechanisms that can provide the necessary input energy to force a molecule into an excited energy state; one of the most common is high temperature. The molecule will store energy in various quantized ways, such as electronic (moving electrons from one shell to another), vibrational (the atoms in the molecule will harmonically oscillate as though they were connected by a system of springs), and rotational (the molecule will rotate at a quantized speed based upon the angular momentum of the particular molecule). The probability of a molecular species occupying a rotational energy state is temperature-dependent, which manifests into temperature-dependent variation of intensities of the rotational emission spectral lines in a given electronic-vibrational band spectrum. This temperature dependence of the rotational emission line intensities can be utilized to calculate gas temperature [4]. Emission spectroscopy is the process by which the emitted light from a molecule or atom is quantified and characterized. Since molecules can store energy only in specified, quantized amounts, the spectral emission of an excited molecule is similar to a “fingerprint” of the energy state of that molecule. As the rotational and vibrational energy bands of the molecule will

11.2 Theory

Normalized Intensity (a. u.)

1.0

0.8

= 0.65 Spark position: TDC Gate-width: 10 ms Trig: 20° BTDC Accumulations: 10

0.6

0.4

0.2

0.0 200

300

400

500

600

700

800

900

1000

1100

Wavelength (nm) Figure 11.3 Spectral distribution of light intensity for hydrogen combustion [4].

change as the temperature of the molecule changes, the appearance of an emission spectrum of a known molecule can be predicted with some degree of certainty, based on prior knowledge of quantum mechanics and molecular theory [7]. An example of the spectral distribution of light intensity for hydrogen combustion in an engine is shown in Figure 11.3. Here, there is a significant peak in the UV spectrum near 310 nm for OH chemiluminescence, while other peaks in the 700 nm and infrared (IR) spectra signify water vapor emission bands. This technique was applied to the OH molecule for this example. The UV spectral bands of OH emissions have been thoroughly measured and analyzed in a study by Dieke and Crosswhite [8]. There are four main band heads, R1, R2, Q1, and Q2 at 306.537 nm, 306.776 nm, 307.844 nm and 308.986 nm, respectively, corresponding to the molecular transitions (A2S, n ¼ 0 ! X2P, n0 ¼ 0) in the 306–310 nm wavelength region [6–8]. The relative intensities of unresolved groups of lines designated as G0, G1, and Gref, can be utilized to extract the gas temperature [7]. Molecular spectra have been used widely for temperature measurements whenever direct methods are not feasible, and especially with flame temperatures in the range of 2000 to 8000 K. The OH UV spectra form a excellent basis for estimating gas temperatures in a hydrogen combustion system, as very little or no interference from other light emissions is present in this spectral regime. This is unlike the spectral emissions from a hydrocarbon flame, where carbon-based radical species may interfere with the OH signal. It is possible to calculate the temperatures using the absolute intensities of the spectral lines and spontaneous transition probabilities. However, the experimental determination of absolute intensities is rather difficult, and the calculations become somewhat involved. A more rigorous use of this application for calculating temperature is described elsewhere [4, 7].

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11.3 Applications in Research

With regards to research applications, there are many ways in which the physical characteristics of light emission from flames may be used. Today, with so much attention being devoted towards improving vehicle efficiencies and reducing toxic emissions, an understanding of the combustion process in engines represents an excellent application for passive optical techniques. The endoscopic access into the combustion chamber of an internal combustion engine is shown schematically in Figure 11.4. The endoscope is fitted inside a sleeve/ window system so as to keep it properly cooled and protected from the hot combustion products. Although this technique produces only a line-of-sight measurement into the combustion chamber, most of the volume of the combustion

Figure 11.4 Schematic representation of endoscope access into an internal combustion engine combustion chamber [4].

11.3 Applications in Research

chamber is visible and, with modern imaging equipment, the data provided by this approach can be quite good. Often, in research applications, where access to measurement areas can be created and controlled (much more so than in industry), both passive and active (external light source) techniques may be used together to paint a more comprehensive picture of the combustion event. Once an optical access has been created, the correct application of an appropriate measurement technique will allow the combustion to be studied under the most relevant conditions possible. In Figure 11.5, for example, the natural soot luminosity produces several different colors and intensities based upon the temperature and soot concentrations. Here, some of the combustion areas are very bright in intensity (appearing as white), while most of the remaining pixels are significantly dimmer, corresponding to orange or red emissions. Subsequently, the soot temperature and soot volume fraction profiles can be determined after performing the relevant calculations. An example of the application of this technique is shown in Figure 11.6. In this case, Figure 11.6a is the natural soot luminosity image, while Figure 11.6b is the calculated soot temperature profile of the soot luminosity image. Finally, Figure 11.6c is the calculated soot volume fraction based upon the same soot luminosity image. It should be noted how the highest-temperature soot tends to occur where it is expected, close to the external portion of the flame where there is sufficient oxygen for intense combustion. At the same time, the highest soot concentration is also located in an expected region, close to the middle of the diesel jets where the pyrolysis is occurring (see Vol. 2 Ch. 15 and Vol. 3 Ch.14). One major advantage of using combustion imaging is the ability to obtain twodimensional (2-D) information with precise temporal resolution. By acquiring a set of images that encompass the entire duration of combustion, the digital images can be analyzed with software to determine the areas of maximum temperature and soot concentration as a function of crank-angle or time. This allows an accurate examination to be conducted of the fuel–air distribution in the combustion chamber, of the location and time of ignition, and also to track the progress of combustion in both time and space.

Figure 11.5 Typical diesel engine soot incandescence image, viewed through an endoscope.

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Figure 11.6 Soot luminosity image (a), soot temperature (b) and soot volume fraction (c), as calculated using two-color optical pyrometry.

The same approach can be taken by using chemiluminescence or electron-shift photons, as shown in the images of Figures 11.7 and 11.8, which were recorded in a hydrogen research engine using hydrogen direct injection. The injector was located on the left-hand side of the image in Figure 11.7, close to the intake valve. The cut-out

11.3 Applications in Research

Figure 11.7 View inside a hydrogen-fueled automotive engine.

on the piston crown for the injector is also visible, located between the two intake valve cut-outs. As the OH molecule emits radiation at roughly 309 nm, the standard optics designed for visible light will not function in this situation. Likewise, as this wavelength of light is in the near-UV band, standard optic equipment will be inadequate, and all optical equipment for UV imaging must be composed of special materials that allow for the transmission of UV light. Hence, the window, endoscope,

Figure 11.8 Image of OH intensity from hydrogen combustion. The red color corresponds to a high OH intensity, and the blue to a low intensity.

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eyepiece, camera lens and camera pixels or film must all be rated for use with UV radiation. Similar to the soot incandescence technique described above, this type of imaging provides a 2-D view into the combustion chamber, with precise temporal resolution. A false-color UV image of hydrogen combustion in an automotive engine is shown in Figure 11.8, where the red color corresponds to high-intensity OH radiated light at 309 nm, and the blue color to low-OH radiation intensity [17]. The image clearly shows the highest OH intensity in the upper left quadrant of the viewing area. As the injector is also located in this quadrant, it is clear that much of the hydrogen gas had remained in that area when the combustion started. As the combustion event progresses with time, these images can be analyzed for intensity so as to determine local areas of intense, high-temperature combustion, along with areas that have lower-intensity OH emission and where, therefore, the lowertemperature reactions will most likely be occurring [4].

11.4 Outlook

Combustion systems that have diffusion flame characteristics tend to have a light emission that is dominated by soot radiation rather than by rotational emission or chemiluminescence. The light intensity of soot radiation is usually overwhelming by comparison to the intensity created by other processes. As a result, gas temperature measurements using emission spectroscopy or chemiluminescence have been limited to the premixed flame prior to diffusion combustion in diesel engines [9, 10]. Temperature measurements in diesel combustion have traditionally been performed using the soot radiation emission by two-color optical pyrometry. The soot emission spectra are then characterized using radiation theory, and the soot radiation temperature and soot volume fraction extracted [11–16]. Today, however, more advanced combustion systems are utilizing so-called lowtemperature combustion (LTC) regimes in an effort to maintain high efficiency, while producing low soot and low NOx emissions. These combustion systems are known by a variety of names, including homogeneous charge compression ignition (HCCI), modulated-kinetics (M-K) combustion, uniform bulky combustion (UNIBUS), and others. One common characteristic of these approaches is that, without dilution, the reaction rates tend to be extremely high because combustion occurs in the entire volume of the combustion chamber, at the same time. In essence, all of these LTC systems attempt to lower the reaction rate of the combustion event by introducing high levels of diluents, either by exhaust gas recirculation (EGR) or excess nitrogen and carbon dioxide, to reduce the concentration of oxygen in the intake air. In fact, these systems are capable of altering the oxygen concentration from 21% ambient down to 15% and below, depending upon the operating conditions. By controlling the availability of oxygen, the speed of reaction is decreased significantly, and this results in combustion temperatures that are usually well below

References

2000 K. In this temperature range, NOx does not tend to form, while the rich zones tend to be too cool for the pyrolysis of soot to occur [9]; hence, soot radiation is minimal, if present at all. Both, emission spectroscopy and chemiluminescence then become potentially useful techniques for acquiring temperature and combustion information.

11.5 Summary

In this chapter the light emission from flames has been discussed. The development of civilization has relied upon this characteristic of combustion reactions, to allow humans to live in cold climates, to cook food, and to function at night. The intense heat released by combustion reactions produces a significant amount of radiation, both expressed as heat and light. Such radiation can be characterized based on the mechanism by which it was generated, such as soot incandescence or electron-shift photon emission, and chemiluminescence. Depending upon the type of flame being studied, either or both of these characteristics may be observed. In general, for mixing controlled combustion, the use of a soot incandescence approach tends to work well, as this provides much more emissive intensity than does electron-shift photon emission. As a result, fairly accurate measurements of both temperature and soot concentration can be made. The measurement of soot temperatures has significant use when studying many practical devices, such as internal combustion engines, coal combustors, fire suppression, and other uses. However, for premixed flames or hydrogen-fueled combustion soot incandescence is not generally available, and measuring light emission using thermally generated electron-shift photons becomes a practical solution. Several species emit photons when thermally excited, the most popular being OH , CH , and C2 [10]. The photons generated by these electron shifts are quantized, and thus can be measured at very discrete wavelengths. As a consequence, these photon emissions – if measured accurately enough – can provide temperature information of the species through the use of spectroscopy. Applications include premixed flame burners, internal combustion engines, hydrogen-fueled flames, and others. Light emission from flames has considerable practical application to many of the problems that mankind faces today. Consequently, many research groups are using these techniques to improve energy efficiency, to decrease harmful emissions, and to detect the presence of flames in hazardous situations.

References 1 Musculus, M.P.B. (2006) Multiple

simultaneous optical diagnostic imaging of early-injection low-temperature combustion in a heavy-duty engine, SAE 2006-01-0079. Society of Automotive Engineers, Warrenville, PA.

2 Hottel, H.C. and Broughton, F.P. (1932)

Determination of true temperature and total radiation from luminous gas flames. Ind. Eng. Chem. Anal. Edit., 4, 166–175. 3 Uyehara, O.A., Myers, P.S., Watson, K.M., and Wilson, L.A. (1946) Flame

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temperature measurements in internal combustion engines. Trans. ASME, 68 (1), 17–30. Ciatti, S.A., Bihari, B., Wallner, T., and JAUTO399. (2007) Establishing combustion temperature in a hydrogenfuelled engine using spectroscopic measurements. Proc. Inst. Mech. Eng., J. Automobile Eng., 221, 699–712. Zhao, H. and Ladommatos, N. (2001) Engine Combustion Instrumentation and Diagnostics, Society of Automotive Engineers, Warrenville, PA. Gaydon, A.G. (1957) The Spectroscopy of Flames, Chapman & Hall, London. de Izarra, C. (2000) UV OH spectrum used as a molecular pyrometer. J. Phys. D: Appl. Phys., 33, 1697–1704. Diecke, G.H. and Crosswhite, H.M. (1961) J. Quant. Spectrosc. Radiative Transfer, 2, 97–199. Musculus, M.B. (2005) Measurements of the influence of soot radiation on incylinder temperatures and exhaust NOx in a heavy-duty diesel engine, SAE 2005-010925. Society of Automotive Engineers, Warrenville, PA. Antoni, C. and Peters, N. (1997) Cycle resolved emission spectroscopy for IC engines, SAE 97917. Society of Automotive Engineers, Warrenville, PA. Bakenhus, M. and Reitz, R.D. (1999) Twocolor combustion visualization of single and split injections in a single-cylinder heavy duty D.I. diesel engine using an endoscope-based imaging system, SAE

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1999-01-1112. Society of Automotive Engineers, Warrenville, PA. Ciatti, S.A., Blobaum, E.L., and Foster, D.E. (2002) Determination of diesel injector nozzle characteristics using twocolor optical pyrometry, SAE 2002-010746. Society of Automotive Engineers, Warrenville, PA. Ciatti, S.A., Miers, S.A., and Ng, H.K. (2005) Influence of EGR on soot/NOx tradeoff in a light-duty diesel engine, ASME ICEF2005-1327. Matsui, Y., Kamimoto, T., and Matsuoka, S.A. (1979) A study on the time and space resolved measurement of flame temperature and soot concentration in a D.I. diesel engine by the two-color method, SAE 790491. Society of Automotive Engineers, Warrenville, PA. Kobayashi, S., Sakai, T., Nakahira, T., Komori, M., and Tsujimura, K. (1992) Measurement of flame temperature distribution in D.I. diesel engine with high pressure fuel injection, SAE 920692. Society of Automotive Engineers, Warrenville, PA. Heywood, J.B. (1988) Internal Combustion Engine Fundamentals, Society of Automotive Engineers, Warrenville, PA. Wallner, T., Ciatti, S.A., Stockhausen, W.F., and Boyer, B.A. (2006) Endoscopic investigations in a hydrogen internal combustion engine. 1st International Symposium on Hydrogen Internal Combustion Engines, Technical University of Graz, Austria, Graz, Austria.

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12 Cool Flames Dionysios I. Kolaitis and Maria A. Founti

12.1 Introduction

The partial oxidation reactions that occur during the preignition stage of liquid hydrocarbon fuel combustion exhibit a complex thermochemical behavior due to the inherent interdependence between physical and chemical time scales. Cool flames form a binding link to autoignition, and are related to a large variety of conventional and novel technological applications. Although cool flames have been experimentally observed and studied for more than 120 years, it is the comprehensive research of the past two decades that has started to unveil the complexity of the lowtemperature partial oxidation of hydrocarbon fuels. The understanding of cool flame characteristics under different conditions with the use of computational tools and the development of dedicated chemical kinetics mechanisms to describe such behavior, especially in the low-temperature regime (T < 800 K), remain open topics; relevant research results are expected to improve liquid hydrocarbon fuel utilization and thus also to lower emission levels. This chapter focuses on the theory of cool flames, their technological applications, and the current status of numerical modeling of stabilized cool flames, and includes a brief outlook of future developments in their various areas of application.

12.2 Theory 12.2.1 Phenomenology

The term “cool flame” is used to describe the low-temperature (500–800 K) oxidative reactions that emerge, usually after an induction period, in an air/hydrocarbon mixture. Cool flames occur preferentially under fuel-rich conditions, and are characterized by the appearance of a faint bluish light, which is attributed to the

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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chemiluminescence of excited formaldehyde. Cool flame reactions are generally exothermic, producing modest amounts of heat; they are often described as partial or intermediate oxidation reactions, due to the fact that they result in the formation of a variety of intermediate species (e.g., alcohols, acids, aldehydes, peroxides). Cool flames appear, in the form of a self-quenching temperature and pressure pulse, during the two-stage hydrocarbon fuel ignition, and are associated with “knocking” in spark-ignition internal combustion engines (ICE). Cool flames have been observed for a variety of organic species, including saturated and unsaturated hydrocarbons, alcohols, aldehydes, ketones, acids, oils, ethers, and waxes [1]. In 1882, Perkin [2] studied the cool flame chemiluminescence characteristics of a large number of fuels such as alkanes, alkenes, aldehydes, and alcohols. In 1929, Emeleus [3] was the first to use the term “cool flame” to describe a faintly luminous flame that exhibited a similar spectrum for a variety of hydrocarbon fuels. Prior to the 1960s, research into cool flames was mainly focused on experimental observations of the phenomenology of the cool-flame and ignition regions. An overview of the basic characteristics of cool flames and their relation to autoignition was provided by Minkoff and Tipper [4], who reviewed the respective scientific knowledge up to that period. During the next decade, an understanding of the importance of free radicals [5], which led to establishing the principles of chain-branching reactions, provided the foundations for modern interpretations of hydrocarbon oxidation. The basic principles of the low-temperature oxidative reactions that constitute cool flames were presented during the early 1980s [6], while the first comprehensive review on cool flames, presenting both experimental results obtained in stirred reactors and theoretical chemical kinetics studies, along with ignition diagrams, appeared in 1987 [7]. In 1995, Griffiths performed a thorough analysis of cool flame reactions phenomenology, and presented a variety of relevant reduced kinetic mechanisms [8]. A subsequent book published in 1997 [9] provided a very detailed overview of the low-temperature combustion and autoignition phenomena of hydrocarbon fuels, whereas the structure of detailed chemical kinetics mechanisms regarding hydrocarbon low-temperature oxidation and autoignition was presented in Ref. [10]. When a mixture of air and a hydrocarbon fuel is introduced to a system at temperatures lower than 850 K, a two-stage ignition can be observed in records of the system’s pressure and temperature temporal profiles. The first stage of the ignition sequence involves the development of a cool flame, which results in a modest temperature and pressure increase (or pulse) in a closed vessel [9]. The shape of the pressure pulse depends on the rate of temperature increase and the fuel concentration, whereas the shape of the temperature pulse depends on the equilibrium between the system’s heat-release and the heat-loss rates. Cool flame pulses are far less intensive than those occurring during ignition. The emergence of temperature and pressure pulses in the reacting mixture corresponds to a rapid acceleration of the overall chemical reaction rate. However, the limited intensity of these pulses, as well as the small conversion of the reactants, suggest that the initial acceleration is followed by a spontaneous deceleration, while the reactants concentration is still high. The appearance of autocatalytic,

12.2 Theory

self-quenching chemical reactions is a unique characteristic property of cool flames that distinguishes them from the “conventional” combustion reactions. In the latter, the acceleration of the reaction rate is limited exclusively by the depletion of the reactants, whereas the acceleration of the reaction rate of cool flames is controlled by changes in the governing kinetic mechanism with increasing temperature. When an air–fuel mixture is fed into a technical combustion device, it is possible, under specific operational conditions, to observe a “delay” in the ignition of the mixture. This “ignition delay time” is usually defined as the time interval between the introduction of the combustible mixture and the appearance of a flame. In the general case of a nonpremixed air–liquid fuel mixture, the ignition delay time is considered to be the sum of a “physical delay time” and a “chemical delay time.” The physical delay time represents the delay owing to the physical processes involved with mixture preparation, such as atomization, fuel evaporation and air–fuel vapor mixing. The chemical delay time corresponds to a period of significant chemical activity, involving the generation of a radical pool and cool flame exothermal reactions, leading to the onset of the flame. When the initial temperature of the system is lower than 850 K and the mixture is relatively fuel-rich, a two-stage ignition can be observed [11]. A characteristic twostage ignition behavior is shown in Figure 12.1, where predictions of the temporal evolution inside a closed perfectly stirred reactor (PSR) fed with a stoichiometric air/n-heptane mixture are depicted. The mixture is considered to enter the adiabatic reactor at 650 K; a detailed kinetic mechanism [12] is used for the numerical simulations, performed using the CHEMKIN package [13]. Initially, no significant thermal activity is observed; however, a radical pool is being established by the lowtemperature oxidative reactions. At approximately 0.057 s, a sudden increase in temperature is observed; this moderate temperature “pulse” is characteristic of the

Figure 12.1 Predictions of the temporal evolution of temperature in a closed perfectly stirred reactor fed with a stoichiometric air/n-heptane mixture.

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onset of cool flame reactions. The maximum temperature increase that is achieved in a cool flame does not exceed 200 K, since only a small amount of the total available chemical energy of the fuel is released. The quantity of fuel which is consumed in cool flame reactions is also relatively small (2–10%) with respect to the initial fuel concentration. For the case presented in Figure 12.1, the period of cool flame reactivity is quite prolonged; however, due to the assumed adiabatic closed PSR conditions, kinetic and thermal feedback phenomena lead to autoignition at approximately 0.15 s. In general, the prevailing kinetic mechanisms governing the oxidative reactions of hydrocarbon fuels are continuously changing, depending on the temperature of the mixture. Thus, it is possible to define low- (T < 800 K), intermediate- (800 K < T < 1000 K) and high-temperature (T > 1000 K) oxidative regions, in which significantly different oxidizing schemes are effective. Cool flames manifest themselves in the temperature region where the transition between low- and intermediate-temperature mechanisms occurs, and are dominated by exothermic degenerate chain-branching reactions that involve the production of a variety of important long-lived intermediate species [7, 8, 14]. Both thermal and kinetic feedback phenomena are important in the cool flame oxidation region, due to the competition between chain-termination and chain-branching reactions [8, 15]. The latter effect results in the appearance of the negative temperature coefficient (NTC) behavior. 12.2.2 Negative Temperature Coefficient (NTC)

Cool flame reactions are responsible for the formation of a variety of important intermediate radical species. At lower temperatures, chain-branching reactions are favored, leading to an acceleration of the overall reaction rate. However, when the temperature is sufficiently increased, the chain-branching precursors begin to decompose; in this case, chain-termination reactions become more important, as their activation energy is decreasing with increasing temperature. As a result, a NTC region appears at the temperature regime where transition between the low- and intermediate-temperature oxidative mechanisms occurs. In this NTC region the overall reaction rate is decreasing with increasing temperature; this behavior is a distinctive characteristic of cool flame reactions, being a unique phenomenon in hydrocarbon oxidation. The characteristic NTC behavior is depicted in Figure 12.2, where fuel concentration predictions are presented as a function of inlet temperature. The numerical simulations are obtained using the CHEMKIN software to integrate a detailed n-heptane oxidation chemical kinetics mechanism [12]. A n-heptane/air mixture is assumed to enter an open-flowing PSR reactor. Initially, the mixture reactivity exhibits an increasing trend up to approximately 640 K; however, as the inlet temperature is further increased the fuel consumption rate decreases, which suggests the emergence of NTC behavior in the intermediate temperature region (640 K < T < 750 K). At even higher temperatures, the fuel consumption rate increases again due to the high-temperature oxidative reactions that lead to

12.2 Theory

Figure 12.2 Predictions of n-heptane concentration as a function of inlet temperature in an openflowing perfectly stirred reactor.

complete combustion at T > 980 K. Such a chemical behavior has been observed in experimental studies performed for a large variety of hydrocarbon fuels, such as propane [16], iso-butane [17], n-pentane [18], n-heptane [19] and mixtures of nheptane and iso-octane [20]. 12.2.3 Stabilized Cool Flames

By exploiting the NTC phenomenon as a “chemical barrier” for autoignition [16], it is possible to “stabilize” the cool flame reactions in an open flowing system. In this case, heat losses at the system’s boundaries are completely balanced by heat generation due to the exothermal cool flame chemical activity. As a result, a steady-state thermochemical operation can be achieved, which prevents thermal runaway leading to “conventional” ignition. When cool flame reactions are stabilized, the steady-state concentrations of the chain-branching propagation radicals become very low [21]. Stabilized cool flames (SCFs) appear exclusively in the NTC region, regardless of the system’s pressure [7, 16]. In Figure 12.3, a typical hydrocarbon/air reactive system exhibiting cool flame reactivity is considered. The heat release rate due to the exothermal chemical reactions is described by an S-shaped curve (curve R), which is characteristic of the NTC region. The conductive heat losses at the system’s boundary vary linearly with temperature; the different thermal conditions are depicted by the different slopes in the heat loss rate curves (lines L1, L2, L3). The mixture’s temperature and the relative position of the heat release and heat loss curves determine the overall thermochemical behavior of the system (e.g., autoignition, quenching, SCF). When heat losses are systematically lower than the corresponding heat release rate values (line L1),

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Figure 12.3 Temperature variation of heat-release and heat-loss rate curves in an open-flowing reactor.

autoignition is promptly observed. In contrast, when heat losses are substantially higher than the exothermally produced energy, no chemical activity can be observed (line L3). In the case that the heat release rate curve (R) intersects with the heat loss rate line (L2), a variety of phenomena can be observed, based on the system’s thermochemical behavior. At system temperatures lower than the first point of intersection (A), heat losses exceed the heat release rate and no chemical activity is observed. When the initial temperature is higher than the critical value where the heat release rate exceeds the heat losses, the temperature of the system starts to increase. In principle, all points where the two curves intersect correspond to potential steady-state operational conditions. However, in terms of system dynamics, only point B, lying inside the NTC region, corresponds to a “stable focus” that may sustain a stable thermochemical environment. Under these conditions, any small perturbation in the system, created by a temporary change of temperature or fuel concentration, does not affect the system’s behavior, as cool flame reactivity tends to “automatically” restore steady-state operational conditions. On the other hand, a perturbation in point A may lead to quenching, whereas a perturbation in point C may lead to autoignition. Thus, only when the thermal heat losses are fully compensated by the reactive heat release rate and the system’s temperature lies in the NTC region (point B), can steady-state operational conditions be achieved; in this case, the system exhibits a SCF behavior. Experimental evidence [22, 23] suggests that in an open flowing air/diesel oil mixture, SCFs are initiated at 580 K, leading to a temperature increase up to 200 K. The temperature of the system is then stabilized at the higher level, as thermal runaway – which would lead to autoignition – is prevented by SCF reactions. Utilization of the SCF phenomenon is important as a means of enhancing liquid fuel evaporation, resulting in the production of a homogeneous, heated, well-mixed

12.2 Theory

and residue-free air/fuel vapor gaseous mixture that can subsequently be used in a variety of technological applications for the proper preparation of the air–fuel mixture. Unfortunately, investigations into SCF phenomena are quite few in number. In 1971, Ballinger and Ryason [24] reported the appearance of SCFs in a flat-flame burner for a variety of fuels such as n-butane, n-pentane and n-heptane, whereas the experiments of Caprio et al. [17] confirmed that SCFs appeared exclusively in the NTC region. In 1988, Morley [11] reported the development of one-dimensional SCFs in air/n-heptane mixtures under atmospheric pressure; in this situation, varying the initial temperature of the mixture was found to have a very small effect on the final operating temperature achieved in each case. This behavior proved to be a consequence of the delicate balance of chain reactions in the NTC region. More specifically, when a critical temperature value is exceeded, chaintermination reactions become more dominant than chain-branching reactions, thus limiting the overall chemical activity. A very systematic and detailed experimental investigation of atmospheric pressure diesel oil SCF reactors has been carried out by RWTH-Aachen and OWI GmbH. These studies have focused on the investigation of plug-flow [23, 25] and recirculating-flow [26, 27] SCF reactors used for liquid fuel evaporation [28] and reforming for fuel cell applications [22, 29]. Representative experimental results are depicted in Figure 12.4, where a plug-flow reactor was used to study the development of SCF using air/diesel oil mixtures under atmospheric pressure conditions [28]. No significant chemical activity was observed when the inlet temperature was below 580 K, but when this critical value was exceeded the mixture temperature at the reactor’s outlet was suddenly raised to approximately 750 K – a value which was almost independent of the inlet temperature. The SCF reactivity ceased at 770 K, and autoignition was observed at inlet temperatures higher than 870 K.

Figure 12.4 Inlet and outlet temperature measurements in an atmospheric pressure plug-flow reactor fed with a stoichiometric air/diesel oil mixture. Adapted from Ref. [28].

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12.2.4 Chemical Kinetics

The oxidative behavior of hydrocarbon fuels, especially in low-temperature conditions, depends heavily on the mechanisms of free radical generation and destruction. The rates of generation or destruction of radicals are controlled by chemical kinetics and, consequently, can vary largely among fuels of different chemical structure. Free radicals (e.g., OH. , H. , HO2. , R. ) are associated with chemical “chain reactions”, which affect the global rate of hydrocarbon oxidation [30]; the latter can proceed in a controlled manner (slow reaction), or through a rapid increase in rate which leads to an explosion (autoignition). The five main processes that describe the macroscopic evolution of a radical chain include: . . . . .

initiation, which is the formation of radicals from parent molecules; propagation, the maintenance of the number of radicals; branching, which involves increasing the number of radicals; termination, where the number of radicals is decreased; and degenerate branching, which includes the formation of new radicals from a quasistable intermediate species.

The relative importance of all these chain reaction processes is largely determined by the temperature of the system. Experimental evidence [11], supported by theoretical arguments and chemical kinetics numerical simulations, has suggested that in alkane oxidation processes three distinct temperature regions can be defined, where different reaction paths are prevailing. At high temperatures (T > 1000 K) the alkyl radicals quickly decompose and hydrogen atom chemistry is important. At intermediate temperatures (800 K < T < 1000 K) HO2. abstraction and H2O2. decomposition reactions are more significant. Finally, at low temperatures (T < 800 K), the reactions of peroxy radicals become the most important features of the prevailing chemistry. The given temperature limits are indicative; the temperature range over which each region extends depends heavily on the system pressure and the chemical structure of the specific alkane. The radicals involved in the hightemperature oxidative pathway (e.g., OH. , O. ) are far more reactive than the respective radicals emerging in the low-temperature path (e.g., R. , HO2. ). In general, cool flames manifest themselves mainly in the low-temperature oxidative region, where degenerate branching reactions become important [8]. One important parameter that distinguishes high-temperature from low-temperature oxidation processes is the breaking of the initial alkane molecule carbon chain. Low-temperature oxidative reactions tend to gradually decompose the carbon chain (e.g., C4 ! C3 ! C2 ! C1), whereas high-temperature reaction paths lead to rapid decomposition (e.g., C4 ! 2 C2). As a result, the accurate description of the kinetics of long carbon-chain species becomes essential when low-temperature oxidative processes need to be investigated. The oxidative process in the high-temperature regime is initiated by the breaking of a carbon–carbon bond to form hydrocarbon radicals, supported by hydrogen

12.2 Theory

Figure 12.5 General kinetic scheme of the primary low-temperature oxidation reactions of alkane fuels.

abstraction from the initial alkane molecule, due to O2 attack [30]. The radicals produced are subsequently rapidly decomposed, leading to the production of CO, CO2, CH2O, and saturated small hydrocarbons [19]. The HO2. , formed by hydrogen abstraction, reacts with the initial alkane to form hydrogen peroxide (H2O2. ). The chain-branching mechanism in this temperature region is based on the decomposition of H2O2. , leading to the formation of two OH. radicals [31] and the oxidation of H. leading to the production of O. and OH. radicals [19]. A general kinetic scheme depicting the main alkane oxidation reactions in the low-temperature region is presented in Figure 12.5. In this case, the alkane (RH) oxidation is initiated by reaction with O2 to produce an alkyl radical (R. ) and HO2. . The latter may also react with the initial fuel molecule, forming H2O2. and a further alkyl radical (R. ). However, the high activation energy that is required for decomposition of the resulting alkyl radicals cannot be achieved at low temperatures. As a result, the alkyl radical produced reacts with O2 to form a peroxy radical (RO2. ) or, alternatively, an olefin and HO2. . The latter reaction is favored at intermediate temperatures, whereas the activation energy of the former reaction depends heavily on temperature. Thus, peroxy radical production is favored at low-temperature conditions, but when the temperature is increased the equilibrium is shifted to the left-hand side, thus effectively “blocking” the low-temperature branch [32]. As a result, HO2. production is favored over OH. , and since the former is appreciably less

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reactive than the latter, the NTC behavior is observed at this specific temperature range, typically 700–800 K [15, 33]. At low temperatures, the peroxy radical undergoes internal H-atom isomerization to form a hydroperoxyalkyl radical (QOOH. ), which, in turn, may react further by following four different alternative routes, depending on the system’s temperature. The followed reaction path has a large impact on the macroscopically observed lowtemperature thermochemical behavior [34, 35]. At low temperatures, the hydroperoxyalkyl radical further reacts with O2 to form a hydroperoxyalkylperoxy radical (O2QOOH. ). The latter can react through internal hydrogen abstraction, leading to the formation of OH. and a ketohydroperoxy radical (OQ0 OOH. ), which decomposes further in a variety of alternative paths, producing at least two radicals; for instance, it can form OH. and a diketone radical (OQ0 O. ) [36, 37]. This reaction scheme constitutes a highly reactive degenerate chain-branching pathway, as one alkyl radical results in the formation of three radicals. At higher temperatures, the hydroperoxyalkyl radical may follow three alternative chain-propagation reaction pathways: (i) OH. elimination and production of a cyclic ether by concerted ring closure; (ii) b-scission forming OH. and a ketone; and (iii) decomposition to an olefin and HO2. . As the temperature increases, these chain-propagation reactions are favored over the degenerate chain-branching pathway; as a result, the overall reactivity of the fuel is decreased, giving rise to the emergence of NTC behavior. The complete description of the detailed chemical kinetics of hydrocarbon fuel oxidation still remains an extremely difficult task, due to the complexity and richness of the involved chemical phenomena (see Chapter 8). As a result, it is impossible to use a simple general mechanism to describe the relevant thermochemical behavior for all possible conditions. The first systematic study of low-temperature oxidative phenomena was conducted by Semenov [5], who demonstrated the importance of the degenerate chain-branching reactions for the first time. A series of experimental studies performed during the 1970s allowed the determination of the intermediate species produced during the low-temperature oxidation of hydrocarbons. A thorough description of the main chemical reactions emerging in the cool flame regime was provided in 1981 by Benson [6], who emphasized the importance of thermochemical feedback phenomena. In 1987, the review of Lignola and Reverchon [7] presented a basic kinetic scheme regarding alkane cool flame reactions, along with a detailed mechanism describing the low-temperature oxidation of propane. The importance of isomerization reactions was recognized at a later date [33]. A complete review of the main reaction pathways emerging during low-temperature oxidation has been provided in the review by Westbrook [10].

12.3 Applications

The thermochemical characteristics of cool flame phenomena may have significant effects on a wide variety of technological applications; some examples are outlined in the following sections.

12.3 Applications

12.3.1 Liquid Fuel Evaporation for Premixed Combustion

Liquid fuel atomization is commonly used in a variety of technical combustion applications, such as furnaces and boilers, internal combustion engines, gas turbines and rocket engines. The aim is to increase the fuel’s specific surface area and thus accelerate the rates of both evaporation and combustion (see Vol. 3 Ch. 11). In a conventional spray combustion system, the incomplete mixing of the liquid fuel droplets and fuel vapors with the surrounding air leads to inhomogeneities in the mixture that may in turn reduce the total fuel utilization and enhance pollutant formation. The utilization of a SCF-assisted evaporation device, where low-temperature oxidative cool flame reactions occur and the liquid fuel is allowed to evaporate, but not to burn, has been recently proposed [28]. The main advantage of such a device is that it allows the spatial and temporal separation of the two main phenomena, namely droplet evaporation and fuel combustion. The additional heat produced by the exothermic reactions enhances droplet evaporation, resulting in the production of a heated, highly homogeneous mixture of air, fuel vapor, and small quantities of intermediate chemical species. This mixture can subsequently be fed into premixed combustion devices that allow a better control of the combustion process. The dynamic stability of the SCF process improves technical flexibility, as operation of the overall system is not affected by random perturbations; in addition, large power modulation can be effectively achieved. In contrast, liquid fuel evaporation devices that operate at higher temperatures, taking advantage of the fuel’s ignition delay time, show considerable sensitivity to operational parameters such as the power level and the system’s temperature, as the ignition delay time is largely affected by these parameters [29]. The use of a SCF-assisted liquid fuel evaporation device, in combination with a porous burner, in domestic heating applications has been shown to provide a number of advantages over conventional boilers, including an improved efficiency under partial loads, a significant reduction in pollutant emissions, and a reduction in the volume of the device itself [38]. A conceptual sketch of a recirculating flow SCF liquid fuel evaporation device is shown in Figure 12.6. The air stream is preheated to the required temperature before entering the device. The fuel is then injected using a conventional pressure atomizing nozzle; the droplets evaporate and the emerging fuel vapors react with the surrounding air. The correct selection of the reacting mixture’s residence time allows the stabilized cool flame reactions to be established. In such a device, stable operational conditions can be achieved when the exothermal heat release is compensated by heat losses (see Section 12.2.3). Operation in the steady-state, nonigniting SCF regime results in the “consumption” of 2–10% of the fuel’s available thermal energy, which is regained as the temperature of the mixture is increased. In an atmospheric pressure SCF diesel oil evaporation device, the temperature of the air–fuel vapor mixture may increase to 200 K in the flow direction and subsequently stabilize at the raised level [23, 28], while autoignition is prevented by the selfquenching autocatalytic nature of the SCF reactions; this process is favored under

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Figure 12.6 Layout of a recirculating stabilized cool flame liquid fuel evaporation device.

fuel-rich conditions [23]. Both, experimental evidence [23] and numerical simulations [39, 40] have suggested that the achieved operational temperature is almost independent of the air preheating temperature, as long as the latter lies in the range of 600–800 K. As a result, a technical SCF liquid evaporation device can be designed to operate near the lower temperature limit in order to have a better overall efficiency by minimizing the amount of energy required to preheat the air. 12.3.2 Liquid Fuel Reforming for Fuel Cell Applications

One innovative technological sector where cool flame reactions are expected to play an important role is that of the liquid fuel reforming process for fuel cell applications (see Chapter 15). The optimum operation of a fuel cell is achieved when it is fed with pure hydrogen; however, due to difficulties in the production, storage and distribution of hydrogen, a variety of alternative (e.g., liquid) fuels have been also considered, at least during an initial transitional period towards the “hydrogen economy.” Liquid fuels (e.g., methanol, ethanol, gasoline, diesel oil) exhibit high energy density values, and allow the use of any existing storage and distribution infrastructure. However, these fuels cannot be fed directly into a fuel cell (with the exception of methanol used in direct methanol fuel cells), but must first be “reformed” in order to produce a gaseous mixture that is rich in hydrogen (and in some cases, also CO). The reforming process is achieved in dedicated technical reactors (reformers), in which the initial fuel is introduced to a well-controlled thermochemical environment. The most commonly used techniques in liquid fuel reforming include thermal partial oxidation (TPOX), catalytic partial oxidation (CPOX), and auto-thermal reforming (ATR). The process of partial oxidation (thermal or catalytic) is based on the concept of an extreme fuel-rich combustion where, ideally, the fuel is oxidized to CO, CO2, H2, and H2O. The presence of a catalyst allows the operating temperature to be decreased. In the ATR process, the partial oxidation is followed by steam reforming, which aims

12.3 Applications

to push the chemical equilibrium towards H2, CO, or CO2 formation; no external heat supply is required in this case. According to various reports [22, 29, 41], the use of a SCF liquid fuel evaporation device as a fuel preparation step, located upstream of a “conventional” fuel reformer, results in significant gains in terms of system efficiency. The intermediate chemical species produced during the oxidative cool flame reactions assist the fuel-reforming process [22, 41]. In the case of CPOX reformers, inhomogeneities in the feeding mixture result in a lower efficiency, catalyst degradation, and soot production; the highly homogeneous mixture produced by a SCF evaporator minimizes such adverse effects [29]. When diesel oil is used as a fuel, the combined SCF-reformer technology results in drastic reductions in SOx, NOx and soot production, due to the low-temperature cool flame reactions [26, 41]. The schematic layout of a technical apparatus combining a SCF liquid fuel evaporation device and a CPOX or ATR reformer is shown in Figure 12.7 [22]. Hartmann et al. [22] investigated, both experimentally and theoretically, the potential of using cool flame reactions in order to produce a complete and residue-free mixture of air and liquid hydrocarbon vapors, appropriate for use in a variety of fuel-reforming methodologies. Their attention was focused on determining the cool flame stability limits for the three main fuel-reforming technologies (TPOX, CPOX, and ATR). The results of these studies, which incorporated a CPOX diesel oil reformer, suggested that the use of a SCF pretreatment stage would result in a 7% increase in the total reforming efficiency of the system. A thorough presentation of the available investigations on cool flame partial oxidation, and the significance of its inclusion in fuel-reforming processes, is available in an extensive review [41]. Matos da Silva et al. [29] studied the autothermal reforming of gasoline using a SCF evaporation device; their experimental results showed that when a SCF reactor was combined with an autothermal gasoline reformer, a more homogeneous mixture was

Figure 12.7 General layout of a technical apparatus combining a conventional liquid fuel reformer (CPOX or ATR) with a stabilized cool flame liquid fuel evaporation device. Adapted from Ref. [22].

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produced compared to conventional mixing technologies, thus producing a positive effect on the overall fuel conversion efficiency of the system. It was also stated that both the CPOX and ATR reforming processes exhibited favorable conversion efficiencies at the temperature regime (620–820 K), where steady-state SCF operation could be achieved for a variety of hydrocarbon fuels. 12.3.3 Internal Combustion Engines

Cool flames affect a variety of phenomena that have been observed in internal combustion engines (ICEs), ranging from the unwanted “knocking” in sparkignition engines to the controlled low-temperature combustion in modern homogeneous charge compression-ignition (HCCI) engines. 12.3.3.1 Knocking Cool flames have initially attracted the interest of research groups due to their association with the “knocking” that is observed in spark-ignition ICEs [8–10, 42]. This adverse phenomenon is attributed to a sudden increase in the temperature and pressure of the unburned “end gas,” which results in its autoignition before it is reached, as would be expected, by the propagating flame front. The macroscopic result of the emerging uncontrolled autoignition sites are observable pressure pulses that excite acoustic resonances of the gas in the cylinder and the engine block, leading to an audible knocking sound and, in the long run, to serious engine damage. In the case of hydrocarbon fuels containing alkanes, the uncontrolled end gas autoignition can be the result of low-temperature cool flame oxidative reactions that lead to thermal runaway. Over the past decades, macroscopic studies of the cool flame reactivity of model fuels have resulted in determining the root causes of the observed phenomena, and have thus allowed the development of appropriate strategies to alleviate them. However, an in-depth understanding of the observed thermochemical behavior of commonly used complex hydrocarbon fuels and anti-knock additives has yet to be achieved, due to the extreme complexity of the chemical kinetic mechanisms involved in the respective operational region [9]. 12.3.3.2 Low-Temperature Combustion and HCCI Engines During the past few decades, the increasingly strict regulations regarding exhaust pollutant emissions have led ICE manufacturers to investigate innovative concepts based on alternative in-cylinder combustion strategies. One such innovative technique that has attracted significant attention due to its theoretical potential in reducing both NOx and particulate matter emissions, whilst simultaneously retaining a high thermal efficiency, is that of low-temperature combustion (LTC) (see Vol. 5 Ch. 2). A variety of alternative strategies for the practical achievement of LTC conditions have been proposed; one very promising technology is that of HCCI (see Vol. 5 Ch. 1), in which the fuel is introduced into the cylinder at a very early stage, aiming to create a premixed, lean, homogeneous mixture that is ignited by compression [43]. One of the most important difficulties arising in practical HCCI engine realizations, and which has prevented them from being readily commercialized,

12.3 Applications

is that of ignition timing control. The lack of any combustion-initiating device in a HCCI engine means that the ignition process must be carefully controlled in order to achieve the required performance. The time of ignition can be varied by changing compression ratio, fuel/air equivalence ratio, the time of intake valve opening, and the intake manifold temperature. Ignition timing is controlled primarily by cool flame chemical kinetics, with little influence from effects such as turbulence or mixing that play such a large role in other engine combustion problems [10, 44]. The respective exothermic heat release determines the point at which the in-cylinder temperature reaches a level sufficient to induce a subsequent “thermal” flame. In an experimental study conducted by Singh et al. [45], a distinct cool flame-induced heat release was observed during the LTC operation of a heavy-duty diesel engine, in contrast to the conventional diesel combustion strategy, where no such phenomenon is observed [46]. The importance of cool flame reactions for the ignition timing control of HCCI engines has been pinpointed by Yamada et al. [47], who performed a detailed kinetic study of the transition from cool flame to thermal flame in enginelike conditions. The detailed experimental studies and numerical simulations of Sohm et al. [44], which were aimed at investigating the cool flame oxidation region in HCCI combustion, resulted in identifying the inadequacies of the available detailed and skeletal chemical kinetics mechanisms in efficiently describing the thermochemical behavior of cool flame phenomena under HCCI combustion conditions. 12.3.3.3 Lean Premixed Prevaporized Combustion in Gas Turbines Lean premixed prevaporized (LPP) combustion provides an efficient means of reducing NOx emissions from gas turbines whilst, allowing, at the same time, an almost soot-free combustion using liquid fuels. The LPP concept is based on the installation of a “fuel preparation” device upstream of the fuel injectors, which allows pre-evaporation of the fuel and premixing of the produced vapors with air, before the mixture enters the combustion chamber. Although premixed combustion in gas turbines is well established for gaseous fuels, its achievement is technically difficult with liquid fuels due to the fact that any fuel autoignition or flame flashback must be prevented, especially in the case of aircraft gas turbines [48, 49]. The available time for liquid fuel evaporation and mixing in the LPP device is limited by the ignition delay time, which is quite short, especially in the high-pressure conditions found in a gas turbine combustor. It is evident that an in-depth understanding of the respective interactive processes, such as droplet evaporation, turbulent mixing and chemical activity, will allow design optimization for a wide variety of operational conditions [50]. In this context, a detailed analysis of the thermochemical conditions leading to the appearance of cool flame reactions is expected to provide valuable insight to the observed phenomena. 12.3.4 Industrial Safety

An important aspect of cool flame reactions is their relevance to industrial safety (see Chapter 18). An accidental release of a hydrocarbon may result in a fuel-rich

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mixture that, even if it is below its autoignition temperature, may initiate cool flame reactions that would eventually lead to autoignition and, in the worst case scenario, an explosion. Pekalski et al. [1] investigated the relation of cool flames and autoignition phenomena to process safety at elevated pressures and temperatures. Their attention was focused on chemical process industries, suggesting that numerous industrial loss incidents, which conventionally are considered “unexplained,” are in fact a result of cool flame initiation leading to a subsequent transition to hot ignition [51]. It is suggested that the operational condition limits that favor cool flame reactions should be also considered as a safety parameter for processes operating at elevated temperatures and pressures.

12.4 Numerical Modeling of Stabilized Cool Flame Reactors

The concept of SCF-assisted liquid fuel evaporation may be applied to a wide variety of technical applications (c.f. Section 12.3). Generic numerical tools, such as chemical kinetics solvers or computational fluid dynamics (CFD) codes, assisted by dedicated numerical models, can significantly support the design and performance optimization process of such devices. Cool flame phenomena are usually described by using a phenomenological approach, and the majority of the respective reports have been based primarily on experimental observations in stirred reactors [1, 7–9, 11, 14, 19, 41, 42]. In this context, the numerical modeling of cool flame reactions is performed either in zero-dimensional chemical kinetics studies, which investigate the autoignition behavior of various hydrocarbon fuels [12, 52, 53], or in one-dimensional (1-D) isolated droplet autoignition simulations [15, 42]. Cool flames are commonly not taken into account in detailed CFD simulations of turbulent reactive flows, with the sole exception of ICE in-cylinder studies, where they are implicitly dealt with under the frame of the “ignition delay time” modeling [54]. Cool flames have attracted the interest of research groups mainly due to their association with “knocking” phenomena and HCCI combustion in ICEs [7–10, 42, 55]; as a result, most of the respective computational studies have focused on the hightemperature and high-pressure conditions prevailing inside the engine cylinder [55, 56]. In these cases, cool flames are usually considered as a “transitional stage” leading to autoignition; reports dedicated to the numerical modeling of nonigniting SCF conditions are extremely scarce. As a result, the numerical simulation of the turbulent, multiphase, multicomponent and reactive flow-field developing in a nonigniting SCF technical device poses significant challenges, as a variety of dedicated numerical models must be developed and validated. The effective simulation of thermochemical phenomena is one of the most decisive parameters affecting the quality of the numerical modeling predictions of turbulent reactive flows. Cool flames – and particularly the low-temperature NTC regime where SCF can be established – are known to be kinetically controlled phenomena characterized by “slow” chemistry [7, 55, 57–60]. As a result, the concept of infinitely fast chemistry, commonly applied in nonpremixed spray combustion

12.4 Numerical Modeling of Stabilized Cool Flame Reactors

simulations where turbulent mixing is assumed to be the controlling parameter [57, 61], is not valid in the case of a SCF liquid fuel evaporator, which is essentially a nonpremixed and noncombusting device [62]. Consequently, multistep, finite-rate chemical reactions must be taken into account, due to the need to describe in sufficient detail the degenerate chain-branching cool flame oxidative activity leading to the NTC behavior [9, 12, 52, 53, 63]. However, the direct integration of detailed chemical kinetics mechanisms into turbulent, two-phase CFD simulations inflicts high computational costs [42, 64]. As a result, depending on the phenomena that need to be studied, different modeling approaches may be used. For instance, when it is important to specify the exact chemical composition of the produced mixture, a 1-D chemical kinetics solver can be used in conjunction with a very detailed chemical kinetics scheme. On the other hand, if there is a need to analyze and optimize the developing turbulent two-phase flow-field inside the SCF device, a complete twophase CFD simulation can be performed, using a simplified numerical approach to model the cool flame chemical activity. A brief presentation of alternative numerical simulation techniques, of varying complexity, is provided in the following sections, demonstrating the simulation methodology for a variety of SCF diesel oil evaporation devices. 12.4.1 One-Dimensional Chemical Kinetics Simulation of a Linear Flow SCF Reactor

The effective numerical simulation of a diesel oil SCF device necessitates the use of an appropriate chemical kinetics mechanism, which is capable of accurately describing the low-temperature oxidative behavior of diesel oil and thus allowing the quantification of the cool flame-induced heat release rate, as well as the chemical composition of the produced mixture. Commercial diesel oil, a petroleum distillate fuel, is a complex mixture of several hundred different hydrocarbon components (e.g., n-alkanes, iso-alkanes, cycloalkanes, mono- and poly-aromatic hydrocarbons). Major differences in the molecular size and structure of these components lead to strongly varying physical and chemical properties of commercial diesel oil. The versatile diesel oil production conditions (e.g., crude oil source, refining process, legal requirements) hinder the “exact” definition of its mixture composition. Various “surrogate” fuels, exhibiting similar thermochemical behavior to diesel oil, are commonly used in experimental and numerical studies of diesel oil combustion [65–68]. Even though significant progress has been made in the basic understanding of diesel oil ignition and combustion behavior, an accurate description of the low-temperature cool flame oxidation phenomena remains an open question [41, 63]. Normal heptane (n-C7H16) exhibits a similar cetane number to diesel oil, and consequently is commonly used as a surrogate fuel to describe its autoignition behavior [53, 56, 64]. A wealth of literature exists regarding the development of chemical kinetic mechanisms for n-heptane, especially for its low-temperature oxidation and autoignition phenomena [68, 69]. A selection of n-heptane oxidation kinetic mechanisms of varied complexity, representing the most characteristic

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approaches in kinetic modeling research, is used here to perform a 1-D simulation of a diesel oil SCF reactor. In general, the size and complexity of the available mechanisms vary significantly, depending on the desired level of detail. Detailed kinetic schemes are the most comprehensive, as they usually consist of hundreds of species and thousands of elementary reactions. Mechanism S561 (561 species and 2539 reactions), which is considered here, has been based on 25 main reaction classes and covers all temperature regions, taking into consideration reactions for all nine heptane isomers [12]. In attempting to overcome the high computational costs associated with using detailed mechanisms, various numerical methods have been developed aiming to reduce their mechanism size [8, 64]. The resulting reduced mechanisms correspond, essentially, to a subset of the initial detailed mechanism, and comprise tens of species and hundreds of reactions. Mechanism S57 (57 species and 290 reactions) is a reduced version of S561, constructed by lumping together specific intermediate species [56]. Mechanism S44 (44 species and 112 reactions) [70] has also been derived from the reduction of a detailed mechanism (168 species and 1008 reactions) that was aimed at predicting high-pressure autoignition. Further attempts at simplification, using the concept of “species lumping,” result in skeletal mechanisms, which involve tens of species and tens of reactions. Mechanism S22 (22 species and 42 reactions) is essentially an extension of the well-known “Shell autoignition model” [8], and has been developed for the HCCI combustion modeling of primary reference fuels [71]. The simplest mechanisms available, called global kinetic schemes, utilize less than 10 reactions and a respective number of species. The global reaction scheme S7 (seven species and seven reactions) also focuses on HCCI combustion conditions [72]. Temperature measurements obtained by Edenfoher et al. [25] in an atmosphericpressure diesel oil-fed SCF reactor are used here to assess the prediction quality of the selected kinetic mechanisms. During the experiments, a mixture of pre-evaporated diesel oil and nitrogen was introduced together with air, preheated at 635 K, into a cylindrical steel tube, 600 mm long and with an internal diameter of 59 mm, operating under atmospheric pressure conditions. The tube was equipped with thermocouples that recorded the temperature profile along the reactor. The CHEMKIN software package [13] is used to perform the 1-D chemical kinetics simulations, utilizing the plug-flow reactor (PFR) concept and assuming adiabatic conditions [63]. In Figure 12.8, predictions of the axial temperature profile inside the diesel oil-fed SCF reactor are compared to respective measurements obtained in the case of initial fuel mass fraction equal to 0.164. The experimentally observed thermal behavior is typical for a SCF reactor: the mean mixture temperature raises gradually, achieving an almost constant value of 740 K, approximately 0.15 m downstream of the inlet section. The observed temperature increase is due to the exothermal cool flame reactions, which are readily stabilized, thus preventing autoignition of the mixture. Significant discrepancies are observed among the predictions with the various kinetic schemes. Mechanisms S44, S561, and S7 predict autoignition, although at different axial positions; surprisingly, the global scheme (S7) shows a better agreement than the more detailed mechanisms. On the other hand, mechanisms S22 and S57

12.4 Numerical Modeling of Stabilized Cool Flame Reactors

Figure 12.8 Axial evolution of temperature in a diesel oil stabilized cool flame linear flow reactor.

perform reasonably well, exhibiting a good quantitative agreement with the measured values. The differences observed among the latter mechanisms and the detailed S561 scheme, which is known to describe in great accuracy the oxidative characteristics of n-heptane [12, 63], suggests that the use of n-heptane as a surrogate for diesel oil, especially in the low-temperature, low-pressure oxidative region, is a rather questionable assumption. In order to overcome this limitation, the development of dedicated detailed diesel oil surrogate fuel oxidation mechanisms employing a carefully selected mixture of complex hydrocarbon components has been recently proposed [65–68]. 12.4.2 Two-Dimensional Two-Phase CFD Simulation of a Linear Flow SCF Reactor

The region of intense chemical activity inside a SCF liquid fuel evaporator extends to a relatively large volume, rather than forming a distinct thin “flame-sheet,” as is the case in nonpremixed combustion technical devices [62, 73]. As a result, “conventional” turbulent combustion modeling techniques cannot be readily applied to perform CFD simulations of nonigniting SCF reactors. A variety of alternative modeling approaches, developed to address the chemical term closure problem in two-phase CFD simulations of SCF diesel oil evaporators, is presented here. The direct integration of a detailed chemical kinetics mechanism in a CFD code leads to high computational costs [42, 64]; especially in the case of diesel oil oxidation (see Section 12.4.1), detailed schemes do not even ensure adequate prediction quality [63]. Various modeling techniques have been proposed to reduce the relevant computational burden, without, at the same time, losing the essential chemical information [8, 9, 64]. Three such alternative low-computational cost methodologies are

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investigated here, and their ability to describe with sufficient accuracy the main thermochemical characteristics of a SCF reactor is comparatively assessed. The first approach is based on the direct integration (DI) of a low-cost chemical kinetics mechanism in the CFD code [74]. In this case, the global mechanism S7 [72] is used to describe the cool flame oxidative behavior of diesel oil. In the second approach, a semi-empirical (SE) algebraic equation is utilized to describe the thermochemical cool flame activity. A polynomial equation is developed [73], using a series of experimental data obtained in a linear SCF diesel spray evaporator [23], correlating the cool flame-induced heat release rate to the mixture’s temperature. The effects of fuel concentration variations are taken into account by introducing a “correction factor” [39], derived by correlating chemical kinetics simulation results using the semi-detailed mechanism S57 [56]. The third approach utilizes the “parameter fitting” technique [9, 75], based on the look-up table (LUT) concept. In this case, each computational cell is assumed to be an “infinitely fast mixing” PSR with spatially uniform temperature and mixture composition; an assumption commonly used in ICE CFD simulations [44, 55]. In the “tabulated chemistry” approach used here, the time-consuming chemical kinetics numerical calculations are performed a priori, and the obtained results are stored in a database (LUT); during the CFD simulations the respective data are readily retrieved using a multilinear interpolation technique. The chemical database is created using the CHEMKIN package to integrate the S57 n-heptane oxidation mechanism [56], assuming PSR conditions [40]. The values of three independent parameters (temperature, fuel concentration, and residence time) are varied within a prescribed range, corresponding to typical values observed in SCF reactors, and resulting in 11 648 different simulated operational conditions. The values of heat release rate and fuel consumption are stored in a LUT [40]; a snapshot of the obtained results, corresponding to a PSR of 0.015 s residence time, is presented in Figure 12.9.

Figure 12.9 Dependence of cool flame induced heat release rate on temperature and fuel concentration.

12.4 Numerical Modeling of Stabilized Cool Flame Reactors

The low-temperature limit of the NTC region is changing with fuel concentration; in fact, the entire low-temperature chemistry is “shifted” towards lower temperatures when the fuel concentration is increased. As expected [19, 42], the overall cool flame activity is favored under fuel-rich conditions. The in-house-developed 2PHASE CFD code [76–78] is used to simulate a linear SCF diesel oil evaporator. Here, the continuous phase is treated as a steady, incompressible, turbulent flow by solving the Reynolds averaged Navier–Stokes (RANS) equations. For this, a finite-volume method is used, based on a staggered grid arrangement, utilizing the SIMPLE algorithm. Turbulence is modeled using the RNG k-e turbulence model [79], and adiabatic conditions are used to simulate the well-insulated outer wall of the reactor. The dispersed phase is described using either the Eulerian or the Lagrangian methodology [77]; in this case, the Eulerian approach is used in conjunction with the DI model [74], whereas the Lagrangian description is used when the SE [39, 73] and LUT [40, 62] models are utilized. The droplet evaporation model used in both Eulerian and Lagrangian simulations is selected based on suggestions from a relevant detailed comparative study [78]. Temperature measurements, obtained along a linear flow SCF diesel oil evaporator [23], are used for validation. The SCF device used in the experiments was a 1.0 m-long insulated metal pipe, with an internal diameter of 0.1 m, equipped with a number of radially movable thermocouples. The air stream, after being preheated, was supplied through a perforated disk to control and homogenize the flow’s turbulence level. A commercial, water-cooled Simplex pressure atomizer, fixed at the center of the perforated disk, was used to inject diesel oil fuel. A typical two-phase SCF behavior is observed (Figure 12.10). Immediately downstream of the fuel injection plane a drop in the mean temperature occurs, owing to droplet evaporation. The emerging fuel vapors are promptly mixed with air, and the exothermal cool flame reactions are initiated. As a result, an increase in the mean temperature of the order of 50–150 K (depending on the inlet temperature) is observed further downstream, reaching a nearly constant value at an axial distance of approximately 0.3 m. As SCF operation is established in the NTC region, autoignition is effectively prevented. Despite the large variations in the inlet temperature (623–723 K), the outlet temperature at the downstream end of the device remains practically constant (ca. 750 K); this is considered to be the “equilibrium” temperature [23, 28] of the SCF diesel oil oxidative reactions at atmospheric pressure conditions (c.f. Figures 12.4 and 12.8). Cylindrically axisymmetric flow conditions were assumed; the computational domain, measuring 0.9 m axially by 0.05 m radially, was discretized using 94
35 nonuniform rectangular grid nodes. The grid was refined close to the nozzle tip to improve local flow resolution. Grid independence was ensured by comparing predictions obtained with this grid arrangement to those obtained with a 143
52 nodes grid; the maximum deviation observed in the results was less than 2%. The initial droplet velocities at the nozzle injection plane were determined by interpolating experimental measurements available for a Simplex hollow-cone pressure atomizer [80], similar to that used in the considered SCF reactor. The inlet velocities of the carrier fluid were approximated by assuming a top-hat profile.

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Figure 12.10 Comparison of numerical predictions and temperature measurements along the symmetry axis of a diesel oil linear flow stabilized cool flame reactor.

In Figure 12.10, predictions of the axial temperature profile inside the SCF diesel oil evaporator are compared to respective measurements for various inlet temperatures. All modeling approaches capture sufficiently well the main features of the developing temperature field; the steady-state temperature levels achieved at the downstream end of the reactor are accurately reproduced, and the axial locations of main exothermal activity are effectively determined. However, predictions in the intense droplet evaporation region, just downstream of the injection plane, result in steeper temperature gradients than the experimentally observed values. The SE model performs best in the 623 K inlet temperature case, though the quality of its predictions slightly deteriorates with increasing temperature levels. On the other hand, the quantitative agreement of the DI and the LUT predictions with measurements improve with increasing operating temperature; the latter behavior reflects the inadequacy of the available reduced and detailed chemical kinetics mechanisms in the low-temperature, low-pressure cool flame oxidation regime [63].

12.4 Numerical Modeling of Stabilized Cool Flame Reactors

12.4.3 Three-Dimensional Two-Phase CFD Simulation of a Recirculating Flow SCF Reactor

Detailed three-dimensional (3-D) CFD simulation tools can be effectively used to support the design and performance optimization process of commercial technical devices. In this context, the ANSYS CFX 11.0 general-purpose CFD software suite is used to simulate a recirculating flow SCF diesel oil evaporator (c.f. Figure 12.6). The SCF reactor consisted of a main cylinder that was attached downstream to a 40 converging nozzle. A metal disk (bluff body) was fixed at the downstream end of the cylinder, in order to generate a flow recirculation zone that enhanced the reactor’s stability, thus allowing large power modulation. The establishment of a steady recirculation region was assisted by an inner cylinder, installed upstream of the bluff body. A water-cooled, 60 hollow-cone, Simplex pressure atomizer was used for diesel oil injection through a central circular opening, whereas the preheated (606 K) air-stream entered the reactor through eight circular openings positioned circumferentially around the central axis. The simulated conditions corresponded to a total power of 9.14 kW and a global equivalence ratio equal to 0.83. The CFX code is used to simulate the developing 3-D two-phase flow that is assumed to be steady, incompressible, and turbulent. A collocated unstructured grid consisting of 2 012 274 tetrahedral elements is used; grid independence is ensured by comparing the results to respective values obtained with a grid comprising 20% more elements. The grid is refined close to the solid boundaries in order to improve the local resolution of the developing boundary layers. The RNG k-e model is used to simulate the turbulent flow [79], whereas conjugate heat transfer is taken into account to determine heat transfer phenomena in flow regions occupied by heat-conducting solids (bluff body and inner cylinder). The SE model (see Section 12.4.2) is used to simulate the thermochemical behavior of cool flame reactions. The simulation of liquid droplet motion and evaporation is performed using an Euler–Lagrange approach; inlet conditions for the droplets are determined by the interpolation of available experimental values [80]. The complex, two-phase flow-field that develops inside the SCF reactor can be seen in Figure 12.11. Predictions of the gaseous-phase velocity vectors reveal the development of three recirculating flow regions. A minor recirculation zone is located just downstream of the injection nozzle, and is due to the entrainment of air through the circumferentially distributed inlet openings. The main recirculating flow is created by the bluff body, and develops between the inner and the outer cylinder. A third recirculation zone appears downstream of the bluff body but, due to the converging nozzle, the flow is fully developed before reaching the outlet. Predictions of droplet trajectories suggest that all droplets evaporate completely before reaching the bluff body, even though a small number actually impinge on the inner cylinder. Predictions of the heat release rate, due to the cool flame reactions, are depicted in Figure 12.12. Substantial chemical activity is observed in the main recirculation region, developing between the inner and the outer cylinders, thus confirming its vital role in the thermochemical stability of the device. High heat release rate values are

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Figure 12.11 Predictions of gas-phase velocity vectors and liquid droplet trajectories.

observed in the recirculation zone just downstream of the bluff body; in this region the multicomponent mixture is subjected to vigorous mixing (c.f. Figure 12.11) that results in enhancing the chemical activity. The visualization of the regions of intense heat production reveals that, in contrast to conventional combustion applications where chemical reactions occur mainly in a very thin “flame sheet”, cool flame reactivity zones are highly extended over a significant fraction of the overall volume. In Figure 12.13, predictions along the symmetry axis and the outer recirculation region are compared to respective temperature measurements [62]. Despite the complex interactions of the occurring physico-chemical phenomena (e.g., droplet

Figure 12.12 Predictions of cool flame-induced heat release rate.

12.5 Outlook

Figure 12.13 Comparison of temperature measurements and predictions in a recirculating flow stabilized cool flame diesel oil evaporator.

evaporation, turbulent mixing, cool flame reactions) and the simplifying assumptions used, a satisfactory quantitative agreement is achieved among the obtained numerical results and the available experimental data. In the region of the symmetry axis, droplet evaporation near the center line results in lower mean temperatures; numerical results underpredict the observed temperature values by approximately 30 K. Better levels of agreement are observed in the recirculation region, where intense cool flame activity occurs (c.f. Figure 12.12). In this case, the adiabatic boundary conditions, used for the simulation, result in a slight overprediction of the measured values. In general, the SE model exhibits an encouraging performance, yielding results within a satisfactory level of accuracy. Major flow features and the cool flame-induced heat release characteristics can be satisfactorily reproduced, thus allowing the developed numerical tool to be used for design optimization of technical SCF diesel oil evaporation devices.

12.5 Outlook

The importance of cool flame reactions in a variety of technological applications (e.g., industrial safety, LTC engines) has only recently been realized. New technological concepts have appeared during the past decade, based on the nonigniting SCF phenomena. SCF devices are expected to significantly assist the widespread use of next-generation liquid fuels and biofuels, as they allow an active monitoring of the involved mass-momentum-thermochemical transport phenomena, ensuring optimal exploitation of the fuel’s heating value with minimization of pollutant formation in conventional (combustion) or novel (e.g. fuel cell) devices. However, open questions remain with regards to a detailed description of the low-temperature cool flame

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oxidative reactions of various hydrocarbon fuels. Advances in this region are expected to increase the accuracy of the available relevant numerical modeling techniques. A thorough understanding of cool flame thermochemical phenomena will benefit research efforts into novel technological concepts, such as liquid hydrocarbon reforming for fuel cells, HCCI internal combustion engines, or LPP gas turbine combustors.

12.6 Summary

The term “cool flames” is used to describe the low-temperature oxidative behavior of common hydrocarbon fuels. Cool flames are associated with two-stage ignition and “knocking” in ICEs, and are characterized by the NTC behavior, where the overall reaction rate is decreasing with increasing temperature. By exploiting this phenomenon as a “chemical barrier” for autoignition, it is possible to stabilize the cool flame reactions in an open, flowing system. The utilization of SCFs in a liquid fuel evaporation device results in an enhancement of the evaporation rate, producing a heated, well-mixed gaseous air–fuel vapor mixture that can be either fed into premixed combustion devices or utilized for reforming the fuel to a hydrogen-rich gas. The increasing recognition of the importance of cool flame phenomena in a large number of technological applications (e.g., liquid fuel reforming, LTC and HCCI internal combustion engines, LPP gas turbine combustion, industrial safety) may lead to a better understanding of the occurring thermochemical phenomena, thus allowing their accurate description using numerical modeling techniques.

References 1 Pekalski, A.A., Zevenbergen, J.F., Pasman,

2

3

4

5

H.J., Lemkowitz, S.M., Dahoe, A.E., and Scarlett, B. (2002) The relation of cool flames and auto-ignition phenomena to process safety at elevated pressure and temperature. J. Hazard. Mater., 93, 93–105. Perkin, W.H. (1882) Some observations on the luminous incomplete combustion of ether and other organic bodies. J. Chem. Soc., 41, 363–367. Emeleus, H.J. (1929) The light emission from the phosphorescent flames of ether, acetaldehyde, propaldehyde and hexane. J. Chem. Soc., 1733–1739. Minkoff, G.J. and Tipper, C.F.H. (1962) Chemistry of Combustion Reactions, Butterworths. Semenov, N.N. (1935) Chemical Kinetics and Chain Reactions, Oxford University Press.

6 Benson, S.W. (1981) The kinetics and

7

8

9

10

thermochemistry of chemical oxidation with application to combustion and flames. Prog. Energy Combust. Sci., 7, 125–134. Lignola, P.G. and Reverchon, E. (1987) Cool flames. Prog. Energy Combust. Sci., 13, 75–96. Griffiths, J.F. (1995) Reduced kinetic models and their application to practical combustion systems. Prog. Energy Combust. Sci., 21, 25–107. Pilling, M.J. (ed.) (1997) Low-Temperature Combustion and Autoignition, Comprehensive Chemical Kinetics, vol. 35, Elsevier, Amsterdam. Westbrook, C.K. (2000) Chemical kinetics of hydrocarbon ignition in practical combustion systems. Proc. Combust. Inst., 28, 1563–1577.

References 11 Morley, C. (1988) Photolytic

12

13

14

15

16

17

18

19

20

perturbation method to investigate the kinetics of hydrocarbon oxidation near 800 K. Proc. Combust. Inst., 22, 911–918. Curran, H.J., Gaffuri, P., Pitz, W.J., and Westbrook, C.K. (1998) A comprehensive modeling study of n-heptane oxidation. Combust. Flame, 114, 149–177. Kee, R.J., Rupley, F.M., and Miller, J.A. (1989) CHEMKIN II: A FORTRAN chemical kinetics package for the analysis of gas-phase chemical kinetics. Report No. SAND89-8009B, Sandia National Laboratories. Harrison, A.J. and Cairnie, L.R. (1988) The development and experimental validation of a mathematical model for predicting hot-surface autoignition hazards using complex chemistry. Combust. Flame, 71, 1–21. Cuoci, A., Mehl, M., Buzzi-Ferraris, G., Faravelli, T., Manca, D., and Ranzi, E. (2005) Autoignition and burning rates of fuel droplets under microgravity. Combust. Flame, 143, 211–226. Gray, B.G. and Felton, P.G. (1974) Lowtemperature oxidation in a stirred-flow reactor - I. Propane. Combust. Flame, 23, 295–304. Caprio, V., Insola, A., and Lignola, P.G. (1981) Isobutane cool flames in a CSTR: the behavior dependence on temperature and residence time. Combust. Flame, 43, 23–33. Griffiths, J.F., Halford-Maw, P.A., and Rose, D.J. (1993) Fundamental features of hydrocarbon autoignition in rapid compression machine. Combust. Flame, 95, 291–306. Dagaut, P., Reuillon, M., and Cathonnet, M. (1995) Experimental study of the oxidation of n-heptane in a jet stirred reactor from low to high temperature and pressures up to 40 Atm. Combust. Flame, 101, 132–140. Callahan, C.V., Held, T.J., Dryer, F.L., Minetti, R., Ribaucour, M., Sochet, L.R., Faravelli, T., Gaffuri, P., and Ranzi, E. (1996) Experimental data and kinetic modeling of primary reference fuel mixtures. Proc. Combust. Inst., 26, 739–746.

21 Cavanagh, J., Cox, R.A., and Olson, G.

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23

24

25

26

27

28

29

30 31

32

(1990) Computer modeling of cool flames and ignition of acetaldehyde. Combust. Flame, 82, 15–39. Hartmann, L., Lucka, K., and Koehne, H. (2003) Mixture preparation by cool flames for diesel-reforming technologies. J. Power Sources, 118, 286–297. Steinbach, N. (2002) Untersuchungen zum Zuendverhalten von Heizoel EL-LuftGemischen unter atmosphaerischem Druck, Ph.D. Thesis, RWTH - Aachen, Germany. Ballinger, P.R. and Ryason, P.R. (1971) Isolated stable cool flames of hydrocarbons. Proc. Combust. Inst., 13, 271–277. Edenhofer, R., Lucka, K., and Koehne, H. (2007) Low temperature oxidation of diesel–air mixtures at atmospheric pressure. Proc. Combust. Inst., 31, 2947–2954. Hartmann, L. (2002) Untersuchungen zu Kalten Flammen in der Unterstoechiometrie zur Realisierung eines Brennstoffzellenreformers fuer fluessige Brennstoffe, Ph.D. Thesis, RWTH - Aachen, Germany. Wittmer, A. (2003) Beitrag zur mathematischen Modellierung der Gemischaufbereitung fluessiger Brennstoffe in Luft, Ph.D. Thesis, RWTH - Aachen, Germany. Lucka, K. and Koehne, H. (1999) Usage of cold flames for the evaporation of liquid fuels. Proceedings of the 5th International Conference On Clean Air Technology, Lisbon, Portugal, pp. 207–213. Matos da Silva, J., Hermann, I., Mengel, C., Lucka, K., and Koehne, H. (2004) Autothermal reforming of gasoline using a cool flame vaporizer. AIChE J., 50 (5), 1042–1050. Glassman, I. and Yetter, R.A. (2008) Combustion, 4th edn, Academic Press. Peters, N., Paczko, G., Seiser, R., and Seshadri, K. (2002) Temperature crossover and non-thermal runaway at twostage ignition of n-heptane. Combust. Flame, 128, 38–59. Pitsch, H. and Peters, N. (1998) Investigation of the ignition process of sprays under diesel engine conditions

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33

34

35

36

37

38

39

40

using reduced n-heptane chemistry. SAE Transactions, Paper No. 982464. Griffiths, J.F. (2002) Autoignition chemistry, Lecture Notes, University of Leeds. Blin-Simiand, N., Rigny, R., Viossat, V., Circan, S., and Sahetchian, K. (1993) Autoignition of hydrocarbon/air mixtures in a CFR engine: experimental and modeling study. Combust. Sci. Technol., 88, 329–348. Sahetchian, K., Champoussin, J.C., Brun, M., Levy, N., Blin-Simiand, N., Aligrot, C., Jorand, F., Socoliuc, M., Heiss, A., and Guerassi, N. (1995) Experimental study and modeling of dodecane ignition in a diesel engine. Combust. Flame, 103, 207–220. Dagaut, P., Reuillon, M., and Cathonnet, M. (1994) High pressure oxidation of liquid fuels from low to high temperature. Combust. Sci. Technol., 95, 233–260. Silke, E.J., Curran, H.J., and Simmie, J.M. (2005) The influence of fuel structure on combustion as demonstrated by the isomers of heptane: a rapid compression machine study. Proc. Combust. Inst., 30, 2639–2647. Brehmer, T., Heger, F., Lucka, K., Schloss, J.V., Abu-Sharekh, Y., Trimis, D., Heeb, A., Koeb, G., Hayashi, T., Pereira, J.C.F., Founti, M., Kolaitis, D., Molinari, M., Ortona, A., Michel, J.B., and Theurillat, P. (2004) BIOFLAM project: application of liquid biofuels in new heating technologies for domestic appliances based on cool flame vaporization and porous medium combustion. Proceedings of the 2nd World Conference and Technology Exhibition on Biomass for Energy, Industry and Climate Protection, Rome, Italy, Paper V3.30. Kolaitis, D.I. and Founti, M.A. (2006) Numerical modelling of transport phenomena in a diesel spray “stabilized cool flame” vaporizer. Combust. Sci. Technol., 178, 1087–1115. Kolaitis, D.I. and Founti, M.A. (2006) A tabulated chemistry approach for numerical modelling of diesel spray evaporation in a “stabilized cool flame” environment. Combust. Flame, 145, 259–271.

41 Naidja, A., Krishna, C.R., Butcher, T., and

42

43

44

45

46

47

48

49

50

Mahajan, D. (2003) Cool flame partial oxidation and its role in combustion and reforming of fuels for fuel cell systems. Prog. Energy Combust. Sci., 29, 155–191. Aggarwal, S.K. (1998) A review of spray ignition phenomena: present status and future research. Prog. Energy Combust. Sci., 24, 565–600. Zhao, H. (ed.) (2007) HCCI and CAI Engines for the Automotive Industry, Woodhead Publishing Limited, Cambridge. Sohm, V., Kong, S.C., Foster, D.E., Morikawa, T., and Iida, M. (2004) A computational investigation into the cool flame region in HCCI combustion. SAE Transactions, Paper No. 2004-01-0552. Singh, S., Reitz, R.D., Musculus, M.P.B., and Lachaux, T. (2007) Simultaneous optical diagnostic imaging of lowtemperature, double-injection combustion in a heavy-duty DI diesel engine. Combust. Sci. Technol., 179, 2381–2414. Dec, J.E. (1997) A conceptual model of D.I. diesel combustion based on laser sheet imaging. SAE Transactions, 106 (3), Paper No. 970873, 1319–1348. Yamada, H., Suzaki, K., Tezaki, A., and Goto, Y. (2008) Transition from cool flame to thermal flame in compression ignition process. Combust. Flame, 154, 248–258. Gradinger, T.B., Inauen, A., Bombach, R., Kaeppeli, B., Hubschmid, W., and Boulouchos, K. (2001) Liquid-fuel/air premixing in gas turbine combustors: experiment and numerical simulation. Combust. Flame, 124, 422–443. Ohkubo, Y., Idota, Y., and Nomura, Y. (1997) Evaporation characteristics of fuel spray and low emissions in a lean premixed-prevaporization combustor for a 100 kW automotive ceramic gas turbine. Energy Conv. Manage., 38, 1297–1309. Cano Wolff, M., Meisl, J., Koch, R., and Wittig, S. (1998) The influence of evaporation on the autoignition-delay of n-heptane air mixtures under gas turbine conditions. Proc. Combust. Inst., 27, 2025–2031.

References 51 D’Onofrio, E.J. (1979) Cool flame and

52

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54

55

56

57 58

59

60

61

62

autoignition in glycols. Loss Prevent., 13, 89–97. Curran, H.J., Gaffuri, P., Pitz, W.J., and Westbrook, C.K. (2002) A comprehensive modeling study of iso-octane oxidation. Combust. Flame, 129, 253–280. Ranzi, E., Gaffuri, P., Faravelli, T., and Dagaut, P. (1995) A wide-range modeling study of n-heptane oxidation. Combust. Flame, 103, 91–106. Colin, O., da Cruz, A.P., and Jay, S. (2005) Detailed chemistry-based auto-ignition model including low temperature phenomena applied to 3-D engine calculations. Proc. Combust. Inst., 30, 2649–2656. Kong, S.C., Marriott, C.D., Reitz, R.D., and Christensen, M. (2001) Modeling and experiments of HCCI engine combustion using detailed chemical kinetics with multidimensional CFD, SAE Transactions, Paper No. 2001-01-1026. Tao, F., Golovitchev, V.I., and Chomiak, J. (2004) A phenomenological model for the prediction of soot formation in diesel spray combustion. Combust. Flame, 136 (3), 270–282. Peters, N. (2000) Turbulent Combustion, Cambridge University Press. Kanury, A.M. (1994) Introduction to Combustion Phenomena, Gordon and Breach Science Publishers. Gaffuri, P., Faravelli, T., Ranzi, E., Cernansky, N.P., Miller, D., d’Anna, A., and Ciajolo, A. (1997) Comprehensive kinetic model for the low-temperature oxidation of hydrocarbons. AICHE J., 43, 1278–1286. Yang, J.R. and Wong, S.C. (2003) On the suppression of negative temperature coefficient (NTC) in autoignition of n-heptane droplets. Combust. Flame, 132, 475–491. Poinsot, T. and Veynante, D. (2001) Theoretical and Numerical Combustion, R.T. Edwards Inc. Kolaitis, D.I. and Founti, M.A. (2009) Numerical simulation of diesel spray evaporation in a “stabilized cool flame” reactor: a comparative study. Flow Turbul. Combust., 82, 599–619.

63 Kolaitis, D.I. and Founti, M.A. (2009)

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66

67

68

69

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72

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On the assumption of using n-heptane as a “surrogate fuel” for the description of the cool flame oxidation of diesel oil. Proc. Combust. Inst., 32, 3197–3205. Montgomery, C.J., Cremer, M.A., Chen, J.Y., Westbrook, C.K., and Maurice, L.Q. (2002) Reduced chemical kinetic mechanisms for hydrocarbon fuels. J. Propul. Power, 18, 192–198. Mati, K., Ristori, A., Gail, S., Pengloan, G., and Dagaut, P. (2007) The oxidation of a diesel fuel at 1–10 atm: experimental study in a JSR and detailed chemical kinetic modeling. Proc. Combust. Inst., 31, 2939–2946. Hernandez, J.J., Sanz-Argent, J., Benajes, J., and Molina, S. (2008) Selection of a diesel fuel surrogate for the prediction of auto-ignition under HCCI engine conditions. Fuel, 87 (6), 655–665. Ranzi, E. (2006) A wide-range modeling study of oxidation of transportation fuels and surrogate mixtures. Energy and Fuels, 20, 1024–1032. Battin-Leclerc, F. (2008) Detailed chemical kinetic models for the low-temperature combustion of hydrocarbons with application to gasoline and diesel fuel surrogates. Prog. Energy Combust. Sci., 34, 440–498. Simmie, J.M. (2003) Detailed chemical kinetic models for the combustion of hydrocarbon fuels. Prog. Energy Combust. Sci., 29, 599–634. Liu, S., Hewson, J.C., Chen, J.H., and Pitsch, H. (2004) Effects of strain rate on high-pressure nonpremixed n-heptane autoignition in counterflow. Combust. Flame, 137, 320–339. Tanaka, S., Ayala, F., and Keck, J.C. (2003) A reduced chemical kinetic model for HCCI combustion of primary reference fuels in a rapid compression machine. Combust. Flame, 133, 467–481. Zheng, J., Miller, D.L., and Cernansky, N.P. (2004) A global reaction model for the HCCI combustion process. SAE Transactions, Paper No. 2004-01-2950. Founti, M.A. and Kolaitis, D.I. (2005) Numerical simulation of diesel spray

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evaporation exploiting the “stabilized cool flame” phenomenon. Atomization Spray, 15, 1–18. 74 Katsourinis, D.I. and Founti, M.A. (2008) CFD modelling of a “stabilized cool flame” reactor with reduced mechanisms and a direct integration approach. Chem. Eng. Sci., 63, 424–433. 75 Clifford, L.J., Milne, A.M., Turanyi, T., and Boulton, B. (1998) An induction parameter model for shock-induced hydrogen combustion simulations. Combust. Flame, 113, 106–118. 76 Klipfel, A., Founti, M., Zaehringer, K., and Petit, J.P. (1998) Numerical simulation and experimental validation of the turbulent combustion and perlite expansion processes in an industrial perlite expansion furnace. Flow Turbul. Combust., 60, 283–300.

77 Founti, M.A., Katsourinis, D.I., and

Kolaitis, D.I. (2007) Turbulent sprays evaporating under “stabilized cool flame” conditions: assessment of two CFD approaches. Numer. Heat Tr. B-Fund., 52, 51–68. 78 Kolaitis, D.I. and Founti, M.A. (2006) A comparative study of numerical models for eulerian-lagrangian simulations of turbulent evaporating sprays. Int. J. Heat Fluid Flow, 27, 424–435. 79 Yakhot, V., Orszag, S.A., Thangam, S., Gatski, T.B., and Speziale, C.G. (1992) Development of turbulence models for shear flows by a double expansion technique. Phys. Fluids A, 4, 1510–1520. 80 Sommerfeld, M. and Qiu, H. (1998) Experimental studies of spray evaporation in turbulent flow. Int. J. Heat Fluid Flow, 19, 10–22.

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13 Industrial Steam Boilers Jorge Barroso, F
elix Barreras, Javier Ballester, and Norberto Fueyo

13.1 Introduction: Principles and Technology

Ever since the emergence of the steam engine during the dawn of the nineteenth century, power generation and the development of mankind have been very closely linked to steam production and boilers. In a boiler, the chemical energy stored in a fuel is transformed into thermal energy for domestic and industrial applications. The steam produced in industrial boilers can be used in steam turbines for both electric and mechanical power generation, and also to provide the heat demanded in industrial processes. The early steam boilers consisted of riveted cylindrical containers that were partially filled with water and externally heated. Steam boilers further evolved in two main directions in order to increase the pressure, temperature, and quality of the steam produced, as well as the efficiency of the heat exchange: .

.

Fire-tube boilers: These consist of a cylindrical vessel in which the heat source is held inside a furnace (or firebox). In order to maintain the temperature of the metal well below its melting point, almost the whole volume of the container is filled with water, leaving just a small volume above certain barrels that will accommodate the steam generated. Water-tube (or water-wall) boilers: In these boilers, the water tubes are arranged inside a furnace in a number of possible configurations. The water tubes connect the upper and lower drums, such that a mix of water and steam moves along them, absorbing heat from the combustion gases that circulate outside.

This chapter focuses on the basics of, and current trends in, industrial water-wall boilers, with special attention being paid to the major research efforts that have allowed their rapid development; these include computational fluid dynamics (CFD)based techniques for boiler design, combustion diagnostics, and control systems. Finally, a brief overview of the technology is provided, addressing recent and emerging developments that have been aimed at increasing the efficiency of steam boilers, as well as reducing their environmental impact.

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright
2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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13.1.1 Boiler Characteristics and Classification

The different parts and elements that constitute a coal-fired industrial steam boiler are depicted in Figure 13.1. In general, the operational principle of a steam boiler can be analyzed by following the route of the different substances circulating through the boiler’s several elements. In this chapter, in order to facilitate description, the water-steam and air-fluegas systems are dealt with separately. In the water-steam subsystem, the main feeding pump provides sufficient energy for the water to reach the drum, after crossing the economizer, with a suitable (design) pressure. The economizer, which is the second to last heat transfer surface along which the hot flue gases pass, consists of a set of serpentine tubes. Water flowing through these tubes is heated to a temperature close to saturation, by the conduction of heat through tube walls. The drum consists of one or several cylindrical vessels, depending on the steam capacity of the plant; these are placed horizontally in the upper part of the boiler, which may have a diameter of 1.5–1.8 m and a length of 15–20 m. When half-filled with water, the function of the drum is to feed water to the lower collectors of the evaporative water-walls, to receive the steam-water mixture produced along them, to separate steam from water, and to allow for the steam to flow to the superheaters. Depending on the range of design parameters, and on the circulation modes in the evaporating systems, there are three main types of water-wall steam boiler, namely the natural-circulation, forced-circulation, and continuous-circulation boiler. In natural-circulation boilers, the circulation of water and steam takes place within a closed circuit, driven mainly by differences in their densities. Water contained

Figure 13.1 Schematic of a high-pressure coal-fired industrial steam boiler.

13.1 Introduction: Principles and Technology

in the lower-half of the drum flows down to the lower collectors through the supply tubes, ascending subsequently through the water-wall tubes. During its circulation through the water-wall tubes, the water absorbs heat radiated by the combustion process in the furnace and is partially transformed into steam, reaching the drum as a water–steam mixture. This type of boiler is widely used when the operating steam pressure ranges from 6 bar up to 183 bar. As the pressure increases, the density differences will be reduced, as is the available gravitational force required to overcome the pressure drop along the circuit. In this situation, a circulating pump is introduced into the system, thus modifying the evaporating circuit and enhancing the amount of heat transferred to the water. These pump-assisted circulating (forced-circulation) boilers are typically used when the steam pressure ranges from 15 MPa to near-critical. Finally, when the steam is produced in the range of critical and supercritical pressures, a continuous-circulation boiler is used. In these boilers, the water and steam circulate only once through the different heat transfer surfaces, which causes the water to be converted completely into steam in the evaporative tubes. The main structural difference between continuously circulating boilers and their natural- and forced-circulation counterparts is the absence of drums, because separation of the steam and water is not required. Different separation systems are incorporated into the drums to achieve the extraction of saturated steam with the appropriate dryness to be sent to the superheaters. In the superheaters, the steam absorbs heat from the combustion gases both by radiation and convection, depending on their location in the furnace, and increasing its temperature up to 500–700
C. In order to produce electricity, superheated steam exits the boiler and is supplied to the high-pressure steam-turbine stage. The exhausted steam is further transported to the intermediate reheaters, placed just downstream of the superheaters along the flue-gas path, where the lowpressure steam is reheated to the parameters required for the medium- and lowpressure stages of the steam turbine. The air–gas subsystem begins at the air supply fan that moves the air through the air heater, where ambient air is heated to 300–400
C, to the boiler combustionsystem. Air heaters are classified according to their operating principle as either recuperative (tubular) or rotary regenerative. Fuel and hot air reach the combustion system where all the processes required to cause the combustion reactions, and also to maintain the flame in the furnace, take place. All of the heat that is transferred to the water, or to the steam, through the different heat transfer surfaces is produced during the combustion process. The furnace is the zone designed for combustion of the fuel, and should be large enough to allow for complete combustion of the fuel, without the flame impinging on the walls. Within the furnace, the walls and roof are normally covered with steel tubes with circulating water and steam; these are called the “water-walls.” Refractory materials are used to cover the outside part of the water-walls so as to reduce heat losses and air leakage. The transfer of heat from the fire to these tubes (a combination of radiant and convective heating) causes the water to boil, thus producing steam. The flue gases leave the furnace at high temperature, and are then flowed around the

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remainder of the heat-recovery surfaces of the boiler. These are (following the fluegas pathway): the superheaters; the reheaters; the economizer; and the air preheater. The gas cleaning system consists of different elements depending on the pollutant which is to be abated. Extensive descriptions of several cleaning systems are provided in Vol. 2 Ch. 11–17 of this Handbook. The circulation of air and flue gases through the heat-transfer surfaces is facilitated by both the gas extraction-system and by air-circulation fans. When the air-supply fan overcomes the total pressure drop in the air system (in such a way that the pressure at the furnace exit is slightly lower than the ambient pressure), and the draft caused by the combination of the gas extraction fan plus the smokestack exceeds the rest of the pressure drop in the circuit, the boiler is said to be “draft-balanced.” Conversely, the boiler has a “pressurized furnace” if the air-supply fan overcomes the pressure drop induced by the air circuit, as well as that of the flue-gas path until the economizer exit. The main characteristics and details of state-of-the-art boilers can be found elsewhere [1–6]. 13.1.2 Combustion Systems

For combustion systems, there are main two ways to burn fossil fuels, namely in suspension or over beds. 13.1.2.1 Bed Combustion Systems Bed combustion is normally used for solid fuels, and can be further classified into either layer-bed or fluidized-bed combustion. When a layer-bed technology is used in the furnace the grate may be stationary (horizontal, inclined, tilting, etc.), vibratory, or moving [7]. The fuel in these boilers enters the furnace through several fuel chutes, and is spread using either mechanical or pneumatic spreaderstokers. Although small and light fuel pieces are burned in suspension, the larger fragments are spread in a thin, even layer on the bed. After combustion, the ash is removed from the combusting bed at the opposite side, or by tilting the grate. In fluidized-bed combustion systems, an initially stationary bed of noncombustible solid particles (usually silica sand), located at the bottom part of the furnace, is brought to a fluidized state by the primary air that is supplied through a distributor plate. In a bubbling fluidized-bed combustor (BFBC), the bed particles are held in suspension by the primary air at comparatively low fluidization velocities, whereas in a circulating fluidized-bed combustor (CFBC), higher gas velocities (normally sixfold that of the BFBC) are employed. Solid fuel particles are supplied into the furnace by the feeders, causing an intense mixing with the hot bed solids. As the noncombustible granular solid particles outnumber the solid fuel particles, regardless of the initial condition with which the fuel is supplied, the bed solids easily increase the fuel particle temperature above that of their devolatilization, with no significant drop in their own temperature. Subsequently, the ignited fuel particles will begin to transfer back their combustion heat to the bed of solid particles at the same high rate.

13.1 Introduction: Principles and Technology

Secondary air is occasionally introduced at the top of the bed (splashing zone) and further upstream (this is sometimes called tertiary air) through several inlet ports that are well distributed over the width of the boiler. The temperature is normally in the range of 800–900
C, and is controlled by an internal heat exchanger. CFBCs generally have higher efficiencies than BFBCs, due to their higher fluidization velocities. Depending on the operating pressure, these systems can be either atmospheric (AFBC) or pressurized (PFBC). When this combustion technology is used, different reacting substances can be added to the bed in order to reduce the formation of contaminants during the combustion process. For example, if limestone or dolomite is supplied to the combusting bed, these quickly react with the SO2 in the combustion gases to form solid sulfates that are extracted from the furnace with the ash, and consequently reducing the emission of this gaseous pollutant to the atmosphere. Simultaneously, the decrease in combustion temperature also reduces the formation and emission of NOx to the environment. The characteristics of fluidized-bed systems are reviewed in Refs [8, 9] for coals and biomass, respectively. 13.1.2.2 Suspension Combustion Systems The alternative approach to burning a solid fuel is in suspension, which is feasible if the fuel particles are small enough to be burned while floating in the combustion chamber (as noted above). The use of these systems has allowed an increase in the nominal power of steam boilers with respect to that obtained in bed-combustion systems, which are limited by the size of the grate. However, in order to burn solid fuels in suspension, an entirely different system of fuel preparation is required which comprises milling, separation, and transportation of the fuel, while ensuring the a suitable particle size and humidity. Similar furnaces are also used to burn gaseous and liquids fuels. Gaseous fuel preparation requires safe transportation from the storage tanks to the burners, due to the dangers of toxicity and explosion. Since, in gas-fueled boilers, the gas pressure changes with the load, the supply system must include pressure-regulation controlloops. Both, internal mixing (premixed) and diffusion gas burners can be used for this purpose. In the premixed type, the ambient air and gaseous fuel are fully mixed at the entrance to the burner, whereas in a diffusion burner the gaseous fuel and air emerge via different paths and are mixed inside the furnace. In an intermediate technology, which is used in some industrial facilities, a small part of the reacting fluids is partially mixed at the inlet to the burner, with the final mixing being completed inside the furnace. The selection of an adequate type of burner opens a wide range of possibilities for regulating the strength of the radiative heat transfer, depending on any given operational conditions. In order to enhance the turbulent mixing between air and gaseous fuel, and to anchor the flame, the gas burners are fitted with a set of directing vanes and baffles that are designed specifically to induce a swirling motion in the air flow; this results in the formation of eddies and in flameanchoring recirculation-regions. Moreover, gas-combustion systems are sometimes equipped with elements that allow the simultaneous combustion with either solid or liquid fuels.

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The increasing consumption of petroleum-derived liquids as fuels for both industrial and automotive applications has caused a reduction in the quality of the residual oils, which are becoming heavier. This quality reduction translates into lower heating values, but above all, into higher levels of viscosity, asphaltenes and Conradson carbon. The present global scenario, characterized by a probable increase in the price of regular fuel oils and scarce reserves of these oils compared to bituminous ones, has motivated the switching to heavy fuel oil in most utility boilers. The combustion systems for these heavy fuel oils require one or two heating stages in order to reduce their viscosity, thus easing the transportation, filtering, and atomization processes. Heating is normally achieved by low-pressure steam heat exchangers, that require an operating temperature of up to 130
C when very heavy paraffin-type petroleum or native crude oils are used. Atomization is a key process in the performance of liquid fuel-fired combustion systems [10]. New technologies and improvements to existing fuel-oil supply systems are thus needed in order to produce sprays for heavy fuel oil with the same quality as those that were generated when lighter oils were used. The adequate atomization of this type of heavy fuel oils is very difficult, and has become a challenge for both research groups and engineers alike. The correct combustion of a spray of liquid fuel involves the volatilization, as rapidly as possible, of the small droplets; for this, the furnace temperature is a key factor, and (similar to gas-fueled systems) an intense mixing between droplets and combusting air must also be achieved. In practice, there are many ways to generate a spray using, for example, spinning cups [11], pressure swirl [12], fan [13], ultrasonic [14] or effervescent atomizers [15], forming solid or hollow cones of droplets (see Figure 13.2). However, for large-scale facilities where large flow rates of very viscous fuel oils need to be handled, the number of methods capable of providing a reasonable combustion efficiency is substantially reduced. In these situations, one of the nozzle types most commonly used is the steam-assisted type with a “Y” configuration [16–18], which can be operated by keeping either a constant steam-to-fuel flow-rate ratio or a fixed fuel-tosteam pressure ratio. This nozzle generally consists of a number of jets (from 2 to 20) arranged in a ring-shaped layout to generate a hollow, conical spray. In each

Figure 13.2 Some typical fuel oil burners used in industrial boilers. (a) Oil-pressure; (b) Twin-fluid; (c) Rotary cup.

13.2 Boiler Design and Diagnostics

individual “Y”, the fuel oil is injected at an angle into the exit port, where it mixes with the atomizing fluid (steam). One drawback of these nozzles when atomizing and burning heavy fuel oil or crude petroleum is that, in order to produce a fine spray, a relatively large amount of steam (usually at high speeds) is needed, and this causes a decrease in the flame temperature, and in some cases may prevent the reignition of the mixture. The intense interaction with the turbulence field induces a large strain rate at the flame front that can lead to local flame extinction, and may also produce a flame elongation that frequently results in contact with the boiler walls. As the reaction zone is cooled down, the reaction times become longer than the mixing times, while local flame extinction results in the production of polycyclic aromatic hydrocarbons (PAH; see also Vol. 2 Ch. 16) that eventually leads to soot formation [19] (see also Vol. 2 Ch. 11 and Ch. 15). In principle, the flame-temperature reduction can decrease the thermal NOx formation, which is highly sensitive to temperature. However, heavy fuel oils and crude petroleum have also substantial sulfur, sodium, and vanadium contents, such that the large amount of steam in the combustion chamber may lead to the formation of alkali sulfates and vanadium salts that cause high-temperature corrosion [20], and sulfuric acid that corrodes the boiler lowtemperature heat-transfer surfaces [21]. A new-concept nozzle that overcomes the main drawbacks (as discussed above) of industrial “Y” atomizers has a suitably designed internal mixing chamber. The benefits of this new nozzle, in which a reduction in droplet size is achieved with a simultaneous decrease in the amount of auxiliary fluid needed, has been demonstrated for coal–water mixtures [22, 23], and for native crude petroleum [24, 25].

13.2 Boiler Design and Diagnostics 13.2.1 Conventional Boiler Design

The key parameters for a new boiler design include power, pressure, temperature, and dryness of the steam, type and properties of the fuel, as well as the form of combustion technology to be used, among others. Design calculations start with the geometric configuration of the furnace, as well as the gas ducts, where the different heat transfer surfaces that configure the heat recovery scheme are arranged. Thermal, aerodynamic, hydrodynamic and mechanical calculations start by assuming an initial configuration for the heat transfer system, as well as the overall configuration for the steam boiler. The several systems that ensure the safe operation of the boiler are then designed, namely fuel preparation and supply, water-steam drying, steam temperature control, and boiler load regulation. The geometric configuration of the furnace is designed to ensure that, depending on the fuel characteristics, its thermal load per unit volume (qvp) and surface (qfp) are below certain recommended values. The usual values for a boiler producing up to 75 t h1 of steam are, as an example, presented in Table 13.1.

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Table 13.1 Recommended values for the key parameters for steam boiler design.

Fuel

qvp (kW m3)

qfp (kW m2)

ah

q3 (%)

q4 (%)

tah (
C)

tf (
C)

Solids Liquids Gaseous

140–465 290 350–470

930–1630 9300 9200

1.2–1.25 1.1 1.1

0 0.5 0.5

0.5–6 0 0

250–300 250–300 250–300

800–1250 800–1250 800–1250

Recommendations for other important parameters in the furnace design are also included in Table 13.1, such as the furnace stoichiometric ratio (ah), chemical and fixed carbon losses (q3 and q4), and air-heater and furnace-exit gas temperatures (tah and tf, respectively). The amount of air needed for combustion, and the volumetric flow rate of hot gases produced, are key parameters for the thermal calculation of the rest of the heat transfer surfaces. A description of the chemical reactions that take place during combustion can be found in Chapter 1 and in Ref. [26]. The equations needed to calculate the volume of air and gaseous combustion products in the steam furnace are summarized in Table 13.2 (modified from Ref. [27]). Here, Cp, Hp, Op, Np, and Sp are the mass fractions of carbon, hydrogen, oxygen, nitrogen, and sulfur, respectively, Wp is the mass fraction of water in solid or liquid fuels (fuel humidity), and Wg is the humidity content (in mg Nm3) for gaseous fuels. Likewise, CO2, CO, H2, H2S, O2, CmHn, are the volumetric fractions of carbon dioxide and carbon monoxide, hydrogen, hydrogen sulfide, oxygen and hydrocarbons (methane, ethane, propane, etc.). Finally, d is the air absolute moisture, and Gat is the steam-to-fuel mass ratio used in the atomization process. The thermal calculation is an iterative process, which starts by assuming a given exhaust gas temperature. With this temperature, the exhaust gas loss (q2),

Table 13.2 Volume of air and combustion products per unit weight or volume of fuel.

Solid and liquid fuels (Nm3 kgfuel1) Dry air, theoretical

Vat;dry ¼ 8:89ðCp þ 0:375 Sp Þ þ 26:5 Hp 3:33 Op

Dry air, actual

Var;dry ¼ a Vat;dry

Moist air, actual

Var;moist ¼ Var;dry ð1 þ 1:62 dÞ

Dry gases

Vg;dry ¼ 1:866 Cp þ 0:7 Sp þ 0:8 Np þ 0:79 Vat;dry þ ða1Þ Vat;dry Vg;moist ¼ Vg;dry þ 11:1 Hp þ 1:62 dVar;dry þ 1:244ðWp þ Gat Þ

Moist gases

Gaseous fuels (Nm Nm3fuel1) 3

Dry air, theoretical

   P
m þ n4 Cm Hn Vat;dry ¼ 4:76 0:5ðCO þ H2 Þ þ 1:5 H2 SO2 þ

Dry air, actual

Var;dry ¼ a Vat;dry

Moist air, actual

Var;moist ¼ Var;dry ð1 þ 1:62 dÞ

Dry gases

Vg;dry ¼ CO2 þ CO þ H2 S þ

Moist gases

Vg;moist ¼ Vg;dry þ H2 þ H2 S þ

P

mCm Hn þ N2 þ 0:79 Vat;dry þ ða1Þ Vat;dry Pn 2 Cm Hn þ 1:62 dVar;dry þ 1:244 Wg

13.2 Boiler Design and Diagnostics

thermal efficiency (g), and fuel consumption (B) are calculated using the set of equations: q2 ¼

Heg Hca ð100q4 Þ ð%Þ Qd

ð13:1Þ

q5 ¼ 0:8180:128  102 D þ 0:64  106 D2 þ 0:135  109 D3

ð%Þ

g ¼ 100ðq2 þ q3 þ q4 þ q5 Þ ð%Þ B¼

Qyield g Qd 100

ð13:2Þ ð13:3Þ

ðkg s1 Þ

ð13:4Þ

where Heg and Hca are the exhaust gas and cold air enthalpies (kJ kgfuel1), Q d is the total heat introduced to the furnace (i.e., the sum of the fuel heating value, and the sensible heat carried by the fuel, air, steam, etc.; kJ kgfuel1), Qyield is the power produced by the boiler (kW), and q5 is the conduction heat loss, which is determined as a function of the steam produced by the boiler (D, in t h1), for steam productions between 100 and 900 t h1. A constant value of 0.2 is assumed for q5 for a steam production above 900 t h1. The absolute temperature of the hot gases at the furnace exit (TH,exit) is then calculated [28] using the equation: TH;exit ¼

 M

Ta 5:67 FF YF aF Ta3 1011 j B CF

0:6

þ1

ðKÞ

ð13:5Þ

as a function of the adiabatic flame temperature (Ta), the relative height of the maximum temperature zone (M), the total furnace area (FF), the wall thermal effectiveness (yF), the thermal emission of the furnace (aF), the fuel consumption (B), the coefficient for heat conservation (j), and of the average specific heat of hot gases in the furnace (CF ). Since the average specific heat depends on gas temperature at the furnace exit, this is an iterative calculation. The calculation of the rest of the heat transfer surfaces is performed using energy balances and heat transfer equations. The heat released by the hot gases in each heat transfer surface (i) is determined by: Qi ¼ BðHg in i Hg out i Þ

ðkWÞ

ð13:6Þ

The heat absorbed by the water at the economizer, and by the steam in the superheaters and reheaters is calculated as Qi ¼ Di ðhout i hin i Þ ðkWÞ

ð13:7Þ

and the heat absorbed by the air in the air pre-heater is determined as Qah ¼ BðHa out Ha in Þ ðkWÞ

ð13:8Þ

In these equations, H represents the gas and air enthalpies (kJ kgfuel1), while hi are the enthalpies of the water in the economizer and of the steam in the superheaters (kJ kg1). B and D are the mass flow rates of fuel and water or steam, respectively.

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Table 13.3 Global heat transfer coefficients (W m2 K1).

Fuel

Tube array

Heat transfer surface

Solid

Triangular

Economizers, generating tubes, supercritical superheaters and reheaters High- and medium-pressure superheaters and reheaters Generating tubes, festoon, economizers Smooth tube exchangers

Square

Gases and liquids All

Triangular and square —

Recuperative tubular air heaters Rotary regenerative air heaters

Equation 1 k ¼ 1 þaea 1

k¼

 a1  1þ

e þ a1

2

a1

k ¼ y a1 k ¼ y aa1 1þaa2 2 k ¼ j aa1 1þaa2 2 k¼

1 xg a1

j þx

1 a a2

The heat transfer equation for each surface is: Qi ¼ k Fi DT log;i

ðkWÞ

ð13:9Þ

where Fi is the heat exchange area, DT log;i is the average logarithmic temperature difference in the given heat transfer surface, and k is the global heat transfer coefficient, which can be determined using the equations summarized in Table 13.3 as a function of the type of fuel and surface [3, 29]. Here, e, j and y are the soiling (including fouling and slagging), thermal usage, and thermal effectiveness coefficients. They are mainly dependent on the type of fuel, the heat transfer surface and the gas velocity. xg and xa are the cross-sectional areas occupied by the gas and air, respectively. Finally, a1 and a2 are local heat transfer coefficients for the gas and the heated fluid at each heat transfer surface. In the high-temperature zone, a1 is calculated as a function of both radiative and convective heat transfer coefficients. Total and local heat transfer coefficients can be determined using the set of equations recommended elsewhere [3, 26, 29]. The calculation for each heat transfer surface is also an iterative process, where the exit temperature of the gas is assumed, and calculations are repeated until a balance of the heat released by hot gas and the heat absorbed by the substance involved is obtained, including in the analysis the actual heat transfer area of the equipment. Once the calculations have been performed for the last heat transfer surface, which depends on the selected heat-recovery scheme, the exhaust gases temperature of the boiler is obtained. As the value of this latter parameter has been initially assumed to determine both the heat exhaust gases loss and the fuel consumption, the iterative process ends when the difference between the assumed and calculated temperatures is negligible. A complete discussion on heat transfer processes is included in Chapter 5. The working conditions for the water or steam flows are established for each heat transfer surface by hydrodynamic calculations [30]. In the case of the economizer, the

13.2 Boiler Design and Diagnostics

Figure 13.3 Schematic representation of a water-steam circulation in water-walls.

optimal operational point for the pumping system, the pump power, pressure losses and water velocity are determined. On the other hand, the velocity, pressure drop and pressure of the vapor phase are calculated for both superheaters and reheaters. In boilers operating in the subcritical range (natural circulation boilers), circulation is induced by the natural draft due to differences in density of the fluid in the downcomer (down-flow) and riser (up-flow) circuits, as depicted in Figure 13.3. In the down-flow tubes, saturated water (according to the drum conditions) circulates to the lower collectors, where the pressure is high due to the liquid column, and the water is subcooled. From the lower collectors, the water circulates through ascending tubes, absorbing heat until the saturation condition is attained at a given height, He. From this height, the amount of steam in the mixture increases, reaching its maximum at the discharge point in the drum. The available gravitational force is calculated using: PM ¼ g Hv ðrLS  rM Þ ðPaÞ

ð13:10Þ

M, are the densities for the where HV is the steam height (Hv ¼ Hdis  He), and rLS y r saturated water and water-steam mixture, respectively. The pressure drops in each circuit, i, are calculated as a function of the average density (r) and fluid velocity (V): Dpi ¼

   2i i V L X r f þ K d i 2

ðPaÞ

ð13:11Þ

P where f is the friction coefficient, K is the sum of all minor head losses, and L and d are the length and diameter of the circuit, respectively. The friction and minor head losses can be determined from the recommendations in Refs [1, 26, 31], or using most fluid mechanics textbooks.

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The operating point – that is, the condition where head losses equal the available buoyancy force – is calculated in an iterative process using the fluid velocity as the iterating variable. When convergence has been achieved, it must be also verified that adequate circulation regimes are established inside the tubes at the lowest thermal load of the circuit. Aerodynamic design is used to calculate pressure losses in gases and air circuits, thus determining the power required for the inducing and forcing fans [32]. The total head (HpF) to be overcome by fans can be calculated from the equation: HpF ¼ p2 p1 þ gðZ2 r2 Z1 r1 Þ þ

r2 V22 r1 V12 þ DpT HpStack 2

ðPaÞ ð13:12Þ

where p, Z, r, and V refer to the average values of pressure, height, density, and velocity at the entrance section (1) and at the exit section (2) of the gas circuit. DpT considers the total pressure losses along the circuit, including the head loss in conducts, as well as that imposed by the different heat transfer surfaces, and can be determined with Equation 13.11. The contribution of the smokestack to the draft of hot gases, HpStack, is only calculated for the gas extraction fan as a function of the stack height (HStack): HpStack ¼ g HStack ðra rg Þ ðPaÞ

ð13:13Þ

where ra and rg are the average air and hot gases densities at the smokestack, respectively. Finally, an appropriate mechanical design is required to ensure that the materials used for tubes, drums, collectors, and supporting structures can withstand the pressures, weight-loads and working temperatures. Some other special systems are also needed to absorb the thermal elongation [33]. Results obtained from different methodologies for designing boilers have been compared with experimental data by several authors, and have shown reasonably good agreement [34, 35]. 13.2.2 CFD Methods for Boiler Design

Computational fluid dynamics is the numerical solution of the equations (usually differential, sometimes also integro-differential) that govern fluid flow, heat transfer and chemical reactions. Initially, CFD was applied to boiler design in the first comprehensive (albeit crude) multidimensional model of a combustion chamber [36]. Historically, the intricacy of the geometry of steam boilers for power generation has posed exacting demands on the numerical model. For example, if the smallest relevant geometric feature of a boiler (e.g., the diameter of a primary duct) was on the scale of a few centimeters (e.g., 0.1 m), and the overall dimension of the boiler was on the scale of 100 m, then the mesh requirements to resolve both scales would be in the range of (100/0.1)3; that is, millions of cells, even if the additional (smaller) scales introduced by the turbulence or the multiphase nature of the flow are ignored.

13.2 Boiler Design and Diagnostics

Hence, it has been only recently, through the combination of growing computer power and algorithmic improvements, that CFD has increasingly found its way into the engineering toolbox for boiler design, operation, and retrofitting. Whilst a large part of the effort has been dedicated to modeling of the combustion chamber (the furnace), most of the boiler subsystems are amenable to analysis using CFD; these include, for instance, the air preheaters; the coal mills and primary ducts in coal boilers; the windbox; the heat-recovery zone; and the post-treatment of the combustion gases, including electrostatic precipitators [37] and other cleaning devices (e.g., flue gas desulfurization [38]). 13.2.2.1 The Furnace The modeling of flow, combustion and heat transfer in the furnace is perhaps the most widely recognized application of CFD to boilers. The practice is, however, strenuous because of the compounded effect of the geometric complexity (as outlined above) and the intricate nature of physical phenomena. These include, notably: multiphase flows; turbulence; chemical reactions, perhaps encompassing pollutant formation; multimodal heat transfer (including radiation); and, of course, the interaction between these parameters such as particle dispersion by the turbulent flow; particle–radiation interaction; heterogeneous chemical reactions on the particle surface; or the coupling between turbulence and chemical reactions. The flow in the combustion chamber of most utility steam boilers is multiphase, with the fuel being supplied as either a liquid (e.g., oil) or solid (e.g., coal or biomass). The mathematical representation of such flow takes place either via Lagrangian equations that track the motion of the particle through the combustion chamber, or via Eulerian equations that treat the dispersed phases as continua, using similar equations to those used for the continuous phase (the gas). Lagrangian particle methods are perhaps the most widely used, since Crowe and coworkers reported [39] their Particle-source-in-cell (PSI-CELL) method. This provided a simple means of incorporating into mainstream, hitherto-single-phase CFDcodes, the crucial particle effects (such as the source of volatile gas), thus achieving full coupling between the phases. Initially, modeling the dispersion of particles by turbulence was a major challenge, as often only time-average gas-fields were calculated, although this was soon remedied with the random-walk methods introduced by Gosman and Ioannides [40] that emulate the pseudorandom deflection of the trajectory as the particle crosses a turbulent eddy. Eulerian multiphase methods treat all phases (whether continuous or disperse) as interpenetrating continua. Albeit, the corresponding equations are well established, at least in their essential features, their numerical solution was hindered by the fact that the several phases, each possibly with a different local velocity, shared the same pressure field, and the same overall mass-conservation equation. This problem was akin to, if only more complex than, the pressure–velocity coupling in single-phase flows, for which Patankar and Spalding had proposed the well-known SIMPLE algorithm [41]. Spalding subsequently developed a multiphase extension, the interphase slip algorithm (IPSA) [42], which has since become the workhorse of Eulerian multiphase methods in commercial CFD software.

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Lagrangian methods are usually preferred as the multiphase model for steam boilers. The reason for this is that they are more economical for a large number of different particles (e.g., those with different diameters); the implementation of physical models (e.g., pyrolysis) is simpler; and the treatment of the dispersed phase is free from numerical diffusion. On the other hand, Eulerian models have been successfully applied to utility boilers in [43], who presented an Eulerian framework with all the relevant particle submodels, as used to simulate NOx formation in an arch-fired boiler [44]. One of the main difficulties with Eulerian models is in calculating the local particle size, which is an intrinsically Lagrangian property. For this, Spalding proposed the SHADOW method [45], which subsequently was generalized to coal combustion by Fueyo and coworkers [46]. Eulerian methods are often the first technical choice when the disperse phase is very dense (in the sense of nondiluted), such as in fluidized beds (see below). 13.2.2.2 Furnace Submodels Whether Eulerian or Lagrangian equations are used as a framework, the successful simulation of a utility furnace requires a plethora of interacting submodels for each of the complex processes that take place in the chamber. Whilst today, rather sophisticated submodels exist for most of these processes, their usefulness is no doubt hampered by the lack of mesh resolution, and usually also by the uncertainty in the physical properties of the fuel and the medium, and by a lack of validation in largescale applications. While turbulence is a key phenomenon in the prediction of mixing (and hence heat transfer and chemical reaction) in the furnace, its modeling is today still achieved via time-averaging, and resolved by resorting to the 1970s k-epsilon model or to one of its more recent variants. The use of large eddy simulation (LES) for large-scale furnaces is beyond the power of present-day computers; however, it can be used for the modeling of single burners, and laboratory (research-level) flames [47]. Particle processes are paramount for the accurate simulation of solid-fired furnaces. The modeling of solid-fuel particle (coal or biomass) often hinges around a four-component scheme since, at a given time during its flight across the furnace, the particle is assumed to be composed of raw fuel, moisture, fixed carbon, and mineral matter. These all evolve (perhaps with the exception of the mineral matter) as the particle is heated up by conduction and radiation. Crucial to any practical application of CFD for furnaces is the representation of fuel pyrolysis (or devolatilization) and burnout (or heterogeneous combustion). Pyrolysis is frequently modeled through empirically derived kinetic rates which, for a given coal rank, can be either a single rate or several competing or participating rates (e.g., a continuous distribution of activation energies). More sophisticated models, such as the chemical percolation model (CPM), [48] avoid the need for this empirical knowledge by relating the devolatilization behavior to the coal structure. The particle surface area is a paramount property in any multiphase-combustion submodel, since generally any interface phenomenon (drag, heat transfer, heterogeneous chemical reaction) is proportional to this area. Its precise modeling, however, is intricate because the solid particles are irregular in shape (albeit nearly

13.2 Boiler Design and Diagnostics

always presumed spherical for modeling purposes); they are porous, as usually measured by the BET (Brunauer, Emmett and Teller method) surface area; and, most importantly, they undergo during their lifetime many shape changes, including fracture. The prediction of a loss of efficiency in the boiler due to unburned solid matter is a challenge, due largely to the uncertainty associated with estimating this area. Particle fracture takes place primarily during char oxidation, and is due largely to the creation of voidage caused by volatilization and within-pore combustion. Reginald and coworkers developed a “particle population balance” model of particle fracture [49] but, unfortunately, this was too complex for direct implementation into CFD simulations and a much simplified version was proposed by Syred et al. [50]. Combustion, in the gaseous phase, of the volatiles produced by particle pyrolysis accounts for a large fraction of the energy generated in the furnace; however, its modeling is impaired by the complexity of the underlying kinetic mechanism. It is well known that the detailed mechanism involves far more species than is practical to include in a three-dimensional (3-D) model of a boiler, even if the volatile matter is supposed to consist of simple hydrocarbons, such as methane. Further, the chemical reaction takes place on scales that are not resolved by the mesh, and thus the interplay between turbulence and chemical kinetics must be modeled. This is usually achieved via the well-known mixture-fraction, fast-chemistry theory, using presumed-shape probability density functions (PDFs) for the mixture fraction, and assuming that the thermochemical state of the fluid is a function only of the mixture fraction (and perhaps heat loss). When kinetic effects are important, the eddy dissipation concept (EDC) [51] is frequently the first port of call, perhaps due to its simplicity and relative economy. More sophisticated chemistry–turbulence interaction approaches, such as the transported PDF model [52] or the linear eddy model [53] are prohibitive for multiburner configurations, although they are beginning to be applied to small single-burner combustors (e.g., those for aeroturbines [54]). 13.2.2.3 Modeling of Other Furnace Types While the pulverized-coal boiler has arguably been the main subject of the CFD modeling efforts, other boiler types have been simulated with varying degrees of completeness and success. Grate combustion has been used for many years as a straightforward way of producing process heat and power (e.g., in the sugar industry), and today is gaining acceptance as a simple method of burning biomass and, in particular, recovering the energy value from municipal solid waste. As grate combustion is conceptually different from suspension combustion, it is customary to write dedicated add-ons to represent it. At the simplest possible modeling level, any physical representation of combustion in the grate is dispensed with, and simple mass and energy balances are used, with presumed spatial profiles for the several variables, so as to provide inlet profiles for mass, temperature, species concentration and radiation to the furnace, which is then simulated using single-phase models. An example of this approach, in which the grate simply becomes a (somewhat sophisticated) boundary condition incorporating assumed profiles (e.g., for burnout) and mass and energy balances, has been described [55]. A more sophisticated alternative

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is the solution of the mass, momentum, species and energy conservation equations in the moving, packed bed [56]. This is usually carried out in a dedicated CFD code, by discretizing the bed using similar techniques as for the furnace [57]; both codes are then coupled through boundary conditions. The versatility and environmental benefits of fluidized-bed combustion have prompted substantial research effort into such systems, and particularly in the use of CFD. The dense nature of the particulate flow poses major challenges for multiphase CFD, and the intricacies of the interphase transfer processes, particle– particle phenomena and chemical kinetics further complicate the path to providing all-inclusive, first-principles models. Thus, many current comprehensive models are closer to zonal models, replacing the fundamental physics with ad-hoc empirical knowledge. Encouraging progress is, however, being made with regards to the simulation of bed hydrodynamics, using either Eulerian–Eulerian or Eulerian– Lagrangian frameworks [58, 59]. 13.2.2.4 The Convective Zone The convective or heat recovery zone (HRZ) of a steam boiler can be regarded as a network of shell-and-tube heat-exchangers. The flow in this zone does not have the physical complexities of the steam boiler; for instance, the chemical reaction is no longer relevant, radiation plays a lesser role, and the multiphase character of the flow is pertinent only for fouling studies, for which the fly-ash load can be regarded as negligible and thus dealt with in a post-processing step. The HRZ, however, shares the geometric complexity of the furnace such that the domain to be simulated is composed of three interplaying subdomains: the shell side; the tube side; and the tube wall. The complete meshing of any of these, let alone all three together, is impractical, and most practical models therefore resort to a specification of the tubes as subgrid features, in what is sometimes called “the distributed resistance analogy.” Most CFD studies of the HRZ are dedicated to calculations of the flowfield, and perhaps fly-ash trajectories, either ignoring heat transfer or simplifying it by assuming a metal temperature and an overall heat transfer coefficient, or directly via a heat transfer rate to the tubes. Thus, Tu and coworkers [60] addressed the CFD modeling of the fly-ash flow, without heat transfer, in the furnace and the convective section using Eulerian–Eulerian models. Coelho [61] has reported results from a CFD model of the HRZ, that considered only the shell side, and assumed either the tubeside temperatures and/or the overall heat transfer in the bundle of tubes. The technique presented by Patankar and Spalding [62] was perhaps the first shown capable of calculating simultaneously the 3-D shell-side flowfield, and the thermal fields in the shell side, the tube side and the metal, and may be regarded as the framework on which successive contributions were based. The underlying idea is that, as the energy equation for the tube wall has no fluid velocity, and the convection on the tube side can be calculated from simple one-dimensional (1-D) considerations, then the respective energy equations can be solved along the full shell-side Navier– Stokes equations without involving further fluid velocities; that is, as a single-phase calculation. The concept was further developed by Gomez and coworkers [63],

13.2 Boiler Design and Diagnostics

Figure 13.4 Simultaneous calculation of the shell-side flow of flue gases and the tube-side temperature distribution in the heat recovery zone of a coal boiler. Solid and line contours are temperatures (K).

and applied as a network of heat exchangers to the HRZ of an actual 350 MW coal boiler. Figure 13.4 illustrates this simultaneous calculation of the flue-gas flow and the tube-side temperature. A similar approach can be used to model the water-walls in the furnace when variations in the tube-side properties are significant along the wall (as for instance in supercritical boilers). This results in an increased accuracy of the temperature estimate, which is a boundary condition for convective and radiative heat transfer in the furnace. 13.2.2.5 Advanced In-Furnace Submodels As the availability of economical computer power escalates, and the demands of the power-utility industry and its regulatory authorities become more exacting, a number of ancillary submodels are being increasingly used to boost the added value of CFD calculations for the design and maintenance of power-production boilers. The use of CFD can be advantageous when the interplay among fuel type, boiler design and boiler operating conditions places the problem outside the applicability envelope of conventional design methods. One such instance is corrosion of the water-walls and other heat transfer surfaces, which is a major operational problem in some boilers. Corrosion (see Section 13.2.4) can result from the water-wall attack by chlorine or sulfur species, or from deposits of partially oxidized fuels. Thus, corrosion-impact correlations can be derived from experimental investigations under laboratory conditions, and then used with CFD-supplied local fields to provide corrosion rates (e.g., mm per year) [64]. The complexity of the modeled processes and the uncertainties associated with the CFD-exported quantities (e.g., the

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unburned matter in coal, see above; or the evolution and subsequent kinetics of the fuel-bound sulfur or chlorine) result in indicators that often require ad-hoc tuning for a specific boiler, or that are qualitative in nature. Ash deposition and slagging [65] are also processes of great operational interest that are amenable to CFD analysis. Ash deposition on heat transfer surfaces can seriously impair heat transfer, and increase the overall pressure loss by reducing the cross-sectional area. 13.2.3 Combustion Diagnostics and Control

Boiler tests are the first – and probably the most important – task in combustion diagnostics and control. To date, several boiler test codes have been published [66, 67], and many research groups have sought to improve both the thermal efficiency and the environmental performance of steam boilers [68–73]. Despite Vol. 2 Ch. 1 of this Handbook being dedicated exclusively to combustion diagnostics, a discussion of those techniques that bear a marked influence on overall boiler efficiency is included at this point. The fundamental control issue in a boiler is the regulation of steam production according to the process demands. This can be achieved by controlling the thermal input in a closed loop according to the load set point, which in turn requires modification of the fuel flow supplied to the burners. The simplest configuration includes three elements: (i) an instrument to measure energy production (steam flow in an industrial boiler or electricity generated in a steam turbine); (ii) an automatic regulator (e.g., proportional integral derivative (PID) controller); and (iii) an actuator suitable to modify the flow of fuel (valve, pump speed, etc.) in response to the controller output. Unfortunately, this basic configuration is unable to cope with the multiple constraints and optimization objectives in large boilers. In many cases, more sophisticated systems are required to control the feed water, steam quality, and so on, by acting on different parameters of the water/steam circuit [74]. However, the main complexities and challenges usually derive from the combustion process. In the first place, the combustion process largely determines the key performance aspects, such as efficiency, pollutant emissions or plant flexibility and reliability. Besides load regulation, the control system should be able to achieve high fuel conversions and minimize pollutant emissions (e.g., NOx) in any situation (e.g., variations in load or fuel quality) and, at the same time, prevent any unstable operation or the risk of flame loss. Second, the behavior and outcome of combustion systems display highly nonlinear and, usually, unpredictable relationships with operating conditions; this greatly limits the range of control methods applicable and increases the need for sensorial information in order to describe the process state at any time. However, the process description afforded by the monitoring techniques available commercially is not as detailed as would be needed to control the process in a safe and efficient manner. This is, perhaps, the main difficulty to overcome in this field, and justifies the growing efforts in recent years aimed at developing advanced instruments for the monitoring of industrial combustion systems.

13.2 Boiler Design and Diagnostics

13.2.3.1 Flue Gas Analysis Besides the fuel feeding rate, the flue gas composition is the main reference used to evaluate and regulate the combustion machinery. The oxygen concentration determines the excess air, as a key parameter of the process regarding fuel conversion, pollutant formation and losses as sensible heat. Both, CO and unburned hydrocarbons (UHC) are indicative of incomplete fuel conversion. The emissions of oxides of nitrogen and sulfur, or particulates, are usually subject to legal limits, which also apply to other species in certain applications (e.g., heavy metals or dioxins in incineration plants). Therefore, flue gas analyzers form an integral part of industrial combustion plants, and an extensive compilation of the analytical instruments commonly used for this purpose has been provided [75]. Many of the traditional methods of flue gas analysis are of the extractive type; that is, a gas sample is aspirated through a probe inserted in the stack and then transported to the analyzer. Electrochemical cells are the most common choice for portable analyzers, but they are not suitable for continuous use. Fixed systems usually consist of individual analyzers for the different species; measurement principles include paramagnetism (O2), flame ionization (hydrocarbons), chemiluminescence (NO) or nondispersive infrared (NDIR; suitable for most of the relevant species, such as O2, CO, CO2, SO2, NOx). The disadvantages of extractive methods – which include probe and line clogging, costs of the sampling equipment and delayed response – have favored the development and dissemination of in situ techniques that are capable of analyzing the gas flowing inside the stack. Zirconia probes (see also Vol. 2 Ch. 10) can withstand high temperatures (up to 1500
C), and are a common choice for O2 detection. Today, significant efforts are being devoted to develop solid-state analyzers, with the potential of enabling the commercialization of robust and economical devices suited to the detection of different species in high-temperature gases. Solid-state electrolytes represent a promising alternative for the in situ measurement of O2, CO, NO, NO2 and SO2, even as a single multisensor element [76, 77]. The influence of the partial pressure of some species (e.g., O2, UHC) on the electric conductivity of some semiconductors (e.g., TiO2, SnO2) has been also used to develop solid cells that can be used at temperatures up to 1000
C [76, 78]. In situ optical methods are becoming increasingly common for stack measurements. In this case, commercial instruments use different techniques based on the absorption of IR radiation at specific wavelengths for the selective detection of different species; the probed volume may include a whole duct diameter or a segment, defined by a slot of the probe inserted into the stack. Particulate concentration can be also determined by measuring the light attenuation or scattering across the stack. However, one problem with such techniques is that the final result obtained is also dependent on the size and shape of the particles, such that ad-hoc in situ calibrations are also required. Tunable diode laser absorption spectroscopy (TDLAS) represents a powerful alternative for the real-time, in situ monitoring of multiple species and temperature in flue gases [79, 80]. For this, one or several laser beams of different wavelengths tuned at the desired absorption bands are multiplexed and transmitted across the probed volume (Figure 13.5). The concentrations of the different species are derived

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Figure 13.5 Schematic of in situ tunable diode laser absorption spectroscopy sensor for detection of two species with absorption bands at l1 and l2, respectively.

from the absorption in specific bands, whereas the gas temperature can be related to the ratio of two absorption bands of a given compound. Tunable diode lasers offer rapid and continuous-wavelength tuning, enabling the selection of most adequate bands as well as the use of wavelength modulation spectroscopy for improved sensitivity and noise reduction. Currently, more than 1000 TDLAS-based instruments have been installed in industrial plants [81], confirming the potential of this technique. 13.2.3.2 In-Furnace Diagnostics Burner settings can be adjusted according to the gas composition measured in the stack in order to achieve optimal performance in terms of combustion efficiency (CO, UHC), sensible heat losses (O2) or pollutant emissions (e.g., NOx or particulates). However, the problem becomes more complex in multiburner boilers, due to possible imbalances in fuel and air feeding rates, or burner geometry. A correct global excess air in the stack does not necessarily mean that all the burners are operated with the same air-to-fuel ratio; some of them may receive a too-high excess of air (which can increase the formation of NOx), whereas some others could produce high CO amounts due to insufficient oxygen availability. Attempts to avoid CO emissions in such instances would result in a too-high air-to-fuel ratio, leading to larger heat losses and, most likely, to increased NOx emissions. The individual monitoring of the different burners becomes essential in order to achieve a correct burner adjustment. To this end, an automatic in-furnace gas sampling system (MEIGAS) has been developed by Union Fenosa, Indra and LITEC. The set-up, which is shown schematically in Figure 13.6, has been tested and installed in several coal-fired utility boilers in Spain. The performance of the individual burners is evaluated from the analysis of gas samples extracted directly from the different flames. A special design of water-cooled sampling probes allows penetration lengths of several meters, while keeping their thickness at 10 mm. This makes possible their insertion through holes in the membrane between the tubes forming the furnace water-wall. This feature significantly reduces the modifications needed, and allows the installation of a large number of probes (one per burner) with very low retrofitting costs. The probes are sequentially and automatically inserted to extract a

13.2 Boiler Design and Diagnostics

Figure 13.6 Automatic gas sampling system (MEIGAS) installed in an arch-fired utility boiler.

gas sample from each flame, which is sent to a gas analyzer (the same unit for all the probes on one side of the boiler). As a result, full information on the levels of O2, CO and NOx at each burner is available for the boiler personnel to regulate individual airto-fuel ratios in order to optimize combustion performance. The TDLAS technique mentioned above can also be applied for measurements inside the combustion chamber; application examples in real systems, including utility boilers, incineration plants or gas turbines, are detailed elsewhere [79, 80, 82]. One drawback of this technique, however, is its line-of-sight nature. The strong gradients of gas composition and temperature inside the chamber introduce certain difficulties regarding interpretation of the results, because the measurement volume may include regions with very different physical and chemical conditions. Nevertheless, this technique offers unique capabilities for multispecies and temperature detection that, taking into account the particularities of the system under study, can be used for advanced combustion monitoring in industrial boilers. Many of the available flame-monitoring approaches are based on different forms of flame emission spectroscopy; hence, the possibility of acquiring direct information from the flame by using economical, rugged optical sensors makes this a most attractive alternative. In some cases, pyrometry is applied to estimate flame temperature from light emission in particular bands, usually in the IR region; ratio pyrometers or thermographic cameras are examples of commercial instruments based on this principle. Two-color pyrometry has also been applied to obtain twodimensional (2-D) temperature distributions from the ratio between bandfiltered IR

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images, or between two channels of low-cost, RGB cameras (e.g., green and red) [83]. Clearly, pyrometry is only applicable in particle-laden flames (sooting or solid-fuel flames), and is dominated by black-body radiation. Chemiluminescence sensing is a different approach, not affected by this limitation. Combustion reactions are known to produce some excited radicals that can emit light when the molecules de-excite. For example, OH. , CH. and C2. display characteristic emission bands at around 310, 431, and 516 nm, respectively [84]. Radiation collected at specific wavelengths can be related to instantaneous heat release rate, or to the air-to-fuel ratio [85, 86], and has been used extensively for research purposes, especially in studies of flame dynamics. Techniques based on similar principles have been proposed for on-line flame monitoring in practical applications. Flame monitoring based on radiation signals entails some fundamental difficulties. First, the development of defined relationships between the signal strength and physical flame parameters is not, in general, possible. For this reason, radiation signals have been treated in many cases as a “fingerprint” of the flame, rather than attempting to derive physical parameters from it. Second, the radiation detected depends not only on the flame characteristics but also on the detector response, background radiation, optical path, fouling of the windows, and so on. This feature advises using relative magnitudes (ratios between signals, normalized standard deviation, etc.), which should be less affected by this problem. Analysis in the frequency domain of the signal apparently solves both difficulties, and has been used in some cases, including certain commercial systems. For example, some industrial flame scanners analyze the characteristic frequencies of the signal so as to distinguish among neighbor flames. More sophisticated approaches, both regarding data processing and their use for burner control, have been applied for flame monitoring and optimization in large boilers [87–89]. A growing interest on image-based flame monitoring methods has been verified in the past few years, most likely fostered by the continuous increase in the performance-to-cost ratio for video cameras. Actually, these are also emission-based sensing methods, with the advantage of providing detailed information in the form of 2-D maps. Different approaches have been tested, including broadband and bandfiltered radiation, the use of color, intensity or geometrical parameters, statistical or frequency analysis, among others (e.g., see Refs [90, 91]). 13.2.3.3 State Identification and Control of Combustion Systems As with any other processes, the control or optimization of combustion requires, as a first step, some knowledge of the actual system condition. Flue gas analyzers can be used to determine the excess air, unburned fuel, or pollutant emissions, although whether this type of information is sufficient to determine the actual combustion will depend on the particular application. Nevertheless, these are quantitative data that can be used to evaluate combustion performance, as well as to drive a control procedure. The situation is very different for most flame-monitoring methods, in particular those using direct flame information (light emission, acoustic signals, flame images) as a feature of the process. As a first step, this sensorial information must be

13.2 Boiler Design and Diagnostics

converted into meaningful parameters, through some type of procedure for the identification of operating conditions. A wide variety of approaches has been tested, in terms of the type of result sought and of the method selected for data processing [85, 86, 91, 92]. In some cases, the objective is to identify the actual combustion condition with respect to situations previously known. Although not exactly a quantitative evaluation, the classification of the flame can be very useful to qualify the flame according to certain “quality” criteria, to identify malfunctions, or to implement knowledge-based optimization procedures. Alternatively, flame signals can be processed to estimate certain specific combustion parameters, such as the airto-fuel ratio, pollutant emissions, and so on. As noted above, such capabilities are particularly useful for multifuel burners, as they enable evaluation of the characteristics of individual flames, which is not possible from global flue gas analysis. Currently, most closed-loop combustion controls are designed to regulate the mass flow rate of fuel (according to an energy throughput set point) and air (based on oxygen concentration in flue gases). However, more ambitious control objectives would be needed to optimize the process. For example, flue gas analysis might be used to automatically control the burner settings in order to minimize NOx emissions. However, prediction of the flame response is very difficult, and the risk of reaching unstable regimes or, ultimately, to cause flame blow-off is too serious, and limits the scope of such strategies. More detailed information on the process (i.e., the flame) would be required to implement reliable methods for combustion optimization. To this end, some of the above-described flame-monitoring techniques have been tested, with good results having been reported [86, 89, 92, 93] when the flame signals were used to drive an optimization procedure. Although these types of approach are still under development, the results have shown much promise, and their implementation in practical boilers might result in important benefits in terms of efficiency, pollutant reduction, or plant flexibility. A different category of advanced combustion controls is that based on monitoring a large number of process variables as a means to identify the plant state and to determine the optimal operating conditions. In this case, however, the combustion process is characterized by using conventional instrumentation, which greatly limits their capabilities with regards to flame diagnostics or the optimization of burner settings. Artificial intelligence techniques (e.g., neural networks) have been used to estimate the performance parameters as a function of input data (state identification) and/or to identify combinations of manipulated variables, leading to desired or optimal performance. Such experiences in large utility boilers have been described elsewhere [94–96]. 13.2.4 Ash Deposition and Corrosion

Different mineral impurities are introduced into the furnace with the fuel, especially in coal-fired boilers. Although the large majority of these are extracted as slag, an important proportion is dragged by the combustion gases, as particulate matter and gaseous compounds. Part of the solid mineral matter dragged by the gas flow will be

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deposited on the gas-side of thermal surfaces, forming a layer that reduces both convective and radiative heat transfer. This phenomenon is termed slagging or fouling, depending on the surface-metal temperature. The repeated impact of particles on tubes and baffles may also lead to erosion and, eventually, to failure of the heat transfer equipment [97–100]. Gaseous or solid compounds can also react with the metal, causing corrosion of the heat transfer surfaces. Slagging, fouling, corrosion and the erosion of thermal surfaces of boilers are phenomena closely linked with the presence of contaminants in fuels, and these problems are among the most important aspects to be considered during furnace design and boiler operation and maintenance. In this respect, various reviews [101–104] have provided excellent reference material on the different aspects of these problems (see also Vol. 2 Ch. 18). The slagging or fouling tendency of a fuel can be predicted by empirical indices based on fuel or ash composition, as described, for example, in Ref. [105]. Other techniques have also been developed to describe or predict the deposition tendencies of different fuels in industrial boilers [102, 106–116]. Boiler corrosion has been studied by many groups [20, 21, 117–120]. Depending on the zone of the boiler where the heat transfer surfaces are placed, there are, basically, two different types of corrosion: .

.

High-temperature corrosion: This is mainly dependent on the composition and concentration of ash formed during fuel combustion. The combustion of fuel with high vanadium, sulfur, chlorine, sodium, and potassium contents results in highly corrosive deposits. The slag produced during combustion has a low melting temperature, and adheres to hot metal surfaces (450
C); these sticky deposited materials then capture ash, soot and coke, which in turn reduces the heat transfer and causes corrosion. Sulfur is one of the most aggressive agents, leading to high-temperature corrosion when complex sulfates are involved. Alkali sulfates, such as Na2SO4 and K2SO4 are highly corrosive when molten; alkali iron trisulfates, for example Na3Fe(SO4)3, are highly reactive materials that melt in the temperature range reached on superheaters. In addition, the reaction between sodium and vanadium forms compounds that normally melt between 510 and 870
C. The system NaVO3Na2O3V2O5, which has a melting point as low as 480
C, can be found in superheaters, and is also very corrosive. Vanadium salts are extremely corrosive, as they dissolve the protective oxide film formed on the metal surface and then transport the oxygen to the clean metal surface. In order to prevent or reduce deposits and corrosion, the fusion temperature for the sodium and vanadium salts must be raised. Cold-end corrosion: This is due to the formation of sulfuric acid in the exhaust gases that can condense over the metal surface. During the combustion of sulfurbearing fuel, the sulfur is readily oxidized to SO2 in the combustion reaction, a part of which is subsequently transformed to SO3 upon cooling of the flue gases. The free SO3 can react with water vapor in the combustion gases, forming sulfuric acid, H2SO4, responsible for cold-end corrosion. Condensation of H2SO4 can occur directly on the metal walls of the heat exchangers in the low-temperature area and stack linings; condensation may also occur on soot particles, which will

13.2 Boiler Design and Diagnostics

adhere to the surfaces and result in a build-up of soot deposits, as well as potential acid smut emissions. Cold-end corrosion potential is closely related to the acid dewpoint temperature that is, in turn, a function of the water vapor and acidspecies concentration in the flue gas. For this reason, the exhaust-gas temperature is normally selected in boilers so that metal temperatures in the last heat recovery equipment (usually air heater) exceed by several degrees the acid dewpoint temperature. However, this temperature margin should be minimized in order to limit the efficiency drop due to sensible heat in flue gases which, as previously discussed, is the most important heat loss in boilers. There are several means of reducing high-temperature corrosion in a steam boiler, including: (i) switching to another “better quality” fuel (normally more expensive); (ii) reducing the air excess; (iii) improving the furnace design injection of additives; or (iv) the use of corrosion-resistant materials. The use of a better-quality fuel allows for a more efficient combustion with a lower excess air, reducing both the amount of water vapor formed and the SO3 concentration in the combusting gases, and leading to a lower sulfuric acid concentration in the flue gases. However, considering the current trend towards the reduced quality of fuels, this route is not always feasible and alternative solutions must be sought. The design aspects of low-temperature heat exchangers, such as selecting appropriate materials or ensuring a suitable flow velocity, should also be considered. Whilst, today, the use of porcelain-based coating products is becoming popular, this involves significant extra costs that are not always affordable. The most widespread way to counteract corrosion problems is to mix chemical additives, such as combustion catalysts, with the fuel. These catalysts can reduce the formation of sulfuric acid, and magnesium-based additives will neutralize the acid as it is formed. It is well known that alkaline metals (e.g., sodium and potassium) are suitable for neutralizing SO3 formation during combustion, while magnesium-based additives help by raising the melting temperature of vanadium oxides. A higher melting point means that the deposit will be drier and more brittle, and so will not adhere to the metal surface and be easier to remove. Although by using additives the mass of the deposit will be increased, it will become weaker so that it can be periodically detached; alternatively, it may be easily removed by soot blowing. Magnesium oxide-based slurry and organometallic-type additives have been used successfully to treat chemically very heavy fuel oils [20, 21], and these are available commercially [121–124]. In the case of the Mg-based slurry, a suitable optimization of the Mg : V ratio is required to obtain good results since, if the ratio is low (R  0.5) the required modification of deposits will not be achieved. Fouling can be reduced with a higher Mg : V ratio (R ¼ 1) but, in this case, nonreacted Mg will be deposited onto the heat transfer surfaces, producing hard deposits that are very difficult to remove. As a result, a high thermal radiation would occur, causing an increase in the pipe-metal temperature and requiring a higher flow of tempering water to maintain the superheated steam temperature at the design level. Very good results have been obtained with organometallic-type additives, which raise the melting temperature of the deposits by modifying the chemical composition of the slag. A decrease in the acid

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dewpoint temperature for the stack gases, and a lesser degree of fouling on the continuous regenerative air heaters, have also been detected [20, 21].

13.3 Technology Outlook

Today, the two main priorities of industrial steam boilers are first, to reduce pollutant emissions, and second to increase efficiency, and both of these impact directly on the area of steam generation. Hence, the major technological developments in steamboiler technology must respond to these two basic driving forces, the main trends of which are summarized briefly in the following sections. 13.3.1 Supercritical and Ultra-Supercritical Boilers

One way to increase the efficiency of the Rankine cycle is to use supercritical steam as the working fluid. Supercritical boilers consist of a bundle of parallel tubing through which water is pumped. The water absorbs heat while passing along the length of the pipes in both the furnace and the convective section of the boiler, which causes it to change gradually to steam and to rise in temperature until the design value in the superheated range is reached. In order for this to occur, heat transfer to the watersteam circuit must be increased in comparison to conventional boilers, which currently incorporate a spirally laid tube-wall and internally rifled tubes as the most suitable options. These types of unit require not only a very high water purity, due to the absence of a boiler drum with a blow-down capability for the accumulated impurities, but also a very well controlled and uniform volumetric heat release in the combustion chamber. These conditions are necessary because cooling of the tubes in supercritical boilers occurs at lower heat transfer rates than in subcritical boilers, where nucleate boiling takes place. Today, several supercritical power plants are in operation, each demonstrating improved efficiencies compared to their subcritical counterparts, yet with similar outage frequencies [6]. Supercritical power plants (250 bar, 540
C) with single or double reheat systems represent a mature and commercial technology [125], but the technological ability to increase these parameters to 300 bar and 600
C is available today and this should allow an improvement in efficiency to about 45%. In fact, it has been predicted that ultra-supercritical boilers with steam parameters of 375 bar and 700
C, and with an efficiency of 50%, might well be available in less than 10 years [6]. Additional research effort is needed in order to develop new nickel-based superalloys for long-term operation at steam temperatures in the range of 700–720
C, for thin-walled super and reheater tubes, and thick-walled outlet headers and steam piping. Likewise, new austenite steels for boiler tubes operating over the temperature range 600–700
C (to minimize the use of expensive super-alloys) are required. Moreover, new methods are required to create components manufactured from super-alloys, for welding similar and dissimilar materials, and for reducing

13.3 Technology Outlook

high-temperature fireside corrosion. To date, materials suitable for the construction of the critical components of coal-fired boilers generating steam at 350 bar and 760
C have been identified, evaluated, and qualified by Viswanathan and coworkers [126]. For heavy section and tubular applications, this group tested Ni-based alloys, austenitic alloys and ferritic materials, as well as T92 and T23 alloy steel for seamless pipes used in water-wall tubing. Welds and multiaxial creep rupture were identified as life-limiting features by Perrin and Fishburn [127], and these will clearly require further investigation for the design of supercritical boiler components. 13.3.2 Low-NOx Combustion Systems

During the past two decades, the reduction of NOx emissions has probably been the main single motivation for the development of new burners and combustion concepts. Traditional fast-mixing burners are normally characterized by high NOx emissions, and in the past have had to be substituted by new combustion strategies enabling reduced emissions while assuring high efficiency and flame stability. The primary measures for NOx control (i.e., those based on modifications to the combustion process) are reviewed briefly in the following sections. The principles of the main existing technologies for reducing NO emissions are described elsewhere [128–135], although a more detailed review is also available in Vol. 2 Ch. 17 of this Handbook. If a fuel contains a significant amount of nitrogen, then the major part of the emissions is normally attributable to the fuel-NO route. In these cases, the simplest method of reducing NOx emissions would be to replace the fuel with another that contained less or no nitrogen, either totally [130] or partially [136, 140]. Unfortunately, this is not always possible, and so NO formation must be reduced by acting on the combustion process: .

.

Operating with low excess air (typically <2%) and with a lower temperature of the combustion air leads to reduced NOx formation [129–131]. However, special care must be taken when applying this measure, as it may reduce both combustion and overall boiler efficiencies. Air staging, in its different forms, is the most extended NO-control measure. This consists of altering the air-to-fuel ratio in order to lower not only the peak temperature of the combustion gases but also the oxygen concentration in the reaction zone. The general idea is that the combustion process takes place in two stages: a first step, with very low oxygen levels (substoichiometric conditions) so as to suppress or reduce the formation of NOx; followed by a second step, where the necessary oxygen is provided to complete the fuel oxidation. Air staging is an area of research in which many technology patents have been recently claimed [141–144]. As an example, the effectiveness of air staging for NOx control using fuel oil number 6 has been demonstrated by Shihadeh and coworkers [145]. Unfortunately, air staging may pose certain problems, such as flame instabilities and incomplete combustion, thereby causing the increased emission of both

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.

.

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particulates and CO, and also losing the flexibility to cover a whole load range with the required safety margins [130, 146]. Nevertheless, improved designs that allow the simultaneous achievement of a higher boiler efficiency and reduced NOx emissions have been recently developed (e.g., Ref. [144]). Substoichiometric conditions can be implemented by several means, including: overfire air (OFA) [129–131, 148], burners out of service (BOOS) [129– 131], and biased-burner firing [129, 130], among others. Low- and ultralowNOx burners based on in-flame air staging are commercially available (see, e.g., Refs [149–152]), and several reports have highlighted the advantages of these technologies [153–160]. It is considered that, by comparison with a conventional burner, a reduction in NO emissions close to 60% can be achieved with low-NOx burners, and from 85–90% with their ultralow counterparts [128]. Flue gas recirculation (FGR) represents a common method for controlling NOx emission in industrial boilers. The technique involves returning part of the cold flue gases to the combustion zone in the furnace, in order to reduce the oxygen concentration and combustion temperature, by a dilution effect. Reburning is a three-step combustion process which results in fuel staging in the combustion volume. The main combustion zone is operated under slightly fuellean conditions, whilst the remainder of the fuel is introduced downstream, when the primary combustion has been completed. This creates a substoichiometric region that is rich in hydrocarbon radicals that reduce HCN and NO to N2. When this reduction process is concluded, combustion is completed in a third stage with the addition of tertiary air. In this way, the NOx reduction can be up to 70%, depending on the design of the process (residence time, stoichiometry, and temperature in the reburning zone) and the properties of the reburning fuel (notably, higher reductions are achieved for nitrogen-free fuels). This technique has been deployed mainly in large industrial boilers burning coal and fuel oil, and the benefits obtained in industrial tests have been reported by several authors [146, 161–164]. The injection of water or steam into the combustion zone causes a decrease in the temperature of the combusting gases, and therefore also of NOx formation via the thermal mechanism. Friedrich and coworkers [130] have noted that this technique may also introduce certain negative effects, such as a decrease in overall boiler efficiency, an increase in CO emissions, an increase in the visibility of stack plumes (even if only due to steam emissions), and an increase in the corrosion rate on the gas side. Unfortunately, the use of steam or water injection as a NOx control strategy in industrial facilities is very limited, due not only to additional energy costs but also to these deleterious effects [132].

13.3.3 Oxycombustion

In oxycombustion, which is a relatively novel combustion concept, the aim is to reduce CO2 emissions from fossil-fuel combustion by using pure oxygen as the

References

oxidant (occasionally, it is mixed with recirculated flue gases). The absence of nitrogen in the oxidant stream facilitates separation of the CO2, by condensing the water vapor in the flue gases, for subsequent sequestration. Oxycombustion can, in principle, be implemented in conjunction with most other combustion technologies; in the case of coal, these include subcritical, supercritical, and ultrasupercritical pulverized coal-fired boilers, as well as circulating fluidized-bed (CFB) boilers. In the case of pulverized coal-fired boilers, oxycombustion has the added benefit of (potentially) causing a significant reduction in NOx emissions (by about 60%), particularly of those originating via the thermal route, as much of the air nitrogen is removed prior to combustion. Unfortunately, combustion in pure oxygen carries the deleterious effect of higher adiabatic flame temperatures, which can (potentially) lead to damage of the heat transfer surfaces. In the absence of any suitable materials, the flame temperature can be reduced by recirculating the flue gases, but this may leads to larger boiler-island footprints than are strictly required for pure-oxygen combustion, and to an increase in the parasitic power load. In the case of pulverizedcoal firing, the sulfur content of the coal may also be a limiting requirement; as a large fraction of the flue gases is recirculated, the concentration of sulfur-species within the recirculated gases may be increased more than threefold [165]; this would resulting not only in a greater potential for corrosion compared to air combustion, but also make the installation of downstream flue gas desulfurization equipment unavoidable. The oxyfuel CFB concepts currently under development have, in this regard, the distinct advantage of easily accommodating in-furnace sulfur capture (via the addition of limestone). It has also been suggested that the well-known benefits of fluidized-bed (air) combustion – namely more uniform gas and metal temperature profiles and a greater fuel flexibility – might also be carried over to oxycombustion.

References 1 Perry, R.H., Green, D.W., and Maloney,

6 Beer, J.M. (2007) High efficiency electric

J.O. (1984) Perry’s Chemical Engineers Handbook, 6th edn, McGraw-Hill, New York. Babcock and Wilcox Company (2007) Steam - Its Generation and Use, 41st edn, Babcock and Wilcox Co. Grigorieba, B.A. and Zorina, B.M. (1982) Thermal and Nuclear Power Station Handbook, 6th edn, Energy, Moscow, in Russian. Cidelkoski, L.N. and Iureniev, B.N. (1988) Industrial Boiler Fittings, 3rd edn, Energy, Moscow, in Russian. Beer, J.M. (2000) Combustion technology developments in power generation in response to environmental challenges. Prog. Energy Combust. Sci., 26, 301–327.

power generation: the environmental role. Prog. Energy Combust. Sci., 33, 101–134. Yin, C., Rosendahl, L.A., and Kær, S.K. (2008) Grate-firing of biomass for heat and power production. Prog. Energy Combust. Sci., 34, 725–754. Basu, P. (1999) Combustion of coal in circulating fluidized-bed boilers: a review. Chem. Eng. Sci., 54, 5547–5557. Khan, A.A., de Jong, W., Jansens, P.J., and Spliethoff, H. (2009) Biomass combustion in fluidized bed boilers: potential problems and remedies. Fuel Process. Technol., 90 (1), 21–50. Lefevbre, A. (1989) Atomization and Spray, Hemisphere Publishing Corp., New York.

2

3

4

5

7

8

9

10

j323

j 13 Industrial Steam Boilers

324

11 Fraser, R.P., Dombrowski, N., and

12

13

14 15

16

17

18

19

20

21

22

Routley, J.H. (1993) The filming of liquids by spinning cups. Chem. Eng. Sci., 18 (6), 323–337. Radclife, A. (1955) The Performance of a type of swirl atomizer. Proc. Inst. Mech. Eng., 169, 93–106. Dombrowski, N., Hasson, D., and Ward, D.E. (1960) Some aspects of liquid flow through fan spray nozzles. Chem. Eng. Sci., 12, 35–50. Lang, R.J. (1962) Ultrasonic atomization of liquids. J. Acoust. Soc. Am., 34 (1), 6–8. Sovani, S.D., Sojka, P.E., and Lefebvre, A.H. (2001) Effervescent atomization. Prog. Energy Combust. Sci., 27 (4), 483–521. Mullingen, P.J. and Chigier, N.A. (1974) The design and performance of internal mixing multijet twin fluid atomizers. J. Inst. Fuel, 47, 251–261. Bryce, W.B., Cox, N.W., and Joyce, W.I. (1978) Oil droplet production and size measurement from a twin-fluid atomizer using real fluids. Proceedings of the 1st International Conference on Liquid Atomization and Spray Systems, Tokyo, pp. 259–263. Sargeant, M. (1982) Blast atomizer developments in the central electricity generating board. Proceedings of the 2nd International Conference on Liquid Atomization and Spray Systems, Madison, Wisconsin, pp. 131–135. Warnatz, J., Maas, U., and Dibble, R.W. (2001) Combustion: Physical and Chemical Fundamentals, Modeling and Simulation, Experiments, Pollutant Formation, 3rd edn, Springer-Verlag, Berlin. Barroso, J., Barreras, F., and Ballester, J. (2004) Behavior of a high-capacity steam boiler using heavy fuel oil. Part I: Hightemperature corrosion. Fuel Process. Technol., 86 (2), 89–105. Barreras, F. and Barroso, J. (2004) Behavior of a high-capacity steam boiler using heavy fuel oil. Part II: Cold-end corrosion. Fuel Process. Technol., 86 (2), 107–121. Graziadio, M., Andreussi, P., Tognotti, L., and Zanelli, S. (1987) Atomization of coal-water fuels by pneumatic internal mixing nozzle. Part I-Two-phase flow

23

24

25

26

27

28

29

30

31

32

33

34

35

inside the nozzle. Atomization Spray Technol., 3, 187–208. Andreussi, P., Tognotti, L., Graziadio, M., and De Michele, G. (1990) Atomization of coal-water fuels by a pneumatic nozzle: characteristics of the spray. Aerosol Sci. Technol., 13, 35–46. Barreras, F., Lozano, A., Barroso, J., and Lincheta, E. (2006) Experimental characterization of industrial twin-fluid atomizers.AtomizationSpray,16,127–145. Ferreira, G., Garc
ıa, J.A., Barreras, F., Lozano, A., and Lincheta, E. (2009) Design optimization of twin-fluid atomizers with an internal mixing chamber for heavy fuel oils. Fuel Process. Technol., 90 (2), 270–278. John Zink Co . (2001) The John Zink Combustion Handbook (eds C.A. Baukal and E. Robert) CRC Press, Schwartz. Instituto de Diversificación y Ahorro de Energ
ıa (1988) Uso Eficiente de la Energ
ıa en Calderas y Redes de Fluidos, IDAE, Madrid. Blokh, A.G. (1988) Heat Transfer in Steam Boiler Furnaces, English-edition, translated by Malyavskaya, G.R., Shreyeva, N.K.,and Klimovich, Zh.D., translation editor, O.G. Martynenko (ed. R. Viskanta), Hemisphere, Corp., Washington. Kuznetzova, N.A. (ed.) (1973) Thermal Design of Boilers. Normative Method, Ed. Énergiya, Moscow, in Russian. Lokchina, B.A., Peterzona, D.F., and Chbarza, A.L. (1978) Hydrodynamic Design of Boilers. Normative Method, Ed. Énergiya, Moscow, in Russian. Idelcik, I.E. (1979) Memento des Pertes de Charge, Translated from Russian, Eyrolles Editeur, Paris, nouveau tirage. Mochan, S.I. (ed.) (1977) Aerodynamic Design of Boilers. Normative Method, Ed. Énergiya, Moscow, in Russian. Antikaiin, P.A. (1969) Metal and Mechanical Design of Steam Boiler Plants, Ed. Énergiya, Moscow, in Russian. Amery, M. and Reza, S. (2008) A case study: The effects of the design factors on the thermal profile of Shahid Rajaiee boiler. Appl. Therm. Eng., 28, 955–961. Skiepko, T. and Shah, R.K. (2004) A comparison of rotary regenerator theory

References

36

37

38

39

40

41

42

43

44

45

and experimental results for an air preheater for a thermal power plant. Exp. Therm. Fluid Sci., 28, 257–264. Khalil, E.E., Spalding, D.B., and Whitelaw, J.H. (1975) The calculation of local flow properties in two-dimensional furnaces. Int. J. Heat Mass Transfer, 18 (6), 775–791. Neimarlija, N., Demirdzic, I., and Muzaferija, S. (2009) Finite volume method for calculation of electrostatic fields in electrostatic precipitators. J. Electrostat., 67 (1), 37–47. Fueyo, N., Gomez, A., and Gonz
alez, J.F. (2008) A comprehensive mathematical model of flue-gas desulfurization. Proceedings: Progress in Industrial Mathematics, European Consortium for Mathematics in Industry, 2006, vol. 12, pp. 290–295. Crowe, C.T., Sharma, M.P., and Stock, D.E. (1977) The particle source in cell (PSI-CELL) model for gas-droplet flows. J. Fluid. Eng., 99, 325–332. Gosman, A.D. and Ioannides, E. (1994) Aspects of computer simulation of liquid fueled combustors. Proceedings of the 19th Aerospace Meeting. AIAA, St. Louis, MO, Paper No. 81-03223. Patankar, S.V. and Spalding, D.B. (1972) A calculation procedure for heat, mass and momentum transfer in threedimensional parabolic flows. Int. J. Heat Mass Transfer, 15 (10), 1787–1806. Spalding, D.B. (1980) Numerical computation of multi-phase flow and heat transfer, Recent Advances in Numerical Methods in Fluids (ed. C. Taylor), Pineridge Press, pp. 139–168. Fueyo, N., Gambón, V., Ballester, J., Dopazo, C., and Gonz
alez, J.F. (1999) Computational evaluation of low-NOx operating conditions in arch-fired boilers. ASME J. Eng. Gas Turb. Power, 121, 735–740. Benim, A.C., Epple, B., and Krohmer, B. (2005) Modelling of pulverised coal combustion by a Eulerian-Eulerian twophase flow formulation. Prog. Comput. Fluid D, Int. J., 5 (6), 345–361. Spalding, D.B. (1982) The “Shadow” method of particle-size calculation in twophase combustion. Nineteenth

46

47

48

49

50

51

52

53

54

55

Symposium (International) on Combustion, pp. 941–951. Fueyo, N., Ballester, J., and Dopazo, C. (1997) The computation of particle size in Eulerian-Eulerian models of coal combustion. Int. J. Multiphase Flows, 23 (3), 607–612. Selle, L., Lartigue, G., Poinsot, T., Koch, R., Schildmacher, K.-U., Krebs, W., Prade, B., Kaufmann, P., and Veynante, D. (2004) Compressible large eddy simulation of turbulent combustion in complex geometry on unstructured meshes. Combust. Flame, 137 (4), 489–505. Grant, D.M., Pugmire, R.J., Fletcher, T.H., and Kerstein, A.R. (1989) Chemical model of coal devolatilization using percolation lattice statistics. Energy Fuel, 3 (2), 175–186. Reginald, E., Mitchell, A.E., and Akanetuk, J. (1996) The impact of fragmentation on char conversion during pulverized coal combustion. Proc. Combust. Inst., 26 (2), 3137–3144. Syred, N., Kurniawan, K., Griffiths, T., Gralton, T., and Ray, R. (2007) Development of fragmentation models for solid fuel combustion and gasification as subroutines for inclusion in CFD codes. Fuel, 86 (14), 2221–2231. Magnussen, B.F. (1981) On the structure of turbulence and a generalized eddy dissipation concept for chemical reaction in turbulent flow. Nineteenth AIAA Meeting, St. Louis. Dopazo, C. (1992) Recent Developments in PDF Methods, Academic Press, pp. 375–474. Kerstein, A.R. (1991) Linear-eddy modeling of turbulent transport. Part 6. Microstructure of diffusive scalar mixing fields. J. Fluid. Mech., 231, 361–394. Patel, N. and Menon, S. (2008) Simulation of spray-turbulence-flame interactions in a lean direct injection combustor. Combust. Flame, 153 (1–2), 228–257. Goerner, K. and Klasen, Th. (2006) Modelling, simulation and validation of the solid biomass combustion in different plants. Prog. Comput. Fluid D., Int. J., 6 (4–5), 225–234.

j325

j 13 Industrial Steam Boilers

326

56 Yang, Y.B., Lim, C.N., Goodfellow, J.,

57

58

59

60

61

62

63

64

65

Sharifi, V.N., and Swithenbank, J. (2005) A diffusion model for particle mixing in a packed bed of burning solids. Fuel, 84 (2–3), 213–225. Soren, K.K. (2004) Numerical modelling of a straw-fired grate boiler. Fuel, 83 (9), 1183–1190. Papadikis, K., Bridgwater, A.V., and Gu, S. (2008) CFD modelling of the fast pyrolysis of biomass in fluidised bed reactors, Part A: Eulerian computation of momentum transport in bubbling fluidised beds. Chem. Eng. Sci., 63 (16), 4218–4227. Ibsen, C.H., Helland, E., Hjertager, B.H., Solberg, T., Tadrist, L., and Occelli, R. (2004) Comparison of multifluid and discrete particle modelling in numerical predictions of gas particle flow in circulating fluidised beds. Powder Technol., 149 (1), 29–41. Tu, J.Y., Fletcher, C.A.J., and Behnia, M. (1997) Numerical modelling of threedimensional fly-ash flow in power utility boilers. Int. J. Numer. Methods Fl., 24 (8), 787–807. Coelho, P.J. (1999) Mathematical modelling of the convection chamber of a utility boiler – an application. Numer. Heat Transfer A, 36, 411–428. Patankar, S.V. and Spalding, D.B. (1974) A calculation procedure for the transient and steady-state behaviour of shell-andtube heat exchangers, in Heat Exchangers: Design and Theory Source Book (eds A.A. Afgan and E.U. Schluner), McGraw-Hill, New York, pp. 155–176. Gómez, A., Fueyo, N., and D
ıez, L.I. (2008) Modelling and simulation of fluidflow and heat transfer in the convective zone of a power-generation boiler. Appl. Therm. Eng., 28 (5–6), 532–546. Valentine, J.R., Shim, H.-S., Davis, K.A., Seo, S.-I., and Kim, T.-H. (2007) CFD evaluation of waterwall wastage in coalfired utility boilers. Energy Fuels, 21 (1), 242–249. Wang, X.H., Zhao, D.Q., He, L.B., Jiang, L.Q., He, Q., and Chen, Y. (2007) Modeling of a coal-fired slagging combustor: Development of a slag submodel. Combust. Flame, 149 (3), 249–260.

66 ASME Code and Standards PTC 4.1,

67

68

69

70

71

72

73

74 75

76

77

Performance Test Code Steam Generating Units, USA; 1972 (date of specific issue adopted: 1964). Triembomblia, V. (1979) Thermo Technical Test in Boilers, MIR, Moscow, in Russian. Kuprianov, V.I. (2005) Applications of a cost-based method of excess air optimization for the improvement of thermal efficiency and environmental performance of steam boilers. Renew. Sust. Energy Rev., 9, 474–498. Barroso, J., Barreras, F., Amaveda, H., and Lozano, A. (2003) On the optimization of boiler efficiency using bagasse as fuel. Fuel, 82, 1451–1463. Tanetsakunvatana, V. and Kuprianov, V.I. (2007) Experimental study on effects of operating conditions and fuel quality on thermal efficiency and emission performance of a 300-mw boiler unit firing Thai lignite. Fuel Process. Technol., 88, 199–206. Lee, C., Jou, C.G., Tsai, C., and Wang, H.P. (2007) Improvements in the performance of a medium-pressure-boiler through the adjustment of inlet fuels in a refinery plant. Fuel, 86, 625–631. Blanco, J.M. and Peña, F. (2008) Increase in the boiler’s performance in terms of the acid dew point temperature: environmental advantages of replacing fuels. Appl. Therm. Eng., 28, 777–784. Kuprianov, V.I. and Tanetsakunvatana, V. (2003) Optimization of excess air for the improvement of environmental performance of a 150 mw boiler fired with Thai lignite. Appl. Energy, 74, 445–453. Dukelow, S.G. (1991) The Control of Boilers, Instrument Society of America. Liptak, B.G. (ed.) (2003) Analytical instrumentation, in Instrument Engineers’ Handbook, Volume 1: Process Measurement and Analysis, CRC Press LLC, Boca Raton, Florida, pp. 1127–1764. Yamazoe, N. (2005) Toward innovations of gas sensor technology. Sensor. Actuat. B, 108, 2–14. Bhoga, S.S. and Singh, K. (2007) Electrochemical solid state gas sensors: An overview. Ionics, 13, 417–427.

References 78 Xu, Y., Zhou, X., and Sorensen, O.T.

79

80

81

82

83

84 85

86

87

88

(2000) Oxygen sensors based on semiconducting metal oxides: an overview. Sensor. Actuat. B, 65, 2–4. Kohse-Hoinghaus, K., Barlow, R.S., Ald
en, M., and Wofrum, J. (2005) Combustion at the focus: laser diagnostics and control. Proc. Combust. Inst., 30, 89–123. Hanson, R.K. and Jeffries, J.B.(March 6–9 2005) Advances in laser-based sensors for combustion systems. 6th Symposium on Smart Control of Turbulence, Tokyo. Lackner, M. (2007) Tunable diode laser spectroscopy in the process industries: a review (invited). Rev. Chem. Eng., 23 (2), 65–147. Ebert, V., Fernholz, T., Giesemann, C., Pitz, H., Teichert, H., Wolfrum, J., and Jaritz, H. (2000) Simultaneous diodelaser-based in situ detection of multiple species and temperature in a gas-fired power plant. Proc. Combust. Inst., 28, 423–430. Huang, Y., Yan, Y., and Riley, G. (2000) Vision-based measurement of temperature distribution in a 500-kW model furnace using the two-colour method. Measurement, 28, 175–183. Gaydon, A.G. (1974) The Spectroscopy of Flames, Chapman & Hall, London. Muruganandam, T.M., Kim, B.-H., Morrell, M.R., Nori, V., Patel, M., Romig, B.W., and Seitzman, J.M. (2005) Optical equivalence ratio sensors for gas turbine combustors. Proc. Combust. Inst., 30, 1601–1609. Docquier, N., Lacas, F., and Candel, S. (2002) Closed-loop equivalent ratio control of premixed combustors using spectrally resolved chemiluminescence measurements. Proc. Combust. Inst., 29, 139–145. Fuller, T.A., Flynn, T.J., Bailey, R.T., Daw, C.S., Finney, E.A., Kersting, K., Fields, R., Bermke, R., and Stallings, J. (2004) Field experience with the Flame Doctor system. Proceedings of the EPRI-DOEEPA-AWMA Combined Power Plant Air Pollutant Control Mega Symposium, Washington DC, Paper BR-1739. Khesin, M. (1997) Demonstration tests of new burner diagnostic system on a 650

89

90

91

92

93

94

95

96

97

98

MW coal-fired utility boiler. American Power Conference, 59th Annual Meeting. Wojcik, W. (2008) Application of fibreoptic flame monitoring systems to diagnostics of combustion process in power boilers. Bull. Pol. Acad. Sci., 2, 177–195. Lu, G., Gilabert, G., and Yan, Y. (2005) Vision based monitoring and characterisation of combustion flames. J. Physics: Conf. Ser., 15, 194–200. Hern
andez, R. and Ballester, J. (2008) Flame imaging as a diagnostic tool for industrial combustion. Combust. Flame, 155, 509–528. Demayo, T.N., McDonell, V.G., and Samuelsen, G.S. (2002) Robust active control of combustion stability and emissions performance in a fuel-staged natural gas-fired industrial burner. Proc. Combust. Inst., 29, 131–138. Ballester, J., Hern
andez, R., Sanz, A., Smolarz, A., Barroso, J., and Pina, A. (2009) Chemiluminescence monitoring in premixed flames of natural gas and its blends with hydrogen. Proc. Combust. Inst., 32, 2983–2991. Warriner, G.H., Logan, S., Pascoe, S., and Noblett, J.G. (2001) Full-scale implementation results for GNOCIS Plus. Proceedings of the EPA-DOE-EPRI Combined Power Plant Air Pollutant Control Symposium, Chicago IL. Partlow, B., Marz, P., Joffrion, V., and Grusha, J. (2003) Experience with advanced controls when combined with an ultra low NOx combustion system. PowerGen2003, Las Vegas, Nevada. Tan, C.K., Wilcox, S.J., and Ward, J. (2006) Use of artificial intelligence techniques for optimisation of co-combustion of coal with biomass. J. Energy Institute, 79, 19–25. Das, S.K., Godiwalla, K.M., Hegde, ShubhaS., Mehrotra, S.P., and Dey, P.K. (2008) A mathematical model to characterize effect of silica content in the boiler fly ash on erosion behaviour of boiler grade steel. J. Mater. Process. Technol., 204 (1–3), 239–247. Mbabazi, J.G., Sheer, T.J., and Shandu, R. (2004) A model to predict erosion on mild

j327

j 13 Industrial Steam Boilers

328

99

100

101

102

103

104

105

106

107

steel surfaces impacted by boiler fly ash particles. Wear, 257 (5–6), 612–624. Meuronen, V. (2000) Erosion durability of steels in steam boiler heat exchanger tubes. Wear, 240 (1–2), 164–167. Xie, J.J. and Walsh, P.M. (1995) Erosionoxidation of carbon steel and deposition of particles on a tube during combustion of coal-based fuels in an industrial boiler. Applications of Advanced Technology to Ash-Related Problems in Boilers, Proceedings of the Engineering Foundation Conference, Waterville Valley, New Hampshire, pp. 141–159. Raask, E. (1985) Mineral Impurities in Coal Combustion. Behavior, Problems and Remedial Measures, Hemisphere Publishing Corporation, New York. Bryers, R.W. (1992) Fireside behavior of mineral impurities in fuels from Marchwood 1963 to the Sheraton Palm Coast 1992. Proceedings of the Engineering Foundation Conference on Inorganic Transformations and Ash Deposition during Combustion, March 10–15, 1991, Palm Coast, Florida, pp. 3–68. Bryers, R.W. (1996) Fireside slagging, fouling and high-temperature corrosion of heat-transfer surface due to impurities in steam-raising fuels. Prog. Energy Combust., 22, 29–120. Benson, S.A., Steadman, E.N., Zygarlicke, C.J., and Erickson, T.A. (1995) Ash formation, deposition, corrosion and erosion in conventional boilers. Applications of Advanced Technology to Ash-Related Problems in Boilers, Proceedings of the Engineering Foundation Conference, Waterville Valley, New Hampshire, pp. 17–25. Winegartner, E.C. (1974) Coal fouling and slagging parameters. Prepared by the ASME Research Committee on Corrosion and Deposits from Combustion Gases. Couch, G. (1994) Understanding Slagging and Fouling in Pf Combustion, IEACR/72, IEA Coal Research, London, UK. Kalmanovitch, D.P. (1992) Predicting ash deposition from fly ash characteristics. Proceedings of the Engineering Foundation Conference on Inorganic

108

109

110

111

112

113

114

115

116

Transformations and Ash Deposition during Combustion, March 10–15, 1991, Palm Coast, Florida, pp. 661–677. Reg Bott, A. (1992) The assessment of fouling and slagging propensity in combustion systems. Proceedings of the Engineering Foundation Conference on Inorganic Transformations and Ash Deposition during Combustion, March 10–15, 1991, Palm Coast, Florida, pp. 499–521. Scott, D.H. (1999) Ash Behavior During Combustion and Gasification, IEA Coal Research, London. Hutchings, I.S., West, S.S., and Williamson, J. (1996) An assessment of coal-ash slagging propensity using an entrained flow reactor. Proceedings of the Engineering Foundation Conference on Applications of Advanced Technology to Ash-Related Problems in Boilers, July, 16–21, 1995, Waterville Valley, New Hampshire, pp. 201–222. Su, S., Pohl, J.H., Holcombe, D., and Hart, J.A. (2001) Slagging propensity of blended coals. Fuel, 80 (9), 1351–1360. Rushdi, A., Sharma, A., and Gupta, R. (2004) An experimental study of the effect of coal blending on ash deposition. Fuel, 83 (4–5), 495–506. Barroso, J., Ballester, J., and Pina, A. (2007) Study of coal ash deposition in an entrained flow reactor: Assessment of traditional and alternative slagging indices. Fuel Process. Technol., 88 (9), 865–876. Be
er, J.M., Sarofim, A.F., Barta, L.E., Toqan, M.A., Wood, V.J., and Teare, J.D. (1992) Transformation of mineral matter during pulverized coal combustion. Final Report MIT-EL 92-008, Combustion Research Facility, Energy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts. Ma, Z., Iman, F., Lu, P., Sears, R., Kong, L., Rokanuzzaman, A.S., McCollor, D.P., and Benson, S.A. (2007) A comprehensive slagging and fouling prediction tool for coal-fired boilers and its validation/application. Fuel Process. Technol., 88 (11–12), 1035–1043. Lawrence, A., Kumar, R., Nandakumar, K., and Narayanan, K. (2008) A Novel tool

References

117

118

119

120

121 122 123 124 125

126

127

128

for assessing slagging propensity of coals in PF boilers. Fuel, 87 (6), 946–950. Johansson, L., Svensson, J., Skog, E., Pettersson, J., Pettersson, C., Folkeson, N., Asteman, H., Jonsson, T., and Halvarsson, M. (2007) Critical corrosion phenomena on superheaters in biomass and waste-fired boilers. J. Iron. Steel Res. Int., 14, (5) (Supplement 1), 35–39. Li, L., Duan, Y., Cao, Y., Chu, P., Carty, R., and Pan, W. (2007) Field corrosion tests for a low chromium steel carried out at superheater area of a utility boiler with three coals containing different chlorine contents. Fuel Process. Technol., 88 (4), 387–392. Sidhu, T.S., Prakash, S., and Agrawal, R.D. (2006) Hot corrosion studies of HVOF NiCrBSi and Stellite-6 coatings on a Nibased superalloy in an actual industrial environment of a coal fired boiler. Surf. Coat. Technol., 201 (3–4), 1602–1612. Hansen, L.A., Nielsen, H.P., Frandsen, F.J., Dam-Johansen, K., Hørlyck, S., and Karlsson, A. (2000) Influence of deposit formation on corrosion at a straw-fired boiler. Fuel Process. Technol., 64 (1–3), 189–209. Liquid Mineral Group Inc . (2009) http:// www.liquidminerals.com/. Pentol GmbH (2009) http://www.pentol. com/. Bycosin AB (2009) http://www.bycosin. sc/. rb bertomeu, S.L . (2009) http://www. rbbertomeu.es/. Bugge, J., Kjær, S., and Blum, R. (2006) High-efficiency coal-fired power plants development and perspectives. Energy, 31 (10–11), 1437–1445. Viswanathan, R., Coleman, K., and Rao, U. (2006) Materials for ultra-supercritical coal-fired power plant boilers. Int. J. Press. Vess. Pip., 83 (11–12), 778–783. Perrin, I.J. and Fishburn, J.D. (2008) A perspective on the design of hightemperature boiler components. Int. J. Press. Vess. Pip., 85 (1–2), 14–21. Baukal, C. (2005) Everything You Need to Know About NOx, John Zink Co., http://www.metalfinishing.com/ editors_choice/articles/ 051109_John.htm.

129 United States Environmental Protection

130

131

132

133

134

135

136

137

138

139

140

Agency (2007) Course: Basic Concepts in Environmental Science, http://www.epa. gov/eogapti1/home. Friedrich, F.D., Razbin, V.V., and Burke, M. (1996) NOx control in industrial boilers. Combustion Canada 96 Conference: “The future and changing role of combustion in Canada – Efficiency and the environment”, pp. 38/1–38/23. The Texas Institute for Advancement of Chemical Technology (2000) A Summary of NOx Reduction Technologies, Special Report 1. W€ unning, J.A. and W€ unning, J.G. (1997) Flameless oxidation to reduce thermal NO-formation. Prog. Energy Combust., 23 (1), 81–94. Littlejohn, D., Majeski, A.J., Tonse, S., Castaldini, C., and Cheng, R.K. (2002) Laboratory investigation of an ultralow NOx premixed combustion concept for industrial boilers. Proc. Combust. Inst., 29 (1), 1115–1121. Cheng, T.S., Chao, Y.-C., Wu, D.-C., Yuan, T., Lu, C.-C., Cheng, C.-K., and Chang, J.-M. (1998) Effects of fuel-air mixing on flame structures and NOx emissions in swirling methane jet flames. Proc. Combust. Inst., 27 (1), 1229–1237. Staiger, B., Unterberger, S., Berger, R., and Hein F Klaus R.G. (2005) Development of an air staging technology to reduce NOx emissions in grate fired boilers. Energy, 30 (8), 1429–1438. Hansen, A.C., Zhang, Q., and Lyne, P.W.L. (2005) Ethanol-diesel fuel blends – a review. Bioresource Technol., 96 (3), 277–285. Ballester, J.M., Fueyo, N., and Dopazo, C. (1996) Combustion characteristics of heavy oil-water emulsions. Fuel, 75 (6), 695–705. Kadota, T. and Yamasaki, H. (2002) Recent advances in the combustion of water fuel emulsion. Prog. Energy Combust., 28 (5), 385–404. Morcos, V.H. (1995) Performance characteristics of a water-in-oil-fired steam generator for a 300-MW power plant. Energy, 20 (10), 1055–1066. Calabria, R., Chiariello, F., and Massoli, P. (2007) Combustion fundamentals of

j329

j 13 Industrial Steam Boilers

330

141

142

143

144

145

146

147

148

149 150 151 152 153

154

pyrolysis oil based fuels. Exp. Therm. Fluid Sci., 31 (5), 413–420. Syska, A.J., Beer, J.M., Togan, M.A., Moreland, D.P., and Benson, C.E. (1993) Staged air, recirculating flue gas low NOx burner, US Patent number: 5269679 Tackels, G., Roichy, P., and Vernaz, J. (2003) Combustion process and fuel injection burner for implementing such a process, US Patent Number: 6551095 Korenberg, J. (1991) Ultra-low NOx combustion apparatus, US Patent Number 4989549 Berg, L.D., Schindler, E.S., Garrad, N.J., and Halloran, J.J. (2004) Fuel staging methods for low NOx tangential fired boiler operation, US Patent number 6790031. Shihadeh, A.L., Toqan, M.A., Beer, J.M., Lewis, P.F., Teare, J.D., Jim
enes, J.L., and Barta, L. (1994) Low NOx emission from aerodynamically staged oil-air turbulent diffusion flames. ASME FACT-18, Combustion Modeling, Scaling and Air Toxins. Rebola, A. and Costa, M. (2002) Simultaneous reduction of NOx and particulate emissions from heavy fuel oilfired furnaces. Proc. Combust. Inst., 29 (1), 2243–2250. Li, S., Xu, T., Hui, S., and Wei, X. (2009) NOx emission and thermal efficiency of a 300 MWe utility boiler retrofitted by air staging. Appl. Energy, 86 (2), 1797–1803. International Flame Research Foundation (2009) Online Combustion Handbook, http://www.handbook.ifrf. net/handbook/. Callidus Technologies (2009) http:// www.callidus.com/. Coen Company (2009) http://www.coen. com/. John Zink Company (2009) http://www. johnzink.com/. NAO Inc . (2009) http://www.nao.com/. Chung, I., Strupp, C., and Karan, J. (2000) New fuel oil atomizer for improved combustion performance and reduced emissions. Proceedings of the 6th European Conference on Industrial Furnace and Boilers, Lisbon, Portugal. Chung, I., Karan, J., and Strupp, C. (2000) New low NOx oil-gas-combination burner

155

156

157

158

159

160

161

162

163

for process heater applications. Presented at American Flame Research Committee, Otawa, Canada. Sobiesiak, A., Rahbar, S., and Becker, H.A. (1998) Performance characteristics of the novel low-NOx CGRI burner for use with high air preheat. Combust. Flame, 115 (1–2), 93–125. Chung, I., Patel, A., Singh, P., Strupp, C., and Baukal, C.E. (2002) Innovations in atomization. Hydrocarbon Eng., 7 (3), 103–106. Delabroy, O., Haile, E., Lacas, F., Candel, S., Pollard, A., Sobiesiak, A., and Becker, H.A. (1998) Passive and active control of NOx in industrial burners. Exp. Therm. Fluid Sci., 16 (1–2), 64–75. Johnson, L.W., Youssef, C.F., and Kwan, Y. (1993) Retrofit of oil and gas low NOx burners to 400 MW utility boiler. Joint Symposium on Stationary Combustion NOx Control, Bal Harbor, Florida. Michele, G., Benelli, G., Ligasacchi, S., Alberti, M., and Santis, R. (1993) Development and industrial application of an oil and gas low NOx burner. Joint Symposium on Stationary Combustion NOx Control, Bal Harbor, Florida. Price, J.V., Karutski, J.J., Paschedag, A.E., Schindler, E.S., and Washington, K.T. (1993) Low NOx oil/gas burner retrofits and their effects on overall emissions and boiler performance. Joint Symposium on Stationary Combustion NOx Control, Bal Harbor, Florida. Folsom, B., Sommer, T., England, G., Ritz, H., Pratapas, J., Bautista, P., and Facchiano, T. (1996) Three gas reburning field applications: Final results and longterm performance. Combustion Canada 96 Conference: “The future and changing role of combustion in Canada – Efficiency and the environment”. Michele, G., Pasini, S., Bertacchi, S., Tarli, R., Benanti, A., Livraghi, M., Mantini, G., and Santis, R. (1993) Field evaluation of oil-reburning for NOx emission control in a 160 MWe tangentially oil-fired boiler. Joint Symposium on Stationary Combustion NOx Control, Bal Harbor, Florida. Bertran, C.A., Marques, C.S.T., and Filho, R.V. (2004) Reburning and burnout

References simulations of natural gas for heavy fuel oil combustion. Fuel, 83 (1), 109–121. 164 Su, S., Xiang, J., Sun, L., Hu, S., Zhang, Z., and Zhu, J. (2009) Application of gaseous fuel reburning for controlling

nitric oxide emissions in boilers. Fuel Process. Technol., 90 (3), 396–402. 165 DOE-NETL (2008) Pulverized coal oxycombustion power plants. report DOE/NETL-2007/1291

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14 Fuel Cells Xiao-Zi Yuan and Haijiang Wang

14.1 Introduction

A fuel cell is an electrochemical device that produces electricity from an externally supplied fuel and oxidant. Unlike the internal combustion engine (ICE), which converts the chemical energy of the fuel into mechanical energy, a fuel cell converts the fuel’s chemical energy directly into electric energy. The hydrogen–air fuel cell is the most popular, using hydrogen as the fuel and oxygen from air as the oxidant. Besides hydrogen, other fuels, including methanol, ethanol, and natural gas, can also be used directly in fuel cells. The most common method of fuel cell classification is based on the electrolyte used in the fuel cell. According to this system, fuel cells are usually classified into the five most common types: polymer electrolyte membrane fuel cell (PEMFC), alkaline fuel cell (AFC), phosphoric acid fuel cell (PAFC), molten carbonate fuel cell (MCFC), and solid oxide fuel cell (SOFC). Energy conversion within a fuel cell is realized through electrochemical reactions. The fuel is oxidized at the anode and gives up electrons, which travel through an outside circuit to reach the cathode, where oxygen is reduced by the electrons to form water. When electrons pass through an electric load that is connected to the outside circuit, electric power is generated. In a hydrogen–air fuel cell, water and heat are the only byproducts, and therefore fuel cells are very environment-friendly power generation devices. Indeed, the fuel cell is very likely to replace the ICE in the future, due to the “greenhouse gas” effect and the ever-increasing pollution arising from the combustion of fossil fuels. In addition, the energy conversion efficiency through a fuel cell is much higher than that through an ICE. A fuel cell has many advantages, such as silent operation, high power density, quick recharge (refueling), minimal maintenance, and broad application. Such features have been the driving force behind the extensive worldwide research activities into fuel cell technology during the past two decades. However, the fuel cell is, in fact, not a new technology. As early as 1839, William Robert Grove, the father of the fuel cell, discovered that reversing the electrolysis of

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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water could produce electricity when the correct catalysts were used. A few years later, he developed a bank of 50 fuel cells, which he called the “gaseous voltaic battery” [1]. Unfortunately, this great discovery did not lead to any further development for almost a century because the extremely low power density limited the cells’ practical applications. This impasse was not broken until 1937, when Francis T. Bacon started to develop fuel cells that could have practical applications. In 1959, he successfully developed and demonstrated a 5 kW fuel cell that powered a welding machine, a circular saw, and a forklift [2]. At approximately the same time, Willard Thomas Grubb and Leonard Niedrach at General Electric (GE) began the development of polymer electrolyte membrane (PEM) fuel cells. The successful application of fuel cell technology in the 1960s space mission was no doubt an immense boost to fuel cell development. The new era of fuel cell technology development began in the late 1980s and early 1990s, when Ballard Power Systems made rapid breakthroughs with their PEM fuel cell technology. Since then, fuel cell technology development, demonstration, and commercialization have been advancing rapidly, and a whole new fuel cell industry has emerged [1].

14.2 Theory 14.2.1 Principles

To elucidate the principles of fuel cells, the PEM fuel cell can be taken as an example. Figure 14.1 illustrates the key components and structure of a PEM fuel cell and its operational principle.

Figure 14.1 Diagram of the polymer electrolyte membrane fuel cell principle. CL: catalyst layer; GDL: gas diffusion layer. Modified from Ref. [3], with permission from Elsevier.

14.2 Theory

As shown in Figure 14.1, the key part of a single PEM fuel cell, known as the membrane electrode assembly (MEA), consists of a PEM with an anode catalyst layer (CL) and a cathode CL on either side. Adjacent to each CL is a gas diffusion layer (GDL). The PEM functions as a proton conductor and a separator of gases. On the anode side of the cell, hydrogen fuel is delivered to the anode through the flow channels of the anode plate. Similarly, on the cathode side of the cell, oxygen from the air is delivered to the cathode through the flow channels of the cathode plate. At the anode, the hydrogen oxidation reaction (HOR) occurs: H2 ! 2H þ þ 2e


ð14:1Þ

At the cathode, the oxygen reduction reaction (ORR) takes place: 1=2O2 þ 2H þ þ 2e
! H2 O

ð14:2Þ

The overall reaction of the fuel cell is: H2 þ 1=2 O2 ! H2 O

ð14:3Þ

Under standard conditions, the anode potential is Ea0 ¼ 0:00 V versus standard hydrogen electrode (SHE), while the cathode potential is Ec0 ¼ 1:229 V versus SHE. Therefore, the theoretical cell voltage under standard conditions can be calculated as E ¼ Ec0
Ea0 ¼ 1:229 V. Under other conditions, the theoretical cell voltage can be expressed as: E ¼ 1:229
0:85  10
3 ðT
298:15Þ þ 4:3085  10
5 T ½ln ðpH2 Þ þ 1=2 ln ðpO2 Þ ð14:4Þ

where T is the temperature in Kelvins, and pH2 and pO2 are the partial pressure (in atm) for hydrogen and oxygen, respectively. The actual value of the cell voltage is always lower than the theoretical value due to the combined effects of fuel crossover (hydrogen permeates through the electrolyte to the cathode) and parasitic oxidation reactions occurring at the cathode. Single cells produce less than 1 V of electricity, which is far from enough to power a vehicle. To generate a useful voltage, multiple cells must be assembled into a fuel cell stack. This can be achieved in a parallel and/or a series mode to supply feed gas to the stacks. For example, in the case of a parallel gas supply for a PEM fuel cell stack, all cells are fed in parallel from a common hydrogen/air inlet. In the serial configuration, the gas from the outlet of the first cell is fed to the inlet of the second cell, and so on, until the last cell, which helps to prevent nonuniform gas distribution. In order to avoid a large pressure drop, this arrangement can be used only for stacks with a small number of fuel cells. In addition to the stack, practical fuel cells such as those in fuel cell vehicles (FCVs) require several other subsystems and components to function as a system. Generally speaking, most fuel cell systems contain subsystems for processes such as hydrogen reforming or hydrogen purification, air supply (which includes air compressors or blowers as well as air filters), water management, and thermal management [1].

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14.2.2 Thermodynamics 14.2.2.1 Heat of Reaction The overall reaction of a fuel cell described in Equation 14.3 is an exothermic process: H2 þ 1=2 O2 ! H2 OðlÞ þ 286 kJmol
1

ð14:5Þ

The heat or enthalpy change of this reaction is calculated by the difference in the enthalpy change of formation between the products and reactants. Equation 14.5 is valid only at 25  C, and the value of 286 kJ mol
1 is known as hydrogen’s higher heating value (HHV). The heat of this reaction will become 242 kJ mol
1, which is known as hydrogen’s lower heating value (LHV), if hydrogen is combusted with oxygen and produces water vapor (101.3 kPa) at 25  C [4]. 14.2.2.2 Energy Efficiency Engine efficiency or, more broadly, the efficiency of an energy conversion device, can be defined in a number of ways, but it is usually done by comparing the useful energy output with the energy input. As the ICE is a heat engine, its efficiency is limited by the Carnot cycle, with the overall efficiency being determined by the difference between the lower and upper operating temperatures of the engine. For example, if the upper temperature of the heat engine is T1 and the lower operating temperature is T2 (which is assumed to be not lower than room temperature), then the Carnot efficiency, which defines the engine’s maximum efficiency, can be calculated by: Carnot efficiency ¼

T1
T2  100% T1

ð14:6Þ

where both the temperatures are in Kelvins. Thus, the greater the temperature difference between the upper and the lower operating temperatures, the greater the thermodynamic efficiency. Therefore, a high thermal stability of the engine material can allow a higher upper operating temperature, and thus a higher efficiency. For a steam turbine operating at 300  C (573 K) with water exhausted at 50  C (323 K), the Carnot efficiency is 44%. Usually, the actual efficiency of an ICE is much lower than the Carnot efficiency due to its nonideal thermodynamic process. As a result, the average efficiency of ICEs is about 18–20%. For a fuel cell, the useful energy output is the electrical energy produced, and the energy input is the heat of the hydrogen combustion reaction. The heat of this reaction at 25  C is 286 kJ mol
1 (HHV) or 242 kJ mol
1 (LHV). Assuming that all of the Gibbs free energy can be converted into electrical energy, the theoretical efficiency of a fuel cell using the HHV is: g¼

DG0 237:1 kJmol
1 ¼ ¼ 83%ð25  C; 1 atmÞ DH0 286 kJmol
1

ð14:7Þ

where DG0 is the change in the Gibbs free energy of the reaction (the difference between the Gibbs free energy of the products and of the reactants) under standard

14.2 Theory

j337

conditions, and DH0 is the standard enthalpy change of this reaction (25  C, 1 atm), which is the heat of the reaction under standard conditions. If the numerator and denominator in Equation 14.7 are both divided by nF, then the fuel cell efficiency can be expressed as a ratio of two potentials:
DG0 DG 1:23 ¼ 83% ð25  C; 1 atmÞ g¼ ¼ nF ¼ DH0
DH0 1:48 nF 0

ð14:8Þ

where 1.23 V is the theoretical cell voltage and 1.48 V is the thermal-equivalent voltage corresponding to hydrogen’s HHV (this value becomes 1.25 V if based on hydrogen’s LHV). Note that the voltage of the fuel cell is related to the change in the Gibbs free energy of the reaction: DG0 ¼
nFE

ð14:9Þ
1

where F is the Faraday constant (96 485 C mol ), E is the theoretical cell voltage (also known as reversible cell voltage), and n is the number of electrons transferred in the reaction. Figure 14.2 compares the theoretical efficiency of a fuel cell with the Carnot efficiency at the standard pressure, but at different temperatures. It can be clearly seen from the figure that as the operating temperature increases, the Carnot efficiency increases but the efficiency of the fuel cell decreases. At about 700  C, the two types of energy conversion device have the same theoretical efficiency. Above 700  C, the combustion engine is more efficient, but below 700  C the fuel cell 100 Fuel cell liquid product

Theoretical efficiency (%)

90 80 70

Fuel cell vapor 60

product Carnot efficiency, 50°C

50

exhaust 40 30 20 0

100

200

300

400

500

600

700

800

900

Temperature (°C) Figure 14.2 Theoretical efficiency of a H2 fuel cell at standard pressure based on HHV. Reproduced from Ref. [5], with permission from John Wiley & Sons Inc.

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efficiency is higher than the Carnot efficiency, and the lower the temperature, the greater the efficiency difference. The actual efficiency of a fuel cell is defined as the actual voltage divided by the thermal-equivalent voltage [5]: Cell efficiency ¼ mf

Vcell  100% ðbased on HHVÞ 1:48

ð14:10Þ

Cell efficiency ¼ mf

Vcell  100% ðbased on LHVÞ 1:25

ð14:11Þ

and

where mf is the fuel utilization coefficient. The addition of this coefficient in Equations 14.10 and 14.11 is necessary because a small portion of the fuel fed to the cell simply passes through the cell unconsumed. The fuel utilization coefficient is defined as: mf ¼

Fuel reacted Fuel input

ð14:12Þ

The fuel utilization depends on the design of the fuel cell. For open-end and periodic purge designs, fuel utilization can reach about 90%, whereas for dead-end and fuel recycling designs it can reach almost 100%. As will later become apparent, the actual cell voltage changes with the load, as does the actual cell efficiency. For PEM fuel cells intended for vehicles, the nominal operational current density is approximately 1 A cm
2, at which the cell voltage is about 0.65 V. Thus, the fuel cell efficiency under the nominal operational condition is about 44%. Since vehicles operate at different loads, on average the actual efficiency can be higher than 50%, based on current PEM fuel cell technology. The target for commercial fuel cell products for transportation applications has been set by the United States Department of Energy (DOE) at 60%. 14.2.3 Reaction Kinetics 14.2.3.1 The Butler–Volmer Equation The reactions in a fuel cell are typical electrochemical reactions. The reaction kinetics can be described by the Butler–Volmer equation, which relates the current to the electrode potential:     
anFg ð1
aÞnFg
exp ð14:13Þ i ¼ i0 exp RT RT

where i ¼ the current density (A cm
2) i0 ¼ the exchange current density (A cm
2) R ¼ the universal gas constant (8.314 J mol
1 K) T ¼ the temperature (K) F ¼ the Faraday constant (96 485 C mol
1)

14.2 Theory

n ¼ the electron transfer number a ¼ the transfer coefficient g ¼ the overpotential (V) The exchange current density is a measure of how quickly the redox couple exchanges electrons with the electrode at equilibrium, which is dependent on many factors, including: the nature of the redox couple; the properties of the electrode; the medium in which the reaction takes place; and the reaction conditions. The higher the exchange current density, the faster the electrode kinetics. On a platinum electrode in a PEM fuel cell, the exchange current density for the HOR is about 0.1 A cm
2, and for the ORR about 6 mA cm
2 [6]. The HOR is clearly much faster than the ORR, which is why in a PEM fuel cell the cell polarization is assumed to be entirely due to the cathode reaction. The transfer coefficient is a measure of the position of the activated complex along the reaction coordinates for an elementary electron transfer reaction, which has the value of 0.5 for most such reactions. For complicated electron transfer reactions, such as the HOR and ORR in fuel cells that involve many elementary reaction steps, the transfer coefficient has lost its original physical meaning. It may involve many processes, such as the adsorption equilibrium constant of the reactants and the desorption equilibrium constant of the products. It has been found that on a platinum electrode the transfer coefficient is a temperature-dependent parameter for O2 reduction, but for H2 oxidation it seems to be independent of temperature; a value of 0.5 has been widely reported [1]. At low overpotential, the Butler–Volmer equation can be simplified as:
g=i ¼

RT ¼ Rct nFi0

ð14:14Þ

where Rct is the charge transfer resistance, an important kinetic parameter that describes the speed of the electrode reaction. The current–potential relationship described in Equation 14.14 indicates that when the overpotential is low, it is linearly dependent on current density. At very high overpotential, the Butler–Volmer equation can be simplified in a different form: g ¼ a þ b log i

ð14:15Þ

where a¼

2:303RT log i0 anF

ð14:16Þ

2:303 RT anF

ð14:17Þ

b¼


Equation 14.15 is the well-known Tafel equation, an empirical expression relating the overpotential to the current for an electrode reaction. The Tafel current–potential relationship indicates that, when the overpotential is high it is linearly dependent on the logarithm of the current density. The slope is called the Tafel slope, and the steeper

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it is, the slower the reaction kinetics. For ORR in a PEM fuel cell, the Tafel slope is about 70 mV per decade at 80  C, a typical operational temperature for PEM fuel cells. 14.2.3.2 Polarization Curve The most common approach to characterize the performance of a fuel cell is to record the polarization curve [voltage–current (V–I) curve] by measuring the cell voltage at different current densities, or vice versa. Figure 14.3 shows the typical polarization curves of a PEMFC designed by the National Research Council of Canada’s Institute for Fuel Cell Innovation (NRC-IFCI), measured at different operating temperatures. The MEA used for this fuel cell was made of SGL GDL and Gore 5510 catalyst-coated membrane (CCM). The cell was operated under the conditions of zero back-pressure, 100% humidification for both anode and cathode, a constant hydrogen flow rate of 5 SLPM (standard liter per minute), and a constant air flow rate of 25 SLPM. As can be seen from Figure 14.3, the cell voltage drops when current density increases such that, at a specific current density, the higher the cell voltage the better is the fuel cell’s performance. As the power output is the product of the voltage and the current, the power density of the fuel cell can easily be plotted from the polarization curves. Unlike the polarization curves, that monotonically drop with increasing current density, the power density curves always go through a maximum (usually the nominal design point), at which the fuel cell has the highest power output. As operating temperature increases, cell voltage rises, even though there is a slight decrease in the theoretical cell voltage, as mentioned above. This result is due to increased electrode kinetics, a higher proton conductivity of the membrane,

Figure 14.3 Polymer electrolyte membrane fuel cell polarization curves.

14.3 Types of Fuel Cell

increased mass transport, and better water management. Therefore, a high-temperature PEM fuel cell is quite appealing, provided that the component materials are durable. 14.2.3.3 Voltage Losses The voltage loss (voltage drop from the open circuit voltage; OCV), shown in Figure 14.3, is due to the three distinct electrode processes that occur in the fuel cell when current density increases: . .

.

Activation polarization: This refers to an overpotential arising from electrode kinetics, and is mainly dependent on the CL structure. Ohmic polarization: This refers to the voltage drop due to ionic and electric resistances; the major contributors to ohmic polarization are membrane resistance and contact resistances between the layers. Concentration polarization: This is due to mass transport limitations; the GDL structure, flow field design, and water management are the most important factors affecting concentration polarization.

The three polarizations dominate in different current density regions, so that the polarization curve is usually divided into three regions. As shown in Figure 14.3, at low current densities (e.g., <200 mA cm
2), the cell voltage drops exponentially with the current density, due mainly to the sluggish kinetics of the ORR. In the middle region (i.e., current density between 200 and 1000 mA cm
2), the voltage drops almost linearly; the voltage loss in this region is mainly caused by ohmic resistance. At high current densities (e.g., >1000 mA cm
2) a sharp voltage drop is observable, due to the mass transport limitations of the reactant gas through the pore structure of the GDLs and CLs [1]. In summary, the output voltage of an operating single cell, Ecell , can be expressed as: Ecell ¼ EOCV
gact
gohmic
gcon

ð14:18Þ

where Ecell is the cell voltage under a certain operating condition, EOCV represents the fuel cell’s OCV, gact is the kinetic loss (activation polarization), gohmic is the ohmic loss (ohmic polarization), and gcon is the mass transport loss (concentration polarization). For a typical PEM fuel cell designed for transportation applications, the kinetic loss represents the biggest voltage loss under nominal operating conditions. Therefore, improving catalyst activity can effectively improve fuel cell performance.

14.3 Types of Fuel Cell

To date, several different types of fuel cell have been developed, ranging from lowtemperature to high-temperature, and from solid polymer electrolyte to ceramic electrolyte. The major types of fuel cell are PEMFC, AFC, PAFC, MCFC, and SOFC, based on the electrolyte used, and the optimum operating temperatures of these fuel cells are shown in Figure 14.4. In general, the PEMFC, AFC, and PAFC are operated

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Figure 14.4 Fuel cell types and their optimum operating temperatures [7].

at temperatures less than 300  C (hence, they are called low-temperature fuel cells), whereas MCFC and SOFC are operated at high temperatures (hence high-temperature fuel cells). The advantages and disadvantages of these fuel cells are compared in Table 14.1. 14.3.1 PEMFCs

The key components of a PEMFC, as seen in Figure 14.1, include the membrane, CLs, GDLs, and flow field plates. The materials for these key components in state-ofthe-art PEM fuel cells are summarized below.

Table 14.1 Advantages and disadvantages of different fuel cell types.

Fuel cell type

Advantages

Disadvantages

PEMFC

.

.

. . . .

AFC PAFC

. . .

MCFC

. .

SOFC

. . . .

Rapid start-up High power density Simple construction Easy operation Wide applications Fast ORR kinetics, so non-noble metal catalyst can be used High CO tolerance Suitable for electricity and heat cogeneration Fuels other than hydrogen can be used directly Noble metal catalysts not required Fuels other than hydrogen can be used directly High power density Noble metal catalysts not required Suitable for electricity and heat cogeneration

Noble metal catalyst required Gas humidification required . Low contaminant tolerance .

.

CO2 contamination Strong corrosive electrolyte . Strong corrosive electrolyte . High material stability required . Limited to stationary applications . Severe materials requirements . Increased corrosion . Slow start-up .

. .

High material stability required Complicated construction

14.3 Types of Fuel Cell

The NafionÒ membrane, a sulfonated perfluorinated polymer membrane produced by DuPont, is the most commonly used PEM for this type of fuel cell [8]. The water content of the membrane drastically affects its conductivity, causing a drastic increase in proton conductivity. At 70  C, when the membrane is fully humidified, the conductivity approaches 0.1 S cm
1. This water-content-dependent conductivity of the Nafion membrane limits the PEMFC’s operating temperature to below 100  C, whilst a need for external humidification complicates the fuel cell system. In order to reduce the membrane’s ionic resistance, thin membranes in the range of 18–25 mm are commonly used. Attempts have been made to raise the PEM fuel cell operating temperature up to 300  C by replacing the Nafion membrane with an acid-doped polybenzimidazole (PBI) membrane. However, the limited durability of the PBI fuel cell makes its future uncertain. Strategies used by the DOE to develop hightemperature membranes have included the development of novel hydrocarbon membranes and the modification of perfluorinated membranes. The PEMFC requires a noble metal catalyst such as Pt for the anode and cathode reactions, and carbon-supported platinum appears to be the only catalyst suitable for high-power density PEM fuel cells. Decades of effort have led to a significant reduction in the loading of the platinum catalyst, from 10 mg cm
2 to 0.4 mg cm
2, while at the same time the cell performance has greatly improved. Yet, even at this low platinum loading the catalyst constitutes 55% of the fuel cell system cost, such that its replacement is the most important area of research in PEMFC development. Although numerous Pt alloy catalysts have been studied, few have exhibited catalytic activity comparable to platinum. The use of various nonprecious metal catalysts has also been reported, but their catalytic activities have been too low to enable any practical applications of PME fuel cells in the near future. The GDL used for PEM fuel cells is usually a carbon-based porous material, such as carbon fiber paper or carbon cloth, about 0.1–0.5 mm thick. Typically, this has a duallayer structure, comprised of a macroporous carbon substrate and a thin microporous layer. The GDL plays several roles: collecting current; physically supporting the catalyst layer; and providing the transport media for gases, water, and heat. Another key component of PEM fuel cells is the bipolar plate. This is commonly made from carbon composite or stainless steel, though each material has its own advantages and it is difficult to predict which will prevail commercially. The functions of the bipolar plate include separating the reactant gases, providing flow fields for the reactant gases and coolant, and transporting water and heat. The flow field design of bipolar plates can significantly affect PEM fuel cell performance. Common flow field designs include serpentine channel, straight channel, and interdigitated flow fields. 14.3.1.1 H2/Air PEMFCs A H2/air PEMFC system consists of the PEMFC stack and the balance of plant, including the hydrogen system, air system, anode and cathode humidifier, and cooling system. During operation, hydrogen is introduced into the anode by the hydrogen system, while air is introduced into the cathode by the air system. The hydrogen gas for PEMFCs must be quite pure, containing <10 ppm CO. A state-of-

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the-art PEM fuel cell can produce power of about 0.65 W cm
2 active area; hence, small PEM fuel cells can be fabricated that produce several watts of power, and larger cells that produce megawatts. The PEM fuel cell has very broad applications, including vehicles, stationary power sources, and portable electronics. 14.3.1.2 Direct Liquid Fuel Cells (DLFCs) Liquid fuels such as methanol, ethanol, and formic acid have much higher energy densities than hydrogen. As these fuels can be fed directly to the anode of a PEM fuel cell to produce power, they are termed direct liquid fuel cells (DLFCs), and include the direct methanol fuel cell (DMFC), direct ethanol fuel cell (DEFC), and direct formic acid fuel cell (DFAFC). As the direct oxidation of liquid fuel at the anode is kinetically a very difficult electrode process, the DLFCs typically operate at very low current densities. Therefore, unlike the H2/air fuel cell, DLFCs are usually used to generate power in the range of several watts to several hundreds of watts. Interest in DLFCs stems from the demand for high-power batteries in increasingly sophisticated portable electronics. For example, a DMFC can generate five times the power density of the most advanced lithium ion battery, and it can be “recharged” in a second (i.e., refueled by replacement with a new fuel cartridge). The materials used in DLFCs are very similar to those in H2/air PEMFCs, except that a much higher platinum catalyst loading is needed at the anode to facilitate the liquid fuel oxidation reaction. In terms of design, the DLFC stack design is also similar to that of H2/air PEMFCs. A DLFC does not require external humidification because an aqueous solution is used for the anode reaction; nor does it require a cooling system, as so little heat is generated. A DLFC often uses air-breathing rather than a forced air flow. The electrochemical reactions in a DMFC are as follows: Anode reaction : Cathode reaction : Overall reaction :

CH3 OH þ H2 O ! CO2 þ 4 H þ þ 6e
O2 þ 4H þ þ 4e
! 2 H2 O 3=2O2 þ CH3 OH ! 2 H2 O þ CO2

ð14:19Þ ð14:20Þ ð14:21Þ

The theoretical cell voltage of a DMFC under standard conditions is 1.20 V. Currently, the DMFC is the most promising DLFC and has attracted the most research attention; however, its widespread use has raised concerns due to the toxicity of methanol. Ethanol, on the other hand, is a very safe, renewable biofuel that is ideal for DLFCs, although the anode oxidation of ethanol is more challenging than that of methanol. The electrode reactions of a DEFC are as follows [9]: Anode reaction : Cathode reaction : Overall reaction :

CH3 CH2 OH þ 3 H2 O ! 2 CO2 þ 12 H þ þ 12e
þ




3 O2 þ 12 H þ 12e ! 6 H2 O CH3 CH2 OH þ 3 O2 ! 2 CO2 þ 3 H2 O

ð14:22Þ ð14:23Þ ð14:24Þ

14.3 Types of Fuel Cell

The theoretical cell voltage of a DEFC under standard conditions is 1.145 V. So far, the DEFC has proved to be less popular than the DMFC because an effective anode catalyst has not yet been found; however, if a breakthrough were to occur then the use of DEFCs would surely surpass that of DMFCs. In the case of a DFAFC, the electrode reactions are as follows: Anode reaction :

HCOOH ! CO2 þ 2 H þ þ 2e


ð14:25Þ

Cathode reaction :

3 O2 þ 12 H þ þ 12e
! 6 H2 O

ð14:26Þ

Overall reaction :

HCOOH þ 1=2 O2 ! CO2 þ H2 O

ð14:27Þ

The theoretical cell voltage of a DFAFC under standard conditions is 1.4 V [10]. As can be seen from the above reactions, water is not involved in the anode reaction of a DFAFC, and therefore the fuel concentration can be as high as 90%. Thus, the power density of a DFAFC is much greater than that of a DMFC or DEFC. In addition, the oxidation reaction for formic acid is much faster. Whilst these are attractive features [11, 12], formic acid is toxic (like methanol) and is also highly corrosive, which consequently greatly limits DFAFC applications. 14.3.2 Alkaline Fuel Cell (AFC)

The AFC uses concentrated KOH solution absorbed into a porous matrix as the electrolyte, and therefore charge transport within the electrolyte is effected by the movement of OH
ions from the cathode to the anode. When H2 and air are used as fuel and oxidant, respectively, the efficiency of the AFC may be as high as 60%. The AFC can operate over a wide temperature range, from sub-zero degrees Celsius to about 250  C. The operational principle of an AFC, as well as its cell structure and key components, are shown in Figure 14.5 [13]. The electrochemical reactions of an AFC are as follows: O2 þ 2 H2 O þ 4e
! 4 OH
E 0 ¼ 0:401 V

ð14:28Þ

Anode reaction :

H2 þ 2 OH
! 2 H2 O þ 2e
E0 ¼
0:828 V

ð14:29Þ

Overall reaction :

O2 þ 2 H2 ! 2 H2 O Ecell ¼ 1:229 V

ð14:30Þ

Cathode reaction :

The theoretical cell voltage is the same for both AFCs and PEMFCs, but in an alkaline environment the ORR is much faster. Therefore, a wide range of catalysts can be used in AFCs, since not only platinum group metals but also low-cost metals such as Ni and Ag can catalyze the ORR. The fatal drawback of the AFC is its sensitivity to CO2; exposure to CO2 will gradually convert the potassium hydroxide into potassium carbonate, thereby eliminating the electrolyte’s ionic conductivity. In order to avoid damage from CO2, both the fuel and air must be vigorously scrubbed. Cell designs using a circulating electrolyte have also been applied to prevent destruction of the

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Figure 14.5 Alkaline fuel cell structure. Reproduced from Ref. [13], with permission from International Journal of Hydrogen Energy.

electrolyte by CO2. In addition to the CO2 problem, the highly corrosive electrolyte makes the chemical stability of the cell components a major issue. The AFC was selected by the US space program during the 1960s, and remained a very competitive technology throughout the 1970s and 1980s. Subsequently, however, due to the rapid advancement of PEMFCs, AFCs have fallen out of favor. Nonetheless, this type of cell does have its advantages and still has the potential to succeed in certain applications, as it can provide high power density and has a long lifetime [13]. 14.3.3 Phosphoric Acid Fuel Cell (PAFC)

The electrolyte used in a PAFC is phosphoric acid immobilized within a porous matrix (e.g., a Teflon-bonded silicon carbide). A PAFC operates between 150 and 200  C, while the electrode reactions are the same as those that occur in a H2/air PEMFC. The techniques for fabricating PAFC electrodes borrow heavily from AFC technology. During the early research stages, noble metal blacks (unsupported noble metals) were used as the electrode catalyst, but currently carbon-supported Pt or Pt alloy electrocatalysts are commonly used for both the electrodes, so as to reduce the catalyst loadings. As in the H2/air PEMFCs, porous carbon fiber paper and graphite plates are commonly used as the gas diffusion layer and the bipolar plates, respectively. The PEM fuel cell with the PBI membrane is actually a reproduction of the PAFC design [14].

14.3 Types of Fuel Cell

Unlike PEMFCs, PAFCs are much less susceptible to CO impurities in the hydrogen stream. In addition, the operating temperature of 150–200  C reduces the complexity of the power plants and makes the fuel cell suitable for cogeneration. However, the electrolyte’s corrosive nature and the high operating temperature introduce more material challenges [7]. The PAFC is a relatively mature technology, and was the first fuel cell technology to be commercialized. The PC-25 PAFC manufactured by UTC Fuel Cells, a division of United Technology Corporation, was the first available commercial fuel cell unit, and served as a model for fuel cell applications. The PC-25 has been installed in a wide variety of environments, including hospitals, hotels, large office buildings, manufacturing sites, and wastewater treatment plants [15]. 14.3.4 Molten Carbonate Fuel Cell (MCFC)

The MCFC operates at approximately 650  C in order to achieve sufficient conductivity in the carbonate electrolyte. However, low-cost metal cell components must be used in such heat. The high operating temperature offers several advantages, including a high efficiency, the direct use of various fuels, and no need for noble metal catalysts in the electrochemical oxidation and reduction reactions. However, a major challenge for MCFCs is electrolyte management to retain long-term performance. Other issues include hardware corrosion, cathode dissolution, and low power density. The state-of-the-art cell structure of a MCFC is depicted in Figure 14.6, along with the anode and cathode reactions. The overall cell reaction of a MCFC is: H2 þ 1=2 O2 þ CO2 ! H2 O þ CO2

ð14:31Þ

During the mid-1960s, precious metals were frequently used as electrode materials, but soon were replaced by Ni-based alloys at the anode, and oxides at the cathode. The major challenges with Ni-based anodes and NiO cathodes are structural stability (sintering and mechanical deformation of the porous Ni-based anode under compressive load leads to performance decay) and NiO dissolution. Since the mid-1970s, the materials for the electrodes and electrolyte (molten carbonate/LiAlO2) have remained the same. One important achievement in MCFC technology during the 1980s was an improvement of the electrolyte structure such that, over the past thirty years, the performance of a single cell has increased from about 10 mW cm
2 to >150 mW cm
2. Typical MCFCs will generally operate in the range of 100–200 mA cm
2 at a cell voltage of 750–900 mV. MCFCs have been developed for natural gas and coal-based power plants used in industrial, electrical utility, and military applications. To date, MCFC stacks with cell areas up to 1 m2 and with a power output of over 250 kW have been developed and produced, for example, the model FCE-300 (manufactured by Fuel Cell Energy)[15].

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Figure 14.6 State-of-the-art cell structure of a molten carbonate fuel cell [15].

14.3.5 Solid Oxide Fuel Cells (SOFCs)

SOFCs operate at 600–1000  C. The electrolyte consists of solid oxides through which oxide ion transports the current flow. SOFCs are composed entirely of solid-state materials, the most commonly used solid electrolyte being yttria-stabilized zirconia (YSZ), an ionically conducting oxide membrane. The typical anode is Ni-ZrO2 cermet (e.g., YSZ/Ni), and the typical cathode is a perovskite mixed conductor (e.g., LaxSr1–xMnO3, LaxSr1–xFeO3, LaxSr1–xCrO3). SOFCs have two types of cell designs, namely tubular and planar. Three examples of tubular cell design in SOFCs, a unique feature of these cells, are shown in Figure 14.7a–c [15]. Due to their high efficiency, low sensitivity to fuel impurities, and fuel flexibility [16], SOFCs have been used in large-scale stationary power plants, smaller homescale power plants, and portable power generators [17], with a capacity range of 2 kW to 100 MW. Although an SOFC system is not the first choice for transport applications, small-scale systems have been developed for use as auxiliary power units in cars [18]. Unfortunately, the high operating temperature of SOFCs requires highly

14.3 Types of Fuel Cell

Figure 14.7 Three examples of tubular solid oxide fuel cell design [15]. (a) Current flow around the tube; (b) Current flow along the tube; (c) Segmented in series.

stable materials, whilst high cost and low durability/reliability represent the two major barriers for their commercialization. Thus, the key technical challenges for SOFCs today are to develop suitable low-cost materials and low-cost fabrication processes for ceramic materials. While PEMFC research is heading towards high

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operating temperatures (>100  C), SOFC research is attempting to reduce the operating temperature (to 500–850  C). Low-temperature SOFCs have many advantages, such as a wider choice of low-cost component materials, improved stability, and increased design flexibility [19].

14.4 Fuel Cell Applications

Since the start of the twentieth century, ICEs have been used to power vehicles and generators to generate electricity. Attempts to apply fuel cells in power generation began over twenty years ago. Because fuel cells can generate power over a wide range, from a fraction of a watt to hundreds of kilowatts, they can be used in almost any application. For example, fuel cells have been applied to local distribution power stations (>1 MW), to large transportation vehicles such as submarines and buses (100 kW–1 MW), to transportation vehicles such as cars and motorcycles, to back-up power (1 kW–100 kW), to simple riding devices such as bicycles, scooters, and wheelchairs (1 kW–10 kW), to uninterrupted power supply (UPS) (100 W–1 kW), and to portable power devices, ranging from military equipment to cell phones (<100 W) [1]. Aside from the specialized examples of fuel cells in military and space programs, fuel cell usages can be categorized into four main groups: stationary; transportation; back-up; and portable power. PEMFCs can be utilized in all of these fields, especially transportation, because the cells can be fabricated for various power ranges. Indeed, numerous prototype automobiles, buses, utility vehicles, scooters, and bicycles have already been developed using PEMFCs as power sources. The use of DMFCs in portable electronics, such as laptop computers, video cameras, and mobile phones, has also been demonstrated [18]. Fuel cells in stationary power generation offer tremendous flexibility in power supply, from individual homes or complex buildings to entire communities. PAFCs have been favored for stationary applications with a combined heat and power cogeneration. Fuel cells are more attractive as back-up power generators than ICE generators (due to noise, fuel, reliability, and maintenance considerations) or batteries (due to weight, lifetime, and maintenance considerations), while small fuel cells used as portable power generators offer several advantages over conventional batteries [1]. Some examples of these fuel cell applications are shown in Figure 14.8. In addition to the principal application of generating electricity for various purposes, fuel cells have one other special feature – namely, that they can be used to create useful materials at the fuel cell anode or cathode, while simultaneously producing power, rather than consuming it. The principle of a fuel cell reactor that concurrently produces value-added chemicals and energy is shown in Figure 14.9. In this case, a chemical and electricity cogeneration system mainly consists of a conventional fuel cell or fuel cell reactor, an external load, and a subsystem to recover the product chemicals. From the economic and/or environ-

14.4 Fuel Cell Applications

Figure 14.8 Examples of fuel cell applications. (a) Fuel cell buses: Ballard Mark 902Ô FC stack; (b) Stationary fuel cell: EBARA Ballard 1 kW Japan Cogeneration System; (c) Backup power:

the air-cooled Ballard Mark 1020 ACS fuel cell stack; (d) Lift trucks: Ballard Mark 9 SSL fuel cell stack [20]. Image courtesy of Ballard Power Systems, Inc.

mental point of view, this fuel cell application could become commercially attractive for industries. By using fuel cells, a variety of chemicals can be produced including inorganic materials and organic compounds (e.g., hydrocarbons, benzene, alcohols, ketones, and their derivatives). Reactions in fuel cells involve hydrogenations [22–24], dehydrogenations, halogenations, and oxidations, and these reactions are normally quite selective. Consequently, hydrogen peroxide and valuable organic chemicals can be obtained from PEMFCs, while AFCs can also be used to produce hydrogen peroxide. The selective oxidation of hydrocarbons and aromatic compounds, and the production of industrial compounds such as cresols, have been reported for PAFCs, whilst acetaldehyde with high product selectivity from ethanol oxidation can be achieved by using MCFCs. High yields of valuable industrial inorganic compounds such as nitric oxide can be produced with SOFCs [21, 25].

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Figure 14.9 Principle of a fuel cell reactor for chemical and energy cogeneration. Reproduced from Ref. [21], with permission from Elsevier.

14.5 Outlook

Fuel cells have the ability to directly convert the chemical energy of fuels such as hydrogen, methanol, ethanol, formic acid, and methane into electricity. These technologies have several advantages over direct combustion devices, including high efficiency, low/zero emissions, and high power density, and such features will surely lead to fuel cells becoming highly competitive with ICEs in the future. In addition, the rapid depletion of fossil fuels, coupled with increasing concerns over global warming and environmental pollution, are making powerful calls for an accelerated commercialization of fuel cell technology. Indeed, today almost all major automotive companies are developing FCVs. Demonstrations of fuel cell technology relating to transportation and other areas have been quite successful, with two examples of hydrogen and fuel cell demonstration projects being the Hydrogen Highway in British Columbia, Canada, and the California Hydrogen Highway Network [1]. These two demonstration and market development programs are aimed at promoting the application of fuel cell technology by providing a vehicle-fueling infrastructure. Today, several barriers to the commercialization of fuel cell technology remain, the main one being a lack of any hydrogen infrastructure, including hydrogen production, transport, and storage, and fueling stations. In order to develop a hydrogen infrastructure similar to the current fossil fuel infrastructure will require not only a long time but also enormous capital investment. In addition, the cheapest hydrogen currently derives from natural gas reforming, and using fossil fuels means that the well-to-wheel efficiency is less competitive. Likewise, the production of hydrogen from fossil fuels does not help to reduce CO2 emissions, and therefore a parallel development of low-cost hydrogen production from alternative technologies is required. The electrolysis of water by hydro, wind, nuclear, and photovoltaic power

14.6 Summary

each represent very promising possibilities for low-cost hydrogen sources; hydrogen from renewable biomass would also be low-cost. Another challenge for fuel cell commercialization is their cost which, at present, is still much greater than that of an ICE [26]. Low-temperature fuel cells require noble metals as catalysts, and Pt-based catalysts – which are very expensive – constitute the major cost of a PEMFC, although other key components such as the membrane, bipolar plates, and GDL are also costly. Last, but not least, fuel cell durability is one of the most important technical challenges to show fuel cells as viable commercial products. The required lifetimes for fuel cells vary, depending upon their application. For example, a total lifetime of at least 40 000 h and 8000 h of uninterrupted service are required for stationary applications, and a lifetime of at least 20 000 h and 6000 h is required for buses and automobiles, respectively [27, 28]. At present, PEMFC technology can achieve around 2000 h for cars and around 10 000 h for stationary generators. It is also important to consider the durability of the materials to be used when developing low-cost components. In order to help bridge the gap between gasoline vehicles and FCVs in the future, some manufacturers are developing vehicles that burn hydrogen instead of gasoline in ICEs, to reduce automotive energy consumption and CO2 emissions [29]. As the fuel contains no carbon, there are no CO, CO2, or hydrocarbons in the exhaust, and the toxic emissions are expected to be very low, with NOx levels <50 ppm [30]. The energy efficiency of a hydrogen ICE is said to be 20–25%, which is better than that of a gasoline ICE because it can run at a lean air-to-fuel ratio and a higher compression ratio. A hydrogen ICE can also be controlled without a throttle, and is, therefore, claimed to be a lower-cost alternative to a fuel cell. Another benefit is that hydrogen ICE automobiles can be started in weather that is too cold even for gasoline engines, and can use hydrogen that contains impurities, without damaging the engine. Today, BMW and the Ford Motor Company are the two leading developers of hydrogenfuelled ICE vehicles and, in fact, are simultaneously developing hydrogen ICE and fuel cell engine vehicle technologies [29]. Other alternatives aimed at bridging the gap between gasoline vehicles and FCVs are hybrid vehicles, which include ICE–battery, ICE–fuel cell, and fuel cell–battery hybrids. Unfortunately, hydrogen-fuelled ICE vehicles and hybrid vehicles are merely transitional technologies, and to make FCVs more competitive with conventional ICE vehicles, R&D activities must be focused not only on the fuel cell technology itself but also on the entire energy chain, including the hydrogen infrastructure. However, to accomplish this difficult task, FCV market penetration will be needed on an international scale, which in turn will require significant investments from both industry and government [31].

14.6 Summary

Unlike an ICE, which converts the chemical energy of fuels to mechanical power, fuel cells convert the chemical energy of fuels directly into electric power, with the

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potential for wide applications in transportation, stationary power, back-up power, portable electronics, and military and space programs. Fuel cells have much higher energy conversion efficiency than ICEs; they also have the advantages of low/zero emissions, high power density, silent operation, and quick refueling. The five major fuel cell types are the PEMFC, AFC, PAFC, MCFC, and SOFC, classified according to the electrolyte used. Except for the PAFC, which is already considered to be a commercial fuel cell product, the other types are all still in the development stage. The major technical challenges for fuel cells are their cost and durability, although the lack of a fueling infrastructure currently represents a huge obstacle blocking their commercialization. Nevertheless, there is no doubt that FCVs represent the ultimate solution to energy needs in the post-fossil fuel era. The problem here is that the urgency to protect the world against global warming and environmental pollution has left no spare time to accelerate the commercialization of fuel cell technology.

References 1 Yuan, X.-Z. and Wang, H. (2008) PEM fuel

2

3

4

5

6

7

8

cell fundamentals, PEM Fuel Cell Electrocatalysts and Catalyst Layers: Fundamentals and Applications (ed. J. Zhang), Springer. Barbir, F. (2005) PEM Fuel Cells: Theory and Practice, Elsevier Academic Press, New York. Thampan, T., Malhotra, S., Zhang, J., and Datta, R. (2001) PEM fuel cell as a membrane reactor. Catal. Today, 67 (1–3), 15–32. Lakshmanan, B. (2007) Electrochemistry in PEM fuel cells. Presented at the Fourth International Fuel Cells Testing Workshop (NRC/IFCI), Vancouver, Canada. Larminie, J. and Dicks, A. (2003) Fuel Cell Systems Explained, John Wiley & Sons Ltd, Chichester. Wagner, N., Schnurnberger, W., M€ uller, B., and Lang, M. (1998) Electrochemical impedance spectra of solid-oxide fuel cells and polymer membrane fuel cells. Electrochim. Acta, 43 (24), 3785–3793. Perry, M.L. and Fuller, T.F. (2002) A Historical perspective of fuel cell technology in the 20th century. J. Electrochem. Soc., 149 (7), S59–S67. Colliera, A., Wang, H., Yuan, X.-Z., Zhang, J., and Wilkinson, D.P. (2006) Degradation of polymer electrolyte

9

10

11

12

13

14

15

membranes. Int. J. Hydrogen Energy, 31 (13), 1838–1854. Andreadis, G., Song, S., and Tsiakaras, P. (2006) Direct ethanol fuel cell anode simulation model. J. Power Sources, 157 (2), 657–665. Qian, W., Wilkinson, D.P., Shen, J., Wang, H., and Zhang, J. (2006) Architecture for portable direct liquid fuel cells. J. Power Sources, 154 (1), 202–213. Rhee, Y.-W., Ha, S.Y., and Masel, R.I. (2003) Crossover of formic acid though NafionÒ membrane. J. Power Sources, 117 (1–2), 35–38. Ha, S., Adams, B., and Masel, R.I. (2004) A miniature air-breathing direct formic acid fuel cell. J. Power Sources, 128 (2), 119–124. McLean, G.F., Niet, T., Prince-Richard, S., and Djilali, N. (2002) An assessment of alkaline fuel cell technology. Int. J. Hydrogen Energy, 27 (5), 507–526. Yu, S., Xiao, L., and Benicewicz, B.C. (2008) Durability studies of PBI-based high temperature PEMFCs. Fuel Cells, 8 (3–4), 165–174. EG & G Technical Services, Inc . (2004) Fuel Cell Handbook, Under contract No. DE-AM26-99FT40575, 7th edn, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, West Virginia.

References 16 Sun, C. and Stimming, U. (2007) Recent

17

18

19

20

21

22

23

anode advances in solid oxide fuel cells. J. Power Sources, 171 (2), 247–260. Boudghene Stambouli, A. and Traversa, E. (2002) Solid oxide fuel cells (SOFCs): a review of an environmentally clean and efficient source of energy. Renewable Sustainable Energy Rev., 6 (5), 433–455. Acres, G.J.K. (2001) Recent advances in fuel cell technology and its applications. J. Power Sources, 100 (1-2), 60–66. Huang, Q.-A., Oberste-Berghaus, J., Yang, D., Yick, S., Wang, Z., Wang, B., and Hui, R. (2008) Polarization analysis for metalsupported SOFCs from different fabrication processes. J. Power Sources, 177 (2), 339–347. (2009) http://www.ballard.com/ About_Ballard/News_Events_ Press_Room/Image_Gallery/. Alcaide, F., Cabot, P.-L., and Brillas, E. (2006) Fuel cells for chemicals and energy cogeneration. J. Power Sources, 153 (1), 47–60. Yuan, X.-Z., Ma, Z.-F., Jiang, Q.-Z., and Wu, W.-S. (2001) Cogeneration of cyclohexylamine and electrical power using PEM fuel cell reactor. Electrochem. Commun., 11 (3), 599–602. Yuan, X.-Z., He, Q.-G., Ma, Z.-F., Hagen, J., Drillet, J.F., and Schmidt, V.M. (2003) Electro-generative hydrogenation of allyl alcohol applying PEM fuel cell reactor. Electrochem. Commun., 5 (2), 189–193.

24 Yuan, X.-Z., Ma, Z.-F., Bueb, H., Drillet,

25

26

27

28

29 30

31

J.F., Hagen, J., and Schmidt, V.M. (2005) Cogeneration of electricity and organic chemicals using a polymer electrolyte fuel cell. Electrochim. Acta, 50 (25–26), 5172–5180. Sundmacher, K., Rihko-Struckmann, L.K., and Galvita, V. (2005) Solid electrolyte membrane reactors: status and trends. Catal. Today, 104 (2–4), 185–199. Tsuchiya, H. and Kobayashi, O. (2004) Mass production cost of PEM Fuel cell by learning curve. Int. J. Hydrogen Energy, 29 (10), 985–990. Healy, J., Hayden, C., Xie, T., Olson, K., Waldo, R., and Brundage, M. et al. (2005) Aspects of the chemical degradation of PFSA ionomers used in PEM fuel cells. Fuel Cells, 5 (2), 302–308. Knights, S.D., Colbow, K.M., St-Pierre, J., and Wilkinson, D.P. (2004) Aging mechanisms and lifetime of PEFC and DMFC. J. Power Sources, 127, 127–134. (2009) http://machinedesign.com/ article/a-closer-look-at-hydrogen-0206. (2009) http://www.isecorp.com/ ise_products_services/ hydrogen_ice_drive_systems/. Thomas, C.E., James, B.D., and Lomax, F.D. Jr (1998) Market penetration scenarios for fuel cell vehicles. Int. J. Hydrogen Energy, 23 (10), 949–966.

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15 Toxicology of Combustion Products Tarun Gupta and Avinash Kumar Agarwal

15.1 Introduction

The emissions from various combustion sources significantly increase ambient particle number concentrations, as well as particulate mass. Combustion products normally incorporate a complex mixture of hundreds of constituents present in both gaseous and aerosol forms. Among the various combustion sources diesel engine emissions stand out, due mostly to the exponential increase in urban fleet numbers, and also to the finer particles that are emitted. The diesel particulate matter consists of elemental carbon, adsorbed organic compounds, sulfates, nitrates, and trace metals. Diesel particulate matter has a very large surface area, which makes it an excellent carrier for adsorbed inorganic and organic compounds. PM2.5 – that is, particles having an aerodynamic diameter <2.5 mm (also known as fine particles) – are primarily attributed to combustion sources such as power plants and vehicles. In addition, the photochemical transformation of pollutant gases also significantly contributes towards the net PM2.5 emissions. Combustion-generated PM2.5 can travel long distances, and may easily penetrate the interiors of buildings, which in turn results in an ubiquitous exposure of the population to such fine particulate matter. Moreover, as fine particles are sufficiently small to reach the deeper regions of the lungs, exposure to fine particles will occur almost continuously for many human beings throughout their lifetimes. Consequently, the public health significance of fine particulate air pollution is considered to be substantial. The physical and chemical characteristic of combustion products depend on the combustion source, the combustion efficiency, and the type of fuels used. Toxic combustion products mainly include carbon monoxide (CO), oxides of nitrogen (NOx), and polycyclic aromatic hydrocarbons (PAHs), polychlorinated dibenzo-pdioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), particulate matter (PM), and metals [1]. Various combustion sources generate fine particles that often are coated with toxic metals and organics emanating along with combustion gases [2].

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Significant amounts of fine particles, as well as inorganic and organic gases, are generated via incomplete combustion processes. Among the developing nations, close to 90% of rural dwellings routinely use unprocessed biomass fuels (crop and animal waste, wood, etc.) for cooking, heating, and lighting. Hence, it has been estimated that people living in one of the most polluted cities of the developed world on average lose between one and three years of their total lifespan due to pollutionrelated illness [3]. Toxic combustion products are a result of complex reactions, and can be classified as either primary or secondary pollutants. Normally, there is an interest in knowing the types of pollutant, their sources, their interactions with other species upon emission, their physical and chemical characteristics and their dispersion, their transformation into secondary pollutants, and their ultimate fate in the environment. The primary combustion pollutants are able to undergo complex photochemical and other redox reactions when they interact with other atmospheric species, and this results in the formation of more complex and toxic secondary species. These latter fine particles can easily penetrate to the deeper regions of the human lungs because of their relatively smaller size and enhanced mobility. In addition, fine particles may play a significant role in climate modification, due to their light-scattering or -absorbing nature. Major combustion sources include residential fuel used for heating and cooking, open biomass burning, industrial manufacturing plants, fossil fuel-based power plants, and combustion engines (cars, trucks, airplanes, locomotives, agricultural machinery, ships, decentralized power backup units, construction equipments, lawnmowers, etc.). The pollutants emitted from various combustion sources vary due to differences in the process efficiency and the type of fuel used [4]. In the past, extensive investigations have been carried out to measure and identify the primary emissions from a range of stationary and mobile combustion sources [2, 5, 6]. As a result, new techniques have emerged to determine the amount of toxins present on the surfaces of fine particles which, in turn, act as carriers of the toxic chemical species and facilitate their transport and suspension in the air for long periods of time. The bioavailability of these toxins also depends on the physical (surface adsorption/bulk absorption) and chemical (radical/ionic species) nature of their interaction with fine particles. For each mile traveled, diesel engines emit 10-fold more particles than conventional gasoline engines, and 30- to 70-fold more particles than engines equipped with catalytic converters [7]. Upon its emission from the vehicle tailpipe, diesel exhaust is first diluted (see Vol. 3 Ch. 15), and subsequently undergoes further physical and chemical transformations in the atmosphere. In general, the bulk of the dieselgenerated PM is less than 1 mm in size, with emitted ultrafine particles including both nitrate and sulfate substances, while other particles are composed largely of organic compounds. The typical atmospheric residence times for some of these constituents may range from a few hours to several days. The sampling method employed, as well as the overall dilution, will determine the size-specific chemical composition of the ultrafine particles [8, 9].

15.1 Introduction

Although the ultrafine particles emitted from diesel engines are very high in number (invariably more than millions of particles per cm3), they contribute little to the total particulate mass. The highest deposition efficiency in the alveolar region of the lungs is shown by particles with a diameter of
20 nm. Ultrafine particles are able not only to penetrate the epithelium, but also to enter the bloodstream. In fact, shortly after inhalational exposure such particles have been detected in the interstitium of the lung, as well as in the bloodstream [10]. Exposure to diesel exhaust may cause acute and chronic noncancer respiratory effects, and also has the potential to cause lung cancer in humans. As diagnostic tests typically are unable to prove causality, epidemiologic studies have been utilized for decoding the relationship between natural variability in exposure and variability in rates of illness [3]. Several studies have indicated that chronic exposure to fine particles will shorten the lifespan, and that no level of exposure is considered “safe.” The development of disease in humans is far more complex, however, as environmental exposures affect those that who more susceptible on the basis of their age, their proximity to certain sources, meteorology, and by unique circumstances such as comorbid disease, nutritional status, and genetic status. A few epidemiologic studies have reported daily fluctuations in the concentrations of fine particles that correlated weakly with daily fluctuations in the mortality rate [11–14]. Today, research studies are aimed at evaluating specific cellular and molecular mechanisms via both animal and in vitro studies, and even using compromised animal models. According to the current evidence, diesel exhaust exposure can cause acute eye and bronchial irritation, nausea, lightheadedness, phlegm, and cough. There is also some evidence for possible immunologic effects, and the exacerbation of allergenic responses to known allergens. The most toxic organic compounds, which are in fact adsorbed onto the particle surfaces, include PAHs, nitro-PAHs, and their derivatives. In terms of their mass, these toxic adsorbed species account for only 1% of the diesel particulate matter, although many of these species are known to have both mutagenic and carcinogenic properties. Recent toxicological investigations have been focused on determining the fate of fine and ultrafine particles, not only during inhalation but also upon their deposition in the lungs. In addition, attempts are currently being made to explore translocation rates to other extrapulmonary tissues, and to further influence particle chemical composition on such translocation. This chapter presents a review of the potential health effects from exposure to exhaust from diesel emissions and other combustion sources. This includes both a dose–response assessment and a potential hazard assessment associated with human exposure to diesel exhaust, in addition to other combustion-generated pollutants. The chapter also reviews the typical particle size distribution and chemical composition of diesel exhaust particles, including methods for dilution tunnel sampling, particle size analysis, and chemical analysis, with special emphasis placed on toxic metals and PAHs. In addition, a number of important issues related to the toxicity of combustion-generated pollutants are discussed, including their mode of action, dose–response relationships, the interaction of chemical species, and human exposure and risk assessment.

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15.2 Diesel Engine Emissions 15.2.1 Chemical Composition

Diesel exhaust is a complex mixture of hundreds of constituents present in both gaseous and aerosol forms. In the gaseous form, the major constituents of diesel exhaust are CO2, CO, oxygen, nitrogen, water vapor, nitrogen compounds, sulfur compounds, and low-molecular-weight hydrocarbons. Among these materials, the most toxic are formaldehyde, acetaldehyde, acrolein, benzene, 1,3-butadiene, PAHs, and nitro-PAHs. The diesel PM consists of elemental carbon, adsorbed organic compounds and varying amounts of sulfates, nitrates, and trace metals. Diesel PM is respirable and has a very large surface area, which makes it an excellent carrier for adsorbed inorganic and organic compounds. Many of these toxic adsorbed species are known to have mutagenic and carcinogenic properties. Diesel exhaust consists of various organic compounds, including straight- and branched-chain aliphatic and aromatic compounds. In contrast, SO42, CO, metals and NOx represent various inorganic components that originate mainly from the unburned fuel, the lubricating oil, and engine wear. A significant fraction of these pollutants is also attributed to the reaction of engine exhaust gases with the catalysts used to reduce pollutants [15, 16]. In the diesel exhaust, most semi-volatile hydrocarbons are found either absorbed onto the particulate phase, or as liquid droplets along with sulfates [17, 18]. Approximately 90% of the mass for diesel particles exist as two distinct submicron modes: a nuclei-mode (7.5–56 nm); and an accumulation mode (56–1000 nm) [17]. It has been suggested that the accumulation-mode particles are actually chain agglomerates of the primary carbon spheres with adsorbed hydrocarbons and SO42 [19]. In addition, the nuclei mode is enriched with elemental carbon (EC) and condensed organic carbon (OC) fractions. The results of several studies have indicated that larger amounts of sulfur in the fuel will result in higher nuclei-mode particle concentrations [19, 20]. Recently, thermogravimetric analyses have been used with various engines running under different test loads, in order to carefully examine the contribution of unburned oil and fuel to the formation of a soluble organic fraction (SOF) [21]. According to one study, one-fourth of the unburned fuel and three-fourths of the unburned engine oil contributes towards the formation of SOF [8], while a very small portion of the total SOF mass is represented by pyrolysis (see Vol. 2 Ch. 8) and incomplete combustion (Figure 15.1).

Figure 15.1 Schematic diagram of particulate formation during a typical combustion process.

15.2 Diesel Engine Emissions

Nanoparticles present in diesel exhaust have a high number density, a low mass concentration, a large surface area, and a complex variable chemical composition. These nanoparticles have a huge surface area, and may easily become coated with contaminants that include toxic metals (lead, cadmium, arsenic, chromium, zinc), sulfur, and PAHs [22]. When, recently, the chemical composition of nanoparticles present in diesel exhaust was analyzed using mass spectrometry (see Vol. 2 Ch. 20), one study provided strong evidence that the organic component of nanoparticles was primarily unburned oil [23]. In fact, rough estimates suggest that approximately 95% unburned lubricating oil contributes to the volatile component of diesel particles. It was found previously, by using gas chromatography-mass spectrometry (GC-MS), that about 1% of the elutable organic components were monocarboxylic acids, and that about 90% of the elutable mass was unresolved, unbranched, and cyclic alkanes [24]. The residual particles may be attributed to refractory materials such as metal oxides and soot (Vol. 2 Ch. 8). Diesel engine emissions are one of several sources of PAHs and nitro-PAH derivatives found in the ambient atmosphere (see Vol. 2 Ch. 16). Both, PAHs and PAH-derivatives present in the atmosphere are distributed between the gas and particle phases, due mainly to their liquid-phase vapor pressure [25]. Although they are most likely to be formed by incomplete combustion of hydrocarbons at high temperatures [26], they are also formed from the reaction of parent hydrocarbons with nitrogen oxides in ambient air. Possible sources of PAHs in diesel exhaust are unburned PAHs from the fuel, the electrophilic nitration of PAHs during combustion, crankcase oils, and engine or system deposits. A wide spectrum of gas- and particle- phase PAHs and PAH-derivatives are emitted in diesel exhaust [27–29], with methylated PAHs appearing to be the most abundant PAH-derivatives. In one study, over 100 oxy-PAH were identified in the moderately polar fractions of a diesel exhaust extract [30], which also contained hydroxyl, ketone, quinine, acid anhydride, nitroand aldehyde-PAH derivatives. In one of the earliest studies, concentrations of select PAH and 1-nitropyrene were measured in the cylinder and exhaust manifold of an operating diesel engine [26]. In this case, the PAH concentrations were observed to be higher in the cylinder than in the exhaust manifold, whereas the reverse situation was true for 1-nitropyrene concentrations. This suggests that most of the nitro-PAH in the exhaust are probably formed during the expansion/exhaust process rather than during combustion. It was also reported that PAH emissions levels from heavy-duty diesel engine vehicles were essentially higher than those from light-duty gasoline engine cars [31]. Furthermore, it was reported in one study that both the diesel fuel structures and the operational conditions of the engines would affect the chemical composition and potential biological activity of diesel emissions [32]. It further suggested that, by reducing the fuel PAH contents in commercially available diesel fuel, the emission of PAHs to the environment would be reduced. In the presence of heat and sunlight, hydrocarbons emitted from diesel engines and nitrogen oxides react in the atmosphere to form harmful ozone. Today, urban cities with high numbers of industrial facilities and automobiles routinely experience high ozone levels. In addition, rural areas may also be adversely affected as winds

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transport urban NOx emissions to locations hundreds of miles away from their original source [33]. 15.2.2 Sampling

Over the past few years, a variety of different methods have been developed for both the sampling and analysis of exhaust components; moreover, such methods demonstrate a fairly good sensitivity for the levels of diesel particulate samples collected under different engine testing conditions [17]. In general, for the measurement and analysis of diesel exhaust particles, either real-time or filter-based methods are employed (Vol. 2 Ch. 2). In the past, diesel particulates have been monitored by using electron microscopy, impactors and differential mobility analyzers [15, 34]. The measurements of gas/particulate phase distribution (semi-volatile fractions) are often accomplished by the use of a high-volume filter, followed by an adsorbent such as polyurethane foam (PUF) (TenaxÔ, or XAD). The sampling of diesel particulates usually takes place on preweighed filters, such as Teflon-coated glass fiber filters, using some form of high-volume cascade impactors (>1000 l min1). The dilution ratios typically vary from 8 : 1 to 15 : 1, depending on the engine operating conditions and the ambient temperature. It is a general practice to maintain the filter face temperature at 45  1  C [18]. After exposure to dilute diesel exhaust, the filters are ammoniated for 1 h, and then reequilibrated in a 50% relative humidity chamber. The aim of the ammoniation procedure was to convert the sulfuric acid present on the filter to less-hygroscopic ammonium sulfate so as to help stabilize the filter mass, after which the filters were reweighed to determine the mass of particles deposited. The SOF was then obtained by extracting each filter with dichloromethane (DCM) in a Soxhlet apparatus for 24 h. The solute concentration of the extract provided an estimate of the SOF mass. Ion chromatography (IC) was then applied to the aqueous extraction of filters to determine the sulfate fraction. During collection of the particulate organic matter, the adsorption of semi-volatile PAHs could also occur along with a chemical transformation of the semi-volatile compounds [35, 36]. Any vapor-phase organic compounds were collected onto XAD beads (Supelco Inc., Bellefonte, PA, USA), after which the beads were extracted with dichloromethane (DCM) for 24 h in a Soxhlet apparatus. The mass of organic fraction was then determined from the concentration of solute in the extract [17]. In the past, several studies have been carried out with a scanning mobility particle sizer (SMPS; TSI, Inc.) to study diesel exhaust particle size distributions, as well as their number concentrations [37, 38]. Concentration measurements of particles in the size range below 100 nm often requires that particles are made detectable. The detection technique used with the condensation particle counters (CPC) is based on exposing the nanoparticles to a supersaturated vapor, so that they form larger droplets which can easily be counted. CPCs with butanol as the working fluid have been available for many years, and are widely used. A novel technique allows water to be used as the working fluid, so that nanoparticles down to 2.5 nm can be detected and

15.2 Diesel Engine Emissions

Figure 15.2 Schematic of a typical experimental set-up to measure engine exhaust nanoparticles.

counted. In order to determine the size distribution of these nanoparticles, their electrical mobility – which is mainly a function of particle size and number of charges on the particle – can be measured using a differential mobility analyzer (DMA). Corrections have been made for particle charging, the DMA transfer function, particle loss by diffusion in the DMA, diffusional broadening of the transfer function, and the CPC counting efficiency by inverting the particle mobility size distribution [23, 37]. However, the use of a SMPS is rather limited for dynamic measurements in diesel engines because the sampling time required is on the order of minutes (60 s minimum). The characterization of emissions with a strong time dependence (such as the emissions from diesel engines) requires an ability to determine particle size distributions with real-time resolution. A high time resolution of the measurement is required in cases when the particulate emission is changing rapidly over time, and this has been made possible by using an engine exhaust particle sizer (EEPS; TSI Inc., USA) designed for the dynamic measurement of combustion-generated nanoparticles, that is capable of obtaining ten complete size distributions per second (Figure 15.2). This technique enables the visualization of transient processes in the exhaust gas that occur during an engine load change or diesel filter loading, and its subsequent regeneration. Several studies have shown the primary soot particles to be approximately 10–30 nm in size (Figure 15.3), and typically to be present as various clusters and chain agglomerates, with the primary carbon particles ranging from 7 to 120 nm in diameter [17, 39, 40]. The EEPS is a modern instrument that can quickly (within a few seconds) sample and measure the size distribution between 5.6 and 560 nm (in 32 size bins) for dilute exhaust or ambient aerosols, according to the particle’s electrical mobility diameter. This diameter is defined as the diameter of a spherical particle which, when charged, has the same electrical mobility as the particle being measured [41, 42]. The dilution ratio is determined by measuring the ratio of NOx in the undiluted exhaust to that in the diluted sample. Overall dilution ratios at EEPS for typical particle concentrations in the raw exhaust vary from 10 : 1 to 1000 : 1.

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Figure 15.3 Typical diesel and biodiesel particle size distributions as measured using the engine exhaust particle sizer (EEPS).

The tubing is heated to prevent thermophoretic deposition of solid particles and condensation of volatile materials on the wall. 15.2.3 Health Effects

The diesel engine exhaust and related toxicity has been evaluated in numerous acute and chronic studies. Laboratory animals (especially mice and dogs) are considered to serve as good models for humans with regards to their responses to diesel particulate matter (DPM) [43, 44]. Depending upon location (rural or urban), the annual mean exposure to diesel exhaust is estimated to be about 1–4 mg m3 of inhaled air [45]. Numerous studies have shown that diesel exhaust exposure can cause acute eye and bronchial irritation, nausea, lightheadedness, phlegm, and cough. Likewise, several chronic studies involving laboratory rats, mice, hamsters, guinea pigs, cats and monkeys, have been conducted to monitor the respiratory and systemic effects due to exposure to DPM [44, 46]. Acute exposures to high concentrations of fresh diesel exhaust can cause respiratory inflammation and symptoms, while the nasal deposition of large doses of diesel exhaust amplifies the immunological responses to antigens. Lifetime exposures of rodents to high concentrations of DPM cause chronic inflammation and fibrosis. The semi-volatile and sootborne organic material is

15.2 Diesel Engine Emissions

mutagenic; soot extract is carcinogenic to mouse skin, and the inhalational exposure of rats to high concentrations causes lung tumors. It has also been shown, via a series of studies using human subjects, that the nasal instillation of a diesel exhaust aerosol increases local immunoglobulin E (IgE) and cytokine responses to antigen (ragweed and KLH) [47–50]. This activity has also been shown to be due chiefly to the organic fraction of diesel exhaust [51]. Several recent laboratory studies with rats have indicated that the SOF portion is most likely essential for tumor formation [52–55], with the associated SOF – notably the PAHs and nitro-PAHs – having a significant contribution to the overall carcinogenic effect. Exposure to diesel exhaust in presence of other air pollutants may be even more harmful (for example, an increased susceptibility to certain allergies, increased toxicity of DPM in the presence of ozone and NOx). At present, no specific diesel exhaust information is available that provides a direct insight into the question of variable susceptibility within the general human population and vulnerable subgroups, including children and elderly and subjects with preexisting respiratory illness. However, further studies are required to improve the knowledge and database on exposure to diesel exhaust and potential human health effects, and thereby to reduce the uncertainties of future risk assessments. Recently, investigations have been conducted using animal models to determine the mechanisms associated with pulmonary cancer caused by diesel exhaust particles [56]. The results of one such study indicated that the deposition of metals (such as iron) on the lower airways would generate hydroxyl radicals that could trigger the production of oxygen free radicals, and finally cause both acute and chronic lung injuries [57]. The chemical form and oxidation state of metals also affect their toxicity, and today advanced techniques are desired for the speciation of toxic metals. Chromium, which is a widespread environmental contaminant and a known human carcinogen [58], exists in both trivalent [Cr(III)] and hexavalent [Cr(VI)] forms. Compounds of Cr(VI), have been found to be mutagenic and carcinogenic [59], whereas [Cr(III)] is relatively harmless. Chromate (CrO42) can readily penetrate the cell membrane [60]. Cr-induced mutagenesis is due primarily to the generation of reactive oxygen intermediates during the reduction of Cr(VI) by glutathione [61, 62]. Metal species present in DPM, such as As, V, and Zn, are known to deregulate phosphoprotein metabolism, and this results in a modulation of intracellular information exchange systems and transcription factors. A few recent findings have also indicated that these metals activate epidermal growth factor receptors (EGFRs), leading to increases in the levels of guanosine triphosphate-bound Ras in human lung cells [63]. Several studies have shown that lifetime exposures to very high concentrations of DPM have resulted in the development of lung tumors among the animals studied. An overload of DPM, with consequent persistent inflammation and cell proliferation, has been suggested as a plausible mechanism by which tumors develop, indicating a nonlinear mode of action for lung cancer in the rat [43, 46]. The response for some of these studies has been recorded at relatively high exposures of diesel exhaust (>3500 mg m3 of DPM).

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Diesel particulate matter undergoes numerous atmospheric reactions, including photolysis, nitration, and oxidation. The atmospheric chemistry of organic compounds associated with DPM is fairly complex, with one research group [64] having studied the effect of aging and solar radiation on diesel exhaust by simulating such artificial environments within a chamber. Recently, it has been reported that the treatment of DPM with ozone increased the inflammatory potential in rat lungs [65]. Evidence has also been proposed for possible immunologic effects and the exacerbation of an allergenic response to known allergens. Recent experimental studies have highlighted the role of DPM in enhancing inflammatory and allergic responses in the respiratory system [66–70]. It has been observed, in animal studies, that gaseous copollutants such as ozone, NOx and SO2 significantly enhance the pulmonary inflammatory potential of ultrafine carbonaceous particles. The role of surface chemistry has been emphasized in the overall toxicity due to ultrafine particles, with both aged and experimentally sensitized rats having shown a greater sensitivity to a higher dosage of ultrafine carbonaceous particles. The ultrafine particles in these cases were not effectively phagocytosed by the alveolar macrophages upon their deposition in the deeper regions of the lungs [10, 71]. The translocation of ultrafine particles to the liver, and even to neurons, has also been documented [72]. Studies on the health effects of diesel exhaust have, until recently, focused primarily on cancer outcomes [73]. However, during recent years attention has been focused increasingly on understanding the noncancer respiratory effects of diesel exhaust, and their possible role in the acute or chronic health effects of airborne PM. There is emerging experimental evidence of an irritants and/or immunologic effect of diesel exhaust on the respiratory system. In addition, recent epidemiological studies have demonstrated an association between residential proximity to traffic sources and adverse respiratory outcomes, including asthma hospitalization among children, increased respiratory symptoms, and diminished lung function [1, 74, 75]. Exposure assessments in these studies have included self-reported traffic volumes on residential streets (e.g., high, medium, or low), quantitative data on traffic volumes collected by local agencies and, occasionally, air monitoring data at selected locations. Because of the limitations of the exposure data, it is not always possible to uniquely implicate diesel exhaust as distinct from other forms of motor vehicle exhaust in the observed respiratory health effects. These findings show novel mechanisms of action by metals, and may provide insight relevant to the health effects of diesel particulate exposure.

15.3 Health Effects Associated with Other Combustion Sources

The overall evidence from numerous acute and chronic epidemiologic studies indicates a probable link between fine particulate air pollution and adverse effects on cardiopulmonary health. Furthermore, they suggest that all individuals who are chronically exposed may eventually be affected [3]. Epidemiologists have attempted to

15.3 Health Effects Associated with Other Combustion Sources

examine the relationship between hospital emergency room data and ambient air monitoring data [76], with several studies having been carried out to understand the underlying mechanisms by which these pollutants induce harmful health effects. Fine particulate matter has been extensively characterized (both chemically and physically), an in fact attempts have been made to find correlation of harmful health effects with the size, shape, mass, and number of particles. A similar exercise has been carried out with different chemical fractions of the pollutants such as sulfates, acidity, water-soluble metals, organic carbon (OC), and elemental carbon (EC). So far, several studies have proved to be successful in achieving a statistically significant, positive association between cardiac dysrrhythmia and CO, coarse PM, and fine PM (PM2.5 EC fraction), as well as between all cardiovascular diseases and CO, PM2.5 EC and OC fractions. Apart from diesel exhaust, the major sources of nanoparticles include tobacco smoke, welding fumes, smoke from cooking stoves running on kerosene or biomass, and fly-ash from coal-based power plants. The large surface area of nanoparticles maximizes the dissolution of soluble species in the lungs. For insoluble nanoparticles, the large surface area provides a surface on which catalytic chemistry can occur that favors formation of the free radicals that are responsible for driving oxidative stress, the underlying mechanism that promotes an inflammatory response. Large surface areas in the lungs have been shown to correlate well with the ability of a range of low-toxicity, low-solubility nanoparticles to induce inflammation in the rat lung. These particles may be soluble and release transition metals as their primary proinflammatory mechanism, or they may be insoluble and cause inflammation because of their surfaces. Exposure to nanoparticles of various types has been associated with a range of adverse health effects, including fibrosis, bronchitis, metal fume fever, airway diseases, and cancer. It has been seen with animal models that ultrafine particles generate greater inflammatory responses as compared to bigger particles of equal mass. In addition, the response decreases when ultrafine particles move to bigger particle sizes upon coagulation [77]. Ultrafine particles provide a heterogeneous surface for organic radicals that show varied biological activities such as DNA damage and an inducement of oxidative stress [78–80]. These radicals (like the semiquinone type), along with other organics and metals, may result in the generation of reactive oxygen species (ROS) and various other biologically active species. This may in turn provide plausible routes for the occurrence of cancer due to fine particle exposure [81]. Furthermore, evidence has been found that ultrafine particles may induce ischemia during submaximal exercise in patients with coronary artery disease. The results of a European multicenter study have indicated that hospital admissions for cardiovascular diseases in myocardial infarction survivors may be associated with ultrafine particle number concentrations. A longitudinal study lasting over two years was conducted to establish if the exposure of boiler-makers (working at gas-, oil-, and coal-fired plants) to PM was linked to a loss of lung function [74]. Boiler-makers are exposed almost daily to boiler fossil fuel ash, as well as to other copollutants emitted during welding activities. Boiler ash includes fine particulate matter, PAHs, and also metals such as vanadium,

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nickel, iron, zinc, chromium, and arsenic; the ash composition will also vary with the type of fuel (oil, coal, natural gas, or trash) used. A boiler-maker may typically work at a number of different plants that burn multiple fuel types, depending on maintenance and construction needs. Dioxins such as PCDDs and PCDFs are major environmental contaminants; indeed, dioxins have been characterized by the US Environmental Protection Agency (USEPA) as a likely human carcinogen [82]. At present, the uncontrolled combustion of residential waste and accidental fires are among the major sources of PCDD/PCDF emissions in the US [83]. Historically, municipal and commercial waste incineration, pesticide manufacture and use, and pulp and paper processing were the major sources of emissions; in these combustion processes, a series of complex mechanisms results in the formation of dioxins. PCDD/PCDF formation is a catalytic reaction of carbon with oxygen and chlorine which depends mainly on the duration of combustion, the type of catalyst, and the temperature [84]. The chlorination of dibenzofurans occurs after extended residence times and lower flue-gas temperatures; these studies have also shown that when bromine is added to the fuel, PBDFs are formed instead [85]. In one study carried out for flame retardants, the majority of tetra-halogenated dioxins/dibenzofurans which formed during combustion due to elevated levels of bromine in the waste streams were dibromo-dichlorocompounds. In one study, healthy human volunteers were exposed for a short period to concentrated ambient particles [86], whereby the aerosols were extensively characterized and detailed measurements of several health parameters carried out. A subsequent principal component analysis linked the specific water-soluble components of the PM that had collected on the filters to both the observed neutrophil influx and elevated blood fibrinogen levels. A sulfate/iron/selenium factor, which may be attributed to photochemical air pollution, was associated with the neutrophil increase in the lavage, while a copper/zinc/vanadium factor, which was likely related to various combustion processes, was linked to the increased blood fibrinogen [87]. Few other studies have supported any effect of the short-term exposure to PM and ozone on vasoconstriction of the systemic arteries, although biologically plausible mechanisms for such effects remain unclear. In more recent studies, adverse health effects were identified even among healthy individuals exposed to high levels of ambient ultrafine particles under controlled exposure [88, 89]. Although most individuals are exposed to combustion products when either indoors (home, occupational settings) or outdoors (traffic, field work), relatively few people respond to elevated levels of air pollutants. The answer to this anomaly might lie in the differences between individuals on the basis of their previous health conditions, age, and genetic variation. Epidemiologic studies have presented an insight into aspects of individual susceptibility that are not directly attributed to genetic variability. These studies consider various factors such as age, gender, occupation, nutritional status, lifestyle, and coexisting health conditions that may determine and explain the underlying variability in individual responses, even for identical exposures.

15.4 Outlook

15.4 Outlook

Mobile source emissions are of special concern, both because of their ubiquitous nature and because emissions occur at ground level in urban streets where human activity is greatest. Emissions containing PM2.5 particles are potentially more harmful than larger particles, because they can penetrate more deeply into the lower respiratory tract of the lungs. In addition, because they largely originate from fuel combustion, the PM2.5 often contain high concentrations of toxic substances, including acid sulfates, soluble metals, and organic compounds such as PAHs. As part of the Harvard six-city study, an attempt was made to demonstrate the relationship between human respiratory health and survival and the concentrations of PM and sulfates in ambient air within the US. The study, which involved more than 8000 people selected at random from six eastern cities within the US, spanned over fifteen years of observations. One of the key findings was that death rates among the study populations correlated with the concentrations of fine particulate air pollution in the communities in which they lived [90, 91]. Fine particles were found to have the most significant correlation with mortality. In 1990, the American Cancer Society carried out a similar study which included more than 500 000 subjects residing in 154 cities. Again, a correlation was found between elevated PM10 levels and deaths, even in cities which complied with the 1987 air quality standards as established by the USEPA. On the basis of these study results, it was estimated that about 60 000 deaths would occur each year in the US; as a result, the American Lung Association sued the EPA, seeking a review of the air quality standards for PM to be set at a level so as to protect public health. These observations were supported by the results of several other subsequent studies, and in 1997 the EPA announced new regulations to limit the concentrations of PM2.5. Through several epidemiological studies, it has been estimated that each 10 mg m3 of excess fine particulate air pollution corresponds to an 8% increase in lung cancer mortality, and a 6% increase in cardiopulmonary mortality [3]. Hence, both lung cancer and cardiac-related diseases are the major causes of deaths related to air pollution. Although, initially, the USEPA mandated to monitor and control only six criteria pollutants, over a period of time many other toxic air pollutants (including benzene and formaldehyde) have been incorporated into routine monitoring in various parts of the world [92]. It has been estimated that a reduction in emissions from older coalfired power plants in the US alone could not only prevent 18 700 deaths each year, but also save three million lost working days and 16 million restricted-activity days [93]. It has also been predicted that a reduction in greenhouse gas emissions in four major cities (Sao Paulo, Brazil; Mexico City, Mexico; Santiago, Chile; and New York City, USA) could save around 64 000 lives over the next twenty years [93–95]. To date, very few studies have been conducted on the combustion products resulting from the indoor use of solid fuel for cooking and heating, and the resultant human health effects. Currently, simple stoves burning biomass are used by about half of the world’s population [96], and poor ventilation coupled with inefficient combustion

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results in huge amounts of PM emission and the gaseous exposure of women and young children. Furthermore, these pollutants incorporate several volatile organic compounds, including formaldehyde and PAHs [97]. In the US alone, the EPA has estimated that by the year 2030, it will be possible to prevent 9600 premature deaths attributed to diesel particulate, sulfate, and nitrate on an annual basis. This may indeed be possible if the proposed rule for reducing the sulfur content of mineral diesel is complied with [98]. If, however, alternate diesel fuel formulations can provide better or equivalent performance than a reference (10% by volume) aromatic fuel in a specified engine test, then it would be considered a qualified suggestion [99]. In a recent study, the effects of emissions-reducing technologies, coupled with the use of low-sulfur fuel and a catalyzed trap have been extensively studied [100]. Diesel exhaust is a mixture of gases, vapors, semi-volatile organic compounds, and particles. In order to reduce these emissions, engine modifications to control the soot particles and use of low-level sulfur have been considered as two prime areas as part of a worldwide effort to comply with new and stringent standards set for combustion-generated pollutants (Figure 15.4). Particulate traps are effective for the reduction of total particulate matter (TPM), including SOF, whereas oxidation or reduction catalysts are better at reducing all forms of hydrocarbon emissions, including PAHs and nitro-PAHs (see Vol. 2 Ch. 11). Recently, low-sulfur fuels (<0.01% dry mass) have been shown to reduce the number of ultrafine particles for the engines tested [17].

Figure 15.4 Emissions standards for new vehicles (light duty).

References

15.5 Summary

The health effects of nanoparticles emitted into the atmosphere by engines and other combustion sources represents an increasing public health concern worldwide. While the toxicological mechanism of how particles affect human health is not yet known, recent studies have provided growing evidence for an inverse dependency of health effects on particle size, and that the number concentration and surface area might represent more important characteristics with regards to health effects than the mass concentration. Varied responses can be observed even when different species, and different groups and individuals, are subjected to identical exposures. The interactive effects of several pollutants can be mutually additive, synergistic, or antagonistic. The indirect effects of pollutants may be more important than their direct effects; indeed, a pollutant that is nonfatal to organisms but retards their development might have a far more significant effect in the long term. The vast majority of diesel particles is emitted in the submicron particle size mode, and is therefore important in terms of potential health impacts due to an ability of the particles to be inhaled and become trapped in the bronchial passages and alveoli of the lungs. There is considerable evidence demonstrating an association between diesel exhaust exposure and increased lung cancer risk among workers in different occupations. In addition to findings in humans, there is extensive evidence for the induction of lung cancer in rats after chronic inhalational exposure to high concentrations of diesel exhaust, as well as supporting evidence of tumor formation in mice and rats following noninhalational routes of exposure. The mutagenic and chromosomal effects of DPM have also been demonstrated. Nonetheless, the impact of exposure to ultrafine particles in urban air from combustion sources such as automobiles, natural gas combustion, and electric engines remains relatively unknown. Consequently, in order to increase the existing knowledge with regards to the composition, sources, and health impacts of fine and ultrafine particles emitted from different combustion sources, it is essential that further investigations are conducted. The use of elaborate atmospheric studies, and the formation and fate of combustion-generated ultrafine particles, including their spatial and temporal distributions, require urgent and extensive study.

References 1 Warnatz, J., Maas, U., and Dibble, R.W.

(1999) Combustion: Physical and Chemical Fundamentals, Modeling and Simulation, Experiments, Pollutant Formation, Springer, Berlin. 2 Kleeman, M.J., Schauer, J.J., and Cass, G.R. (1999) Size and composition distribution of fine particulate matter

emitted from wood burning, meat charbroiling, and cigarettes. Environ. Sci. Technol., 33, 3516–3523. 3 Pope, C.A. (2000) Epidemiology of fine particulate air pollution and human health: biologic mechanisms and who’s at risk? Environ. Health Perspect., 108 (suppl. 4), 713–723.

j371

j 15 Toxicology of Combustion Products

372

4 Smith, K.R., Samet, J.M., Romieul, U.,

5

6

7

8

9

10

11

12

13

14

15

16

and Bruce, N. (2000) Indoor air pollution in developing countries and acute lower respiratory infections in children. Thorax, 55, 518–532. Cass, G.R. and Simoneit, B.R.T. (1999) Characterization of fine particle emissions from burning church candles. Environ. Sci. Technol., 33, 2352–2362. Kleeman, M.J., Schauer, J.J., and Cass, G.R. (2000) Size and composition distribution of fine particulate matter emitted from motor vehicles. Environ. Sci. Technol., 34, 1132–1142. Godlee, F. (1993) Air pollution II: road traffic and modern industry. Br. Med. J., 303, 1539–1543. Kittleson, D.B. (1998) Engine and nanoparticles: a review. J. Aerosol Sci., 29, 575–588. Wei, Q., Kittelson, D.B., and Watts, W.F. (2001) Single stage dilution tunnel performance. SAE Paper No. 2001-01201. Oberdorster, G. (2000) Pulmonary effects of inhaled ultrafine particles. Int. Arch. Occup. Environ. Health, 74, 1–8. Schwartz, J. (1994) What are people dying of on high air pollution days? Environ. Res., 64, 26–35. Schwartz, J. and Morris, R. (1995) Air pollution and hospital admissions for cardiovascular disease in Detroit. Michigan. Am. J. Epidemiol., 142, 23–35. Samet, J.M., Zeger, S.L., Dominici, F., Curriero, F., Coursac, I., and Dockery, D.W. (2000) The National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and mortality from air pollution in the United States. Health Effects Institute Report No. 94 (pt 2), pp. 5–70. Dominici, F., McDermott, A., Zeger, S.L., and Samet, J.M. (2003) Airborne particulate matter and mortality: timescale effects in four US cities. Am. J. Epidemiol., 157 (12), 1055–1065. Vuk, C.T., Jones, M.A., and Johnson, J.H. (1976) The measurement and analysis of the physical characteristics of diesel particulate emissions. SAE Paper No. 60131. Frisch, L.E., Johnson, J.H., and Leddy, D.G. (1979) Effect of fuels and dilution

17

18

19

20

21

22

23

24

25

ratio on diesel particulate emissions, in The Measurement and Control of Diesel Particulate Emissions (eds J.H. Johnson, T.L. Rosebrock, and J.H. Somers), Society of Automotive Engineers, pp. 245–273. Johnson, J.H., Bagley, S.T., Gratz, L.D., and Leddy, D.G. (1992) A review of diesel particulate control technology and emissions effects. SAE Paper No. 940233. Bagley, S.T., Baumgard, K.J., Gratz, L.G., Johnson, J.H., and Leddy, D.G. (1996) Characterization of fuel and after treatment device effects on diesel emissions. Research report # 76, Health Effects Institute (HEI), Cambridge, MA. Baumgard, K.J. and Johnson, J.H. (1992) The effect of low sulfur fuel and a ceramic particle filter on diesel exhaust particle size distributions. SAE Transactions, 101, 691–699. Opris, C.N., Gratz, L.D., Bagley, S.T., Baumgard, K.J., Leddy, D.G., and Johnson, J.H. (1993) The effects of fuel sulfur concentration on regulated and unregulated heavy-duty diesel emissions. SAE Paper No. 930733. Abbass, M.K., Andrews, G.E., Williams, P.T., and Bartle, K.D. (1991) The influence of diesel fuel composition on particulate PAH emissions. SAE Paper No. 892079. Cass, G.R., Hughes, L.A., Bhave, P., Kleeman, M.J., Allen, J.O., and Salmon, L.G. (2000) The chemical composition of atmospheric ultrafine particles. Philos. Trans. R. Soc. Lond. A Math. Phys. Sci., 358, 2581–2592. Tobias, H.J., Beving, D.E., Ziemann, P.J., Sakurai, H., Zuk, M., Mc Murry, P.H., Zarling, D., Waytulonis, R., and Kittelson, D.B. (2001) Chemical analysis of diesel engine nanoparticles using a nano-DMA/thermal desorption particle beam mass spectrometer. Environ. Sci. Technol., 35, 2233–2243. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., and Cass, G.R. (1993) Sources of fine organic aerosol II: Noncatalyst and catalyst-equipped automobiles and heavy-duty diesel trucks. Environ. Sci. Technol., 27, 636–651. Bildeman, T.F. (1988) Atmospheric Process. Environ. Sci. Technol., 22, 361–367.

References 26 Kittelson, D.B. (1985) Measurements of

27

28

29

30

31

32

33

34

35

36

PAH in the cylinders of an operating diesel engine. EPA Report No. 600/D-85/ 012. National Research Council (1999) Automotive fuel economy, National Research Council Committee on Fuel Economy of Automobiles and Light Trucks, Energy Engineering Board, National Academy Press, Washington, DC. Jensen, T.E. and Hites, R.A. (1983) Aromatic diesel emissions as a function of engine condition. Anal. Chem., 55, 594–599. Barbella, G.T. (1988) Soot and unburnt liquid hydrocarbon emissions from diesel engines. Combust. Sci. Technol., 59, 183–198. Schuetzle, D. and Frazier, J.A. (1986) Factors influencing the emission of vapor and particulate phase components from diesel engines, in Carcinogenic and Mutagenic Effects of Diesel Engine Exhaust (eds N. Ishinishi, A. Koizumi, R.O. McClellan, and W. St€ober), Elsevier Science Publishers, pp. 41–63. Honda, T. (1984) In-situ emission levels of carcinogenic and mutagenic compounds from diesel and gasoline engine vehicles on an express way. Environ. Sci. Technol., 18, 895–902. Westerholm, R. and Li, H. (1994) A multivariate statistical analysis of fuelrelated polycyclic aromatic hydrocarbon emissions from heavy-duty diesel vehicles. Environ. Sci. Technol., 28, 965. U.S. Environmental Protection Agency (1997) Ozone: Good Up High, Bad Nearby. EPA Report No. 451-K-97-002. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Dolan, D.F., Kittelson, D.B., and Pui, D. (1980) Diesel exhaust particle size distribution measurement technique. SAE Paper No. 800187. Yamasaki, H., Kuwata, K., and Miyamoto, H. (1982) Effects of ambient temperature on aspects of airborne polycyclic aromatic hydrocarbons. Environ. Sci. Technol., 16, 189–194. Schauer, J.J., Kleeman, M.J., Cass, G.R., and Simoneit, B.R.T. (1999)

37

38

39

40

41

42

43

44

45 46

47

Measurement of emissions from air pollution sources: C1-C29 organic compounds from medium duty diesel trucks. Environ. Sci. Technol., 33, 1578–1587. Wang, S.C. and Flagan, R.C. (1990) Scanning electrical mobility spectrometer. J. Aerosol Sci., 13, 230–240. Agarwal, J.K. and Sem, G.J. (1991) Continuous flow, single particle counting condensation nucleus counter. J. Aerosol Sci., 11, 343. Sakurai, H., Tobias, H.J., Park, K., Zarling, D., Docherty, K.S., Kittelson, D.B., McMurry, P.H., and Ziemann, P. (2003) On-line measurements of diesel nanoparticle composition and volatility. Atmos. Environ., 37, 1199–1210. Lipsky, E.M. and Robinson, A.L. (2005) Design and evaluation of a portable dilution sampling system for measuring fine particle emissions from combustion systems. Aerosol Sci. Technol., 39, 542–553. Zervas, E. and Dorlhene, P. (2006) Comparison of exhaust particle number measured by EEPS, CPC, and ELPI. Aerosol Sci. Technol., 40, 977–984. Wang, J., Storey, J., Domingo, N., Huff, S., Thomas, J., and West, B. (2006) Studies of diesel engine particle emissions during transient operations using an engine exhaust particle sizer. Aerosol Sci. Technol., 40, 1002–1015. International Life Sciences Institute (ILSI) (2000) The relevance of the rat lung response to particle overload for human risk assessment, in ILSI Risk Science Institute Workshop, vol. 12 (1–2) (ed. D.E. Gardner), Inhalation Toxicology, pp. 1–148. U.S. Environmental Protection Agency (2002) Health assessment document for diesel engine exhaust. EPA Report No. 600/8-90-057F. U.S. Environmental Protection Agency (1990) Clean Air Act. Integrated Risk Information System (IRIS) (2003) Diesel engine exhaust. Available at: http://www.epa.gov/iris/ subst/0642.htm. Diaz-Sanchez, D., Dotson, A.R., Takenaka, H., and Saxon, A. (1994) Diesel

j373

j 15 Toxicology of Combustion Products

374

48

49

50

51

52

53

54

55

exhaust particles induce local IgE production in-vivo and alter the pattern of IgE messenger RNA iso-forms. J. Clin. Invest., 94, 1417–1425. Diaz-Sanchez, D., Tsien, A., Casillas, A., Dotson, A.R., and Saxon, A. (1996) Enhanced nasal cytokine production in human beings after in vivo challenge with diesel exhaust particles. J. Allergy Clin. Immunol., 98, 114–123. Diaz-Sanchez, D. (1997) The role of diesel exhaust particles and their associated polyaromatic hydrocarbons in the induction of allergic airway disease. Allergy, 52 (38 suppl.), 52–56. Diaz-Sanchez, D., Garcia, M.P., Wang, M., Jyrala, M., and Saxon, A. (1999) Nasal challenge with diesel exhaust particles can induce sensitization to a neoallergen in the human mucosa. J. Allergy Clin. Immunol., 104, 1183–1188. Tsien, A., Diaz-Sanchez, D., Ma, J., and Saxon, A. (1997) The organic component of DEP and phenanthrene, a major polyaromatic hydrocarbon constituent enhances IgE production by IgEsecreting EBV-transformed human B cells in-vitro. Toxicol. Appl. Pharmacol., 142 (2), 256–263. Mauderly, J.L., Griffith, W.C., Henderson, R.F., Jones, R.K., and McClellan, R.O. (1990) Evidence from animal studies for the carcinogenicity of inhaled diesel exhaust, in Nitroarenes (ed. P.C. Howard), Plenum Press, New York, p. 13. Mauderly, J.L. (1992) Diesel exhaust, Environmental Toxicants - Human Exposures and Their Health Effects (ed. M. Lippmann), Van NostrandReinhold, New York, pp. 19–162. Heinrich, U., Fuhst, R., and Rittinghausen, S. (1995) Chronic inhalation exposure of Wistar rats and two different strains of mice to diesel engine exhaust, carbon black, and titanium dioxide. Inhal. Toxicol., 7, 533–556. Nikula, K.J., Snipes, M.B., and Barr, E.B. (1995) Comparative pulmonary toxicities and carcinogenicities of chronically inhaled diesel exhaust and carbon black in F344 rats. Fundam. Appl. Toxicol., 25, 80–94.

56 Sjogren, M., Li, H., Banner, C., Rafter, J.,

57

58

59

60

61

62

63

64

65

66

Westerholm, R., and Rannug, U. (1996) Influence of physical and chemical characteristics of diesel fuels and exhaust emissions on biological effects of particle extracts: a multivariate statistical analysis of ten diesel fuels. Chem. Res. Toxicol., 9, 197–207. Valavanidis, A., Salika, A., and Theodosopoulou, A. (2000) Generation of hydroxyl radicals by urban suspended particulate air matter. The role of iron ions. Atmos. Environ., 34, 2379–2386. U.S. Environmental Protection Agency (2001) Consumer Factsheet on: Chromium, 30 July. Available at: http:// www.epa.gov/safewater/dwh/c-ioc/ chromium.html. ATSDR (2001) Tox FAQs for Chromium, Agency for Toxic Substances and Disease Registry, Atlanta, GA. Liu, S.J. and Dixon, K. (1996) Induction of mutagenic DNA damage by chromium (VI) and glutathione. Environ. Mol. Mutagen., 28 (2), 71–79. Cheng, L., Liu, S.J., and Dixon, K. (1998) Analysis of repair and mutagenesis of chromium-induced DNA damage in yeast, mammalian cells, and transgenic mice. Environ. Health. Perspect., 106 (suppl. 4), 1027–1302. Liu, S.J., Medvedovic, M., and Dixon, K. (1999) Mutational specificity in a shuttle vector replicating in chromium(VI)treated mammalian cells. Environ. Mol. Mutagen., 33, 313–319. Wu, W.D., Samet, J.M., Ghio, A.J., and Devlin, R.B. (2001) Activation of the EGF receptor signaling pathway in airway epithelial cells exposed to Utah Valley PM. Am. J. Physiol - Lung C, 281, L483–L489. Pepelko, W.E. and Peirano, W.B. (1983) Health effects of exposure to diesel engine emissions. J. Am. Coll. Toxicol., 2, 253–306. Madden, M.C., Richards, J.H., Dailey, L.A., Hatch, G.E., and Ghio, A.J. (2000) Effect of ozone on diesel exhaust particle toxicity in rat lung. Toxicol. Appl. Pharmacol., 168, 140–148. Schwartz, D.A. (2001) Does inhalation of endotoxin cause asthma? Am. J. Respir. Crit. Care Med., 163, 305–313.

References 67 Arbour, N.C., Loren, E., Schutte, B.C.,

68

69

70

71

72

73

74

75

76

Zabner, J., Kline, J.N., Jones, M., Frees, K., Watt, J.L., and Schwartz, D.A. (2000) TLR4 mutations are associated with endotoxin hyporesponsiveness in humans. Nat. Genet., 25, 187–191. Leikauf, G.D., Mc Dowell, S.A., Gammon, K., Wesselkamper, S.C., Bachurski, C.J., Puga, A., Wiest, J.S., Leikauf, J.E., and Prows, D.R. (2000) Functional genomics of particle-induced lung injury. Inhal. Toxicol., 12 (suppl. 3), 59–73. Prows, D.R. and Leikauf, G.D. (2001) Quantitative trait analysis of nickelinduced acute lung injury in mice. Am. J. Respir. Cell. Mol. Biol., 24, 740–746. McDowell, S.A., Gammon, K., Bachurski, C.J., Wiest, J.S., Leikauf, J.E., Prows, D.R., and Leikauf, G.D. (2000) Differential gene expression in the initiation and progression of nickelinduced acute lung injury. Am. J. Respir. Cell Mol. Biol., 23, 466–474. Johnston, C.J., Finkelstein, J.N., Mercer, P., Corson, N., Gelein, R., and Oberdorster, G. (2000) Pulmonary effects induced by ultrafine PTFE particles. Toxicol. Appl. Pharmacol., 168, 208–215. Hunter, D.D. and Undem, B.J. (1999) Identification and substance P content of vagal afferent neurons innervating the epithelium of the guinea pig trachea. Am. J. Respir. Crit. Care Med., 159, 1943–1948. Park, J.H., Gold, D.R., Spiegleman, D.L., Burge, H.A., and Milton, D.K. (2001) House dust endotoxin and wheeze in the first year of life. Am. J. Respir. Crit. Care Med., 163, 322–328. Hauser, R., Eisen, E.A., Pothier, L., and Christiani, D.C. (2001) A prospective study of lung function among boilermaker construction workers exposed to combustion particulates. Am. J. Ind. Med., 39, 454–462. ATSDR (1996) Tox FAQs for Polycyclic Aromatic Hydrocarbons (PAHs), Agency for Toxic Substances and Disease Registry, Atlanta, GA. Tolbert, P.E., Klein, M., Metzger, K.B., Peel, J., Flanders, W.D., Todd, K., Mulholland, J.A., Ryan, P.B., and Frumkin, H. (2000) Interim results of the

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study of particulates and health in Atlanta (SOPHIA). J. Expos. Anal. Environ. Epidemiol., 10, 446–460. Elder, A.C.P., Gelein, R., Finkelstein, J., Cox, C., and Oberdorster, G. (2000) Endotoxin priming affects the lung response to ultrafine particles and ozone in young and old rats. Inhal. Toxicol., 12 (suppl. 1), 85–98. Squadrito, G., Cueto, R., Deutsch, A., Pryor, W.A., and Dellinger, B. (2001) Damage to human DNA by redox active radicals on PM2.5. J. Free Radic. Biol. Med., 31 (9), 1132–1138. Dellinger, B., Pryor, W.A., Cueto, R., Squadrito, G.L., and Deutsch, W.A. (2000) The role of combustion-generated radicals in the toxicity of PM2.5. Proc. Combust. Inst., 28, 2675–2689. Dellinger, B., Pryor, W.A., Cueto, R., Squadrito, G.L., and Deutsch, W.A. (2001) Role of free radicals in the toxicity of airborne fine particulate matter. Chem. Res. Toxicol., 14, 1371–1377. Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Kazuhiko, I., and Thurston, G.D. (2002) Lung cancer, cardiopulmonary mortality, and longterm exposure to fine particulate air pollution. JAMA, 287, 1132–1141. U.S. Environmental Protection Agency (2000) Draft: Exposure and Health Assessment of 2,3,7,8Tetrachlorodibenzo-p-dioxin (TCDD) and Related Compounds. EPA Report No. 600/P-00-001. U.S. Environmental Protection Agency (2001) Questions and answers about dioxins, 7 August. Available at: http:// www.epa.gov/ncea/pdfs/dioxin/ dioxin_questions_and_answers.pdf. U.S. Environmental Protection Agency (2001) Locating and Estimating Air Emissions from Sources of Dioxins and Furans. EPA Report No. 454/R-97-003, 7 August. Available at: http://www.epa. gov/ttn/chief/le/dioxin.pdf. Wikstrom, E. and Marklund, S. (2001) The influence of level and chlorine source on the formation of mono- to octachlorinated dibenzo-p-dioxins, dibenzofurans and coplanar polychlorinated biphenyls during

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87

88

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90

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combustion of an artificial municipal waste. Chemosphere, 43, 227–234. Ghio, A.J., Kim, C., and Devline, R.B. (2000) Concentrated ambient particles induce mild pulmonary inflammation in healthy human volunteers. Am. J. Respir. Crit. Care Med., 162, 981–988. Ghio, A.J. and Huang, Y.C.T. (2004) Exposure to concentrated ambient particles (CAPs): a review. Inhal. Toxicol., 16, 53–59. Gupta, T., Demokritou, P., and Koutrakis, P. (2004) Development and evaluation of an ultrafine particle concentrator for inhalation toxicological studies. Inhal. Toxicol., 16 (13), 851–862. Samet, J.M., Rappold, A., Gaff, D., Cascio, W.E., Berntsen, J.H., Huang, Y.C.T., Herbst, M., Bassett, M., Montilla, T., Hazucha, M.J., Bromberg, P.A., and Devlin, R.B. (2009) Concentrated ambient ultrafine particle exposure induces cardiac changes in young healthy volunteers. Am. J. Respir. Crit. Care Med., 179 (11), 1034–1042. Dockery, D.W., Pope, A. III, Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris, B.G., and Speizer, F.E. (1993) An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med., 329, 1753–1759. Pope, C.A. III, Thun, M.J., Namboodiri, M.M., Dockery, D.W., Evans, J.S., Speizer, F.E., and Heath, C.W. (1995) Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. Am. J. Respir. Crit. Care Med., 151, 669–674. Suh, H.H., Bahadori, T., Vallarino, J., and Spengler, J.D. (2000) Criteria air pollutants and toxic air pollutants. Environ. Health Perspect., 108 (S4), 625–633.

93 Abt Associates, Inc . (2000) The

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Particulate Related Health Benefits of Reducing Power Plant Emissions, Clean Air Task Force, Boston, MA. Levy, J. and Spengler, J. (2001) Health benefits of emissions reductions from older power plants. Risk in Perspective, 9, 1–4. Available at: http://www.hcra. harvard.edu/pdf/april2001.pdf. Cifuentes, L., Borja-Aburto, V.H., Gouveia, N., Thurston, G., and Davis, D.L. (2001) Hidden health benefits of greenhouse gas mitigation. Science, 293, 1257–1259. Bruce, N., Perez-Padilla, R., and Alback, R. (2000) Indoor air pollution in developing countries: a major environmental and public health challenge. Bull. WHO, 78, 1078–1092. World Resources Institute (1998) UNEP, UNDP, World Bank. 1998–1999 World Resources: A Guide to the Global Environment, Oxford University Press, Oxford. U.S. Environmental Protection Agency (2001) Fact Sheet: EPA’s Revised Particulate Matter Standards, 8 October. Available at: http://www.epa.gov/ttn/ oarpg/naaqsfin/pmfact.html. Gibbs, L.M. (1993) The impact of state air quality and product regulations on current and future fuel properties. ASTM STP 1160, The Impact of US Environmental Regulations on Fuel Quality. McDonald, J.D., Harrod, K.S., Seagrave, J., Seilkop, S.K., and Mauderly, J.L. (2004) Effects of low sulfur and a catalyzed particle trap on the composition and toxicity of diesel emissions. Environ. Health Perspect., 112 (13), 1307–1312.

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16 Explosion Safety Gordon E. Andrews and Herodotos N. Phylaktou

16.1 Introduction

Explosion safety is an important component of the more general area of loss prevention and safety in industry, as detailed in Chapter 18. Explosion safety is a legal requirement in industry in most countries in the worldwide. In Europe, it is controlled by the ATEX Directives [1, 2] and in the UK these are implemented by the Dangerous Substances and Explosive Atmospheres Regulations (DSEAR) [3]. Similar regulations apply in the USA (NFPA 68 [4]) and in countries that use the UN equivalent regulations, such as New Zealand [5]. These regulations require that risks in the use of dangerous substances are eliminated, or reduced to the minimum practical; if the risk cannot be eliminated, then explosion protection measures must be used. Recent major explosion incidents at Texas City in the USA [6] and Buncefield in the UK [7] illustrate the major devastation that occurs following large spillages of flammable substances. The vapor tank explosion incidents in aircraft, such as the 1996 New York TWA disaster, bring home the devastation that occurs when explosion risks are not foreseen and protected against. In this chapter, dangerous substances such as gases, vapors, aerosols or mists and dusts are considered. these are usually referred to in regulations as “flammable” substances. In Europe, their ranking is termed “extremely” and “highly” as well as merely “flammable,” while in the UN-based regulations there are “very high,” “high,” “medium,” and “low” ratings of flammability. Both ratings of flammability risk are based on flash point measurements. The key divisions in Europe are 0, 21, and 55
C, whereas in the UN definitions they are 23, 35, 50, and 93
C. In Europe, diesel fuel is thus not flammable under this definition, as substances with a flash point above 55
C (such as diesel) are not within the scope of the regulations. It will come as a surprise to diesel engine manufacturers that diesel is not considered to be flammable, as they operate by exploding diesel/air mixtures in a piston engine! In the European and UN ratings, lubricating oil and hydraulic fluids are deemed not to be flammable substances, and hence are outside the scope of regulations. Hydraulic fluids are generally hydrocarbons and operate under high pressure. Small

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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leaks of such fluids may give rise to mist explosion hazards, and large leaks to a pool fire hazard; hence, their elimination from ATEX risk analysis on a flash point basis is not sensible. All diesel engine-powered electrical generating systems and marine diesels have their large lubricating oil sumps with explosion protection vents. This is because there have been several lubricating oil sump explosions prior to this protection being fitted. This is because diesel and lubricating oil will explode at atmospheric temperature when they occur as a mist. Also, diesel oil sumps operate at around 100
C oil temperature, and with the air in the space above also hot. Thus, the flammability definition applies only to hot liquids in ambient temperature air. Diesel engines have fuel leaks from the cylinder into the oil past the piston rings. Typically, the oil has a 1–2% diesel dilution after about 1000 h of use. At 100
C, this diesel fuel dilution is above the flashpoint of diesel, and could also be part of the explosion risk. Both, the mist and diesel vapor explosions become more flammable, or the lean flammability limit becomes leaner, at 100
C compared to ambient conditions (as will be discussed later). These are just two examples of practical industrial explosion risks that occur under conditions that are outside the remit of ATEX, due to inappropriate definitions of explosive atmospheres and flammable substances. One of the key problems in the European and UN regulations on these issues is a blatantly incorrect definition of a flammable atmosphere, and also of flammability. This leaves major areas of industrial explosion hazards outside the remit of the very regulations that were designed to control them. Both sets of regulations define an explosive atmosphere as a mixture of air and a gas, vapor, mist or dust under atmospheric conditions that will propagate a flame. The limit in the definition to air as the oxidant and atmospheric conditions is nonsense, as it implies that mixtures of gases, vapors, mist and dust with pure oxygen, or any oxygen level above or below that of air, are not a hazard. Industrial processes at high pressure and/or at elevated temperatures also lie outside the remit of the regulations, as the latter only apply to situations at ambient air conditions. The reality is that the explosion risks are greater at elevated temperatures and elevated oxygen levels, but this point will not be found this in the DSEAR Guidance. This is all due to incorrect definitions of basic parameters in explosion safety. Clearly, this type of confusion in the regulations is a recipe for disaster. Nonetheless, this exclusion of obvious explosion risks in industry from the remit of the ATEX Directives and of the UK DSEAR Regulations does not mean that there is no control of safety in these situations. In the UK, the primary law of the Health and Safety at Work Act 1974 (HSWA) still applies as the DSEAR Regulations are issued under this Act. This requires, in Section 2.2.b, that all employers should provide arrangements for ensuring safety and absence of risk to health in connection with the use, handling, storage and transport of articles and substances. Explosion risks from dangerous substances are thus covered by this Regulation, with no limitation on the nature of the explosive substance and the oxidizing atmosphere, as in the ATEX and DSEAR regulations. In a similar way, the primary European Directive on Safety in the Workplace (89/ 391/EEC) has, in Section 2a, the requirement that “. . .the employer shall, taking into account the nature of activities of the enterprise and/or establishment, evaluate the

16.2 Explosion Stoichiometry

risks to the safety and health of workers, in the choice of work equipment, the chemical substances or preparations used . . ..” Again there is no limitation on the type of substances or the oxidizing atmosphere, as there is in the ATEX Directives. Also the Framework Directive in relation to any risks, which includes explosion risks, requires the risk to be avoided, evaluated and combated using the principles of use of latest explosion protection technology and replacing substances with an explosion risk with those without an explosion risk, where this is practical. Essentially, the UK HSWA has required this in the UK since 1974. This primary safety legislation applies to the industrial explosion risks that are excluded from ATEX and DSEAR and equivalent UN legislation by their restrictive definitions of flammable substances and explosive atmospheres. The ATEX regulations also refer to “potentially explosive atmospheres”; these are defined as those that could become explosive due to local and operational conditions. However, it is clear in ATEX Annex II 1.01 that potentially explosive atmospheres are those that potentially can become an “explosive atmosphere,” as this is defined in the Directive. For example a methane–air equivalence ratio (W) of 0.25 is not flammable at atmospheric conditions, but is flammable at 900 K. However, this is not a “potentially explosive atmosphere,” as the oxidizer is not at ambient conditions and hence ATEX does not apply. However, if the process circumstances are such that normally the process operated at an equivalence ratio of 0.25 at atmospheric conditions, but due to a malfunction further methane could leak to make the mixture W > 0.5, then the circumstances are “potentially explosive” and ATEX applies. However, if measures are in place that prevent the atmosphere being W > 0.25 then ATEX does not apply, as there is no flammable mixture. The issue of the operational equivalence ratio, or how much flammable substance is present, is crucial to the operation of ATEX, and this chapter will deal comprehensively with this issue. Clearly, it is for an employer to demonstrate that measures are in place to prevent the formation of a flammable atmosphere in terms of the concentration of gas, vapor, mists, and dusts. Hence, a knowledge of the lean flammability limit of these substances in equivalence ratio terms is of fundamental importance in explosion safety.

16.2 Explosion Stoichiometry

A major factor in explosion safety is the knowledge of how much of a gas, vapor, mist, or dust must be mixed with an oxidant for an explosion to be possible. It will be shown in the next section that if the stoichiometric (W ¼ 1) mixture can be calculated, then the lean explosion limit mixture (WLL) can be predicted. The minimum ignition energy of gas/air mixtures is <1 mJ for most flammable gases and vapors, <10 mJ for most mists, and <100 mJ for most dusts. Thus, the elimination of ignition sources is not the most reliable method of explosion protection, as the energy required is so trivial. However, an ignition source of 10 KJ cannot ignite a flammable gas, vapor, mist, or dust if the mixture ratio is not in the flammable range, and this is the most

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crucial aspect of explosion protection. This also provides the first line of defense against explosions – that of ventilation or mixture dilution. The issue of stoichiometry is of equal importance in all aspects of combustion, including that of fire safety and fire spread in buildings (see Chapters 19 and 20). Here, the concept of the global fire stoichiometry is important, especially in relation to toxic gas production. The global fire stoichiometry is the stoichiometric air/fuel ratio by mass divided by the total air mass ventilation of a fire divided by the total mass of material consumed in a fire. It will be shown in this chapter that the key difference in fire material is the proportion of pure hydrocarbon fire load and the HCO-type fire load, such as wood and textiles. In a fire, there is no mixing of the air and fuel, which causes the fuel to burn as a diffusion flame; however, in a high-temperature fire the materials can be gasified and a propagating flame, flash fire, or ceiling layer explosion can occur. The phenomenon of backdraft is also an explosion, with enclosed airstarved compartment fires generating flammable gases essentially through a gasification of the fire load such that, if air is then added by opening a door, the result is a violent, explosion flame. This can cause pressure rise with subsequent window fracture, as well as fire escalation due to air being added to the fire; this results in the fire becoming hotter as the equivalence ratio moves from rich to nearer stoichiometric. In the descriptions provided in this chapter, the fuel and air are mixed prior to ignition of the explosion flame, and only fully premixed fuel and air combustion is considered. Stoichiometry is also vitally important in burner design and furnace heating thermal efficiency (these are dealt with in other chapters of this volume). Most text books on stoichiometry calculations are based on gases, and derive the results as volume percent (vol%), but this is of little use if there is a mist or dust explosion hazard. These calculation methods are also not applicable to practical liquid fuels, such as kerosene, with hundreds of different hydrocarbons. The only method of explosion stoichiometry which deals with gases, vapors, mists and dust explosion hazards is mass-based air/fuel (A/F) ratios. Likewise, although most textbooks only derive combustion stoichiometry for pure hydrocarbons, many explosion hazards – particularly for dusts – involve materials with HCO elemental analysis. It may be shown that the stoichiometric air/fuel ratio by mass for any hydrocarbon (HC) or HCO flammable gas, vapor, mist, or dust is given by Equation 16.1, while the oxygen/ fuel stoichiometric mass ratio is given by Equation 16.2: Air=Fuel Mass ¼ ½ð1 þ y=4Þz=2 137:94=ð12 þ y þ 16zÞ

ð16:1Þ

Oxygen=FuelMass ¼ ½ð1 þ y=4Þz=2½32=ð12 þ y þ 16zÞ

ð16:2Þ

where y is the elemental hydrogen : carbon (H/C) ratio in the fuel and z is the oxygen : carbon (O/C) ratio. The difference in the two equations is that air contains 23.2% of oxygen by mass (20.9% by volume). Table 16.1 shows the application of Equation 16.1 to materials that can have explosion hazards for gases, mists or dusts as the fuel. In this table, HCs that are gases, mists, and dusts are compared together with HCO materials that are mists or dusts. The stoichiometric mixture for flammable substances is not dependent on ambient temperature or pressure, but rather depends only on the elemental

PMA PMMA Polyethylene Polypropylene

Kerosene Diesel Hydrogen Ethanol Ethylene glycol Isopropanol Acetone Cellulose

Methane Propane Ethylene Propylene n-Hexane n-Octane Benzene Cyclohexane Hexadecane Gasoline

Material

0 0 0 0 0 0 0 0 0 0 – 0 0

a)

0.5 1.0 0.33 0.33 0.833 – 0.50 0.40 0 0

a)

3.0 3.0 2.67 3.0 1.67 – 1.50 1.60 2.0 2.0

z

4 2.67 2.0 2.0 2.33 2.25 1.0 2.0 2.125 2.0 – 1.95 1.90

y 17.2 15.7 14.8 14.8 15.2 15.1 13.3 14.8 15.1 14.8 – 14.7 14.6 34.5 9.0 5.56 10.39 9.52 5.12 – 7.27 8.28 14.8 14.8

A/F W ¼ 1 9.5 4.0 6.5 4.4 2.15 1.64 2.71 2.27 0.85 2.4 – 1.4 1.0 29.6 6.51 7.71 4.43 3.9 – – – – – –

% by vol. W ¼ 1 69.8 76.4 90.2 90.2 78.9 79.5 90.2 81.1 79.5 81.1 – 81.6 82.1 34.8 133 216 116 126 234 – 165 145 81 81

g m3 W ¼ 1e) 0.53 0.55 0.48 0.54 0.56 0.61 0.52 0.56 0.57 0.55 0.60 [20] 0.51 0.50 [21] 0.14 0.66 – 0.45 0.64 – – – – – –

WLL [14, 15] 0.46 0.425 0.38 – 0.47 – – – – – – – – 0.12 0.48 – 0.45 – – – – – – –

WLL [16, 18, 19]e) 37, 32 42, 32 43, 34 49, 44, 37 48 47 45 45 45 48 42 41 5, 4 88, 64 – 52, 52 81 55 [22] 60 [9] 30 [9] 30 [22] 30 [9, 22] 35 [22]

LL (g m3) [14, 16]

Table 16.1 The stoichiometric A/F ratio by mass for a range of gases, mists, and dusts with the lean limit equivalence ratio, WLL. Measurement of WLL at ambient temperature and pressure.

– – – – – – – – – – – – – – – – – – 0.235 0.256 0.182 0.207 0.37 0.43 (Continued)

WLL Dust

16.2 Explosion Stoichiometry

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0.4

0.5 0.416 1.55 0.718 0 0.073

–

1.5 1.46 3.58 1.50 0 0.778

–

z

0.8

y

9.51c)

7.22 8.09 3.83 5.61 11.5 12.7b)

7.18

A/F W ¼ 1

166 148 313 214 104 94.5b)

– – – – – – 126c)

167

–

–

g m3 W ¼ 1e)

% by vol. W ¼ 1

–

– – – – – –

–

WLL [14, 15]

–

– – – – – –

–

WLL [16, 18, 19]e)

60 [4, 9] 55 [22]

40 [22] 30–60 [9]d) 20–70 [23]d)

40 [22]

LL (g m3) [14, 16]

0.44c)

0.55 0.58b)

0.24 0.3 0.14

0.24

WLL Dust

a) Equation 16.1 does not apply for hydrogen. b) On a dry as free basis. c) 5% water and 20% ash. d) Experimental values from Refs [9, 22, 23] do not give the wood composition, and there is a range of values which indicated a dependence on composition. Wood lean limit equivalence ratios are thus ranges and not specific to a particular wood. e) Assuming the density of air at ambient temperature and pressure is 1.2 kg/m3.

Polyethylene terephthalate (PET) Polyvinyl acetate Pitch pine [10] Spruce [10] Straw [11] Carbon Bituminous coal [12, 13]

Material

Table 16.1 (Continued)

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j 16 Explosion Safety

16.2 Explosion Stoichiometry

composition of the substance. The mixture is also independent of the chemical structure of the material, as shown in Table 16.1, where solid polyethylene, liquid gasoline mist with a complex range of hydrocarbons and the pure hydrocarbon cyclohexane all have the same stoichiometric A/F ratio by mass, because they all have the same H/C ratio of 2.0. For dusts, there is often ash and absorbed water present. If the A/F ratio by mass stoichiometry is evaluated on the elemental composition on a dry ash free (daf) basis, then the actual A/F ratio can easily be calculated if the water (w) and ash (a) content are known on a mass concentration basis, as shown in Equation 16.3: A=F by mass ¼ A=F ðdaf Þ½1ðw þ aÞ

ð16:3Þ

For example, if a bituminous coal had a typical 5% moisture and 20% ash, then the actual A/F ratio by mass would be 9.5/1 (126 g m3), instead of the 12.7 on a daf basis. Table 16.1 also lists the stoichiometry (in g m3), as this is the normal way that dust/air mixtures are referred to. In this case, the results are for ambient temperature and pressure with a typical air density of 1.2 kg m3. It is considered that g m3 is a poor unit to use for stoichiometry, as it is only valid for conditions where flammability was determined at ambient conditions, so that the volume is the same in both the test method and the equipment to be protected. However, it cannot be applied if the temperature in the equipment is higher, as there is then less mass of air in unit volume. Nor can it be applied if the dust is in a high-pressure facility, as there is more mass of air in unit volume than at atmospheric pressure. This has led to misleading reporting of the influence of temperature and pressure on flammability limits for dust mixtures [8, 9]. If stoichiometry is always expressed in terms of A/F ratios on a mass basis, then this confusion cannot arise and confusion in data leads to accidents. The data in Table 16.1 highlight several interesting features. For all hydrocarbons, the stoichiometry is very similar on a mass basis as they are all 14.8/1  10%, whereas on a volume concentration basis they all appear to be different, with a range an order of magnitude from 1% to near 10% by volume. Thus, the type of hydrocarbon does not really matter in an explosion stoichiometry risk assessment as they are all similar; however, this is not apparent from the normal data sources for combustion stoichiometry, which are generally volume concentration-based. The major change in explosion stoichiometry is apparent for liquids (mists) and solids (dusts) with oxygen in their elemental composition. These compounds are much more variable, as there is greater variability in the y- and z-values. Wood is a very variable dust, and the two examples in Table 16.1 are extreme compositions, between which most other wood compositions fall. The data in Table 16.1 also show that biomass fuels in addition to wood, such as straw and tree bark, also have stoichiometric A/F ratios within the range for woods. Thus, for wood dusts or for biomass dust explosion risk analysis, the stoichiometric A/F ratio can vary between 3.8 and 8.1. Consequently, for any explosion risk analysis for wood dust or biomass dusts it is essential to know the actual composition of the wood. For many woods, the typical A/F ratio is about 6/1, and this would be the most reasonable value to use in a generic risk assessment.

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There is current interest in the explosion risk of biomass fuels as they are pulverized and supplied, mixed with air, at elevated temperatures in co-firing applications with coal. To the present authors’ knowledge, the explosion risks under these circumstances have not yet been evaluated; moreover, very few data currently exist on dust explosions with wood dust or biomass fuels, nor any direct data for the vent protection of such explosion risks [9].

16.3 Lean Flammability Limits

For explosion risk analysis, the risk normally occurs when fuel is leaked into air so that the explosion boundary is approached from the lean side; hence, the lean flammability limit is the main safety requirement. It will be shown below that the lean limit for most hydrocarbon fuels is similar in stoichiometric terms, and can be predicted knowing the stoichiometric concentration. The lean limit is determined by a critical minimum flame temperature for the hydrocarbon oxidation chemistry to be fast enough to sustain a flame [14, 15], which will be shown to be 1400–1500 K. However, the rich limit is much more dependent on the type of hydrocarbon [14, 15]. For dusts, the rich limit is rarely determined [9] and the lean limit is most commonly only measured. In this chapter, only the lean limit mixture will be discussed as this is the key safety data that is required, if operational parameters in an industrial process are to be set to achieve conditions that are outside the lean limit and thus have no explosion hazard. For many years, the lean flammability of gases and vapors was determined using equipment developed at the US Bureau of Mines [14, 15]. This consisted of a 1.5 mhigh vertical tube of 50 mm diameter, with a limit definition such that the flame with bottom ignition must travel through the vertical tube and emerge at the top – that is, a 1.5 m travel distance. Flames that started but quenched part way up the tube were deemed to be “not flammable”. The measured lean limits, converted to equivalence ratio, are listed in Table 16.1 for several gases and vapors. For most gas and mist hydrocarbons and alcohols, these are in the equivalence ratio region of 0.45–0.64. A large part of this range comprises the data for alcohols, while most of the hydrocarbon data falls into the lean limit equivalence ratio range of 0.5–0.55. This includes data on gasoline, kerosene, and diesel mist explosions. This lean limit range converts to a range of critical lean limit temperatures of 1480–1550 K [24]. It is considered that to take a lean limit critical temperature of 1500 K would be a conservative value in risk analysis, based on the US Bureau of Mines lean limit measurement method. Most existing safety data are based on this lean limit measurement method. In recent years, with the advent of legislation on explosion prevention, standard methods of lean flammability have been developed which must be used for regulatory purposes. In Europe in 2004 [16], the method of measurement adopted under the ATEX Directive was based on the German standard method [17]. The EU standard consisted of an 80 mm-diameter vertical tube of 300 mm length, with the definition of

16.3 Lean Flammability Limits

the limit being that the flame must travel at least 100 mm from the spark. The only difference from the German standard was that the latter used a 60 mm-diameter tube [17], but both methods produced the same results [18, 19]. Both, the EU and German standard methods resulted in lean limits for gases and vapors that were leaner than those based on the US Bureau of Mines method; this was due to the changes in definition of the minimum travel distance. The difference in these tube diameters and the 50 mm tube used by the US Bureau of Mines was not significant. The 100 mm travel distance-limited flames of the EU and German standards were unable to travel the 1.5 m distance of the US Bureau of Mines equipment, and so for almost a century these were rejected as “flammable” [25]. The European standardbased lean limits have been published for four HCs and for a range of hydrocarbons by Schroder and Molnarne [18], and both these and the standard values have been converted to the lean limit equivalence ratio in Table 16.1. Similar developments of lean limit standards have been derived in the US, and these generally give leaner flammability limits than do the US Bureau of Mines limits [24]. Lean limit data for the new European standard, or its German predecessor, have been published for only a few of the compounds in Table 16.1. However, the lean equivalence ratios for hydrocarbon gases are in the range 0.38–0.48, including alcohols and ethylene. However, if ethylene is excluded then the lean limit range for hydrocarbons and alcohols is 0.42–0.48, and an average of 0.45 would be reasonable. This corresponds to a critical lean limit flame temperature of 1382 K. In view of the uncertainties in the lean limit measurements, it would be reasonable to take 1400 K as the critical lean limit temperature for most hydrocarbon–air flames using the recent legislation approved lean limit measurement methods. This is 100 K lower than was concluded based on lean limit measurements using the US Bureau of Mines equipment. Andrews et al. [24] have reviewed 11 reported measurements of the lean limit for methane–air, all using legislation approved measurement methods in Europe and the US, and concluded that a reasonable mean value was W ¼ 0.47 (1421 K). Andrews and Bradley [26a] have also shown for methane–air explosions, that the leanest mixture that a flame can be measured, by high-speed laser Schlieren interferometry photography, propagating from a spark in a 300 mm-diameter cylindrical closed vessel, was 4.5% or an equivalence ratio of 0.47. The influence of buoyancy on this slowburning limit flame was to convect it vertically until it quenched on the vessel roof (150 mm from the spark), at which stage no significant pressure rise had occurred. The present authors have also measured the lean flammability limit in a closed explosion vessel that was 1.5 m long and 76 mm in diameter, which is effectively the US Bureau of Mines equipment operated under closed vessel conditions with bottom ignition and vertical flame propagation [26b]. Using a limit defined by a minimum pressure rise of 0.1 bar, a lean limit for methane was measured at W ¼ 0.45 (1382 K). This does not mean that the flame traveled 1.5 m, but that sufficient flame travel occurred to burn enough mixture to give a significant pressure rise. These two independent measurement techniques support the conclusion from recent legislation-approved methods of flammability measurements, that the lean limits for hydrocarbon–air explosions is leaner than that in most safety guidance, and that

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a critical lean limit temperature of 1400 K would be a reasonable design procedure. Andrews and Bradley [26a] measured the burning velocity of the limit flame at W ¼ 0.45 as 0.05 m s1, and that at W ¼ 0.5 as 0.1 m s1. For dusts, the lean limits in Table 16.1 have been determined from one of three test vessels: the 1.2 liter Hartmann vertical cylinder; the 20 liter sphere; and the ISO 1 m3 sphere [9]. All three methods are empirical, in that the method of dispersion of the dust is arbitrary due to the need to generate flow and turbulence so as to suspend the dust/air mixture. There is also an arbitrary ignition delay between injecting the air and dust into the vessels and igniting the mixture; a fixed delay must be specified in the tests, as this determines the turbulence level. There is no approved laminar dust cloud test method, whereas all gas and mist data is for laminar mixtures of fuel and air. The Hartmann equipment uses a continuous spark as an ignition source, while the 20 liter and 1 m3 spheres each use a 10 kJ chemical ignitor; this ensures that the lean limit is measured, and not the spark ignition limit. However, only for a small number of dusts does the Hartmann equipment give lean limits that are significantly different from the other methods, which use a higher ignition energy. The 20 liter sphere is not ideal for lean limit measurements [4], as the 10 kJ chemical ignitor produces a 1 bar pressure rise in a small volume with no dust present; this effectively preheated the mixture, which extended the lean limit. The data shown in Table 16.1 for the lean limit of dusts have been derived from either the Hartmann equipment in the data compilations of Maisey [22] and Field [23], or from either the 20 liter or 1 m3 spheres in the large data compilation of Eckhoff [9] (though the latter did not distinguish which data were obtained with which experimental equipment). For the cellulose and polyethylene dusts in Table 16.1, measurements were obtained for both the 1.2 liter Hartmann and either the 20 liter or 1 m3 sphere, and identical lean limits were found. All lean flammability results for gases, vapors, mists, and dusts can only be compared if they are expressed in terms of their lean limit equivalence ratio (as in Table 16.1). If there are hybrid mixtures of gases, vapors, mists and dusts, then these should be evaluated by summating the total mass of gas, vapor, mist and dusts, and expressing a total mass A/F ratio. If the type and proportion of gas, vapor, mist and dusts are known, then the mean stoichiometry can be evaluated and the overall equivalence ratio determined. There is no need for cumbersome equations for the lean limits of mixtures, nor for any graphical presentation of gas/dust flammability data, as is often carried out [8, 9]. Thus, a propane mixture with 2% propane by volume in the presence of 20 mg m3 of a hydrocarbon dust – both of which are not flammable as individual components – will be flammable when in combination, as each has W ¼ 0.25 and in combination W ¼ 0.5 will be flammable. The stoichiometry of hybrid mixtures of hydrocarbon and HCO materials can be determined if the proportions of the two materials are known, so that the mean stoichiometric composition can be evaluated on an A/F ratio by mass basis. The A/F ratio of a hybrid mixture can then be evaluated and its equivalence ratio determined; if this is >0.45 (European flammability measurement equivalent), then the hybrid mixture should be deemed as flammable.

16.3 Lean Flammability Limits

The data in Table 16.1 show that most hydrocarbon and alcohol gases and mists have the same lean limit equivalence ratio of close to 0.55 with the US Bureau of Mines method, or 0.47 with the new European measurement method, for which no mist measurements have been published. For pure hydrocarbon dust explosions, such as those for polyethylene and polypropylene, the data in Table 16.1 show the lean limits to be 0.37 and 0.43, respectively, and much closer to the new European flammability measurements for hydrocarbon gases and air. Whilst, for polyethylene dust, the lean limit is identical to that for gaseous ethylene (WLL ¼ 0.38), for polypropylene the dust limit at WLL ¼ 0.43 is significantly less than for gaseous propylene (WLL ¼ 0.54). These results suggest that solid polyethylene most likely decomposes (under the flash heating of flame propagation) to yield ethylene, as the related oligomers do not have the same flammability limits. However, in the case of polypropylene it is likely that a degree of decomposition has occurred to form the oligomer ethylene, and this is the most likely reason for the leaner lean limit of polypropylene dusts compared to propylene gas. For HCO dusts, the results are quite unusual, as the lean limit is much leaner in equivalence ratio terms than would be expected based on the results for alcohol vapors, which are very similar to hydrocarbons. For the four pure chemical HCO dusts, the lean limits in Table 16.1 vary between W ¼ 0.18 and 0.24, much leaner than for the pure hydrocarbon dusts. Very few data are available for the lean limits of naturally occurring HCO dusts, such as wood dusts; typically, where the HCO dusts data are available the HCO composition of the dust is not given. Table 16.1 lists the stoichiometric mixture for a range of biofuels, for which the HCO composition is known but for which no lean flammability data have been reported. Table 16.1 includes the reported lean limit data for wood dust explosions, for which no HCO composition was given. These results indicate that the lean limit data is compatible with that for pure HCO chemicals, and is approximately 0.2. Currently, there is an urgent need for additional data relating to the dust explosion hazards of biofuels, since major plants are now under construction for co-firing pulverized wood and straw with coal, yet no reliable published data are available pertaining to the explosion risks in the dust-handling plants. For agricultural dusts, such as those that occur in grain silos, a great deal of experimental data has been derived for explosion lean limits, but few have described the HCO composition of the dusts. However, it is likely that their behavior will be similar to other HCO materials, with a lean limit of about 0.2. To date, it has not been realized that HCO dusts had lean limits that were at least half those for hydrocarbon gases, mists and dusts in terms of their lean limit equivalence ratios, as the lean limits for dusts have rarely been expressed in equivalence ratio terms. One feature of the experimental results for HCO dusts in Table 16.1 is highlighted when the gas, mist and dust results are compared to dust measurement units of g m3. The data in Table 16.1 show that all gaseous fuel have limits of 30–37 g m3 for the European lean limit test method, while pure hydrocarbon dusts have lean limits of 30–35 g m3, and pure HCO chemicals in dust form have lean limits of 30–40 g m3. The mean value of the lean limit for wood dusts is 35 g m3, while many agricultural dusts of HCO elemental composition have lean limits of 30–60 g m3 [9, 22]. Taken

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together, all of these data suggest that, in explosions, the solid HCO dusts behave much as hydrocarbons, and this should be contrasted with alcohol vapors, which have lean limits of about W ¼ 0.5 rather than values of about W ¼ 0.2 associated with most HCO dusts. This difference is considered to be due to the vapor of alcohols having the same chemical composition as the liquid alcohol. However, there is no such material as a gaseous wood, nor any form of pure HCO polymer that exists as a polymer in the vapor form. When HCO solid materials are rapidly heated, as in a dust explosion flame, the first materials to be released are the hydrocarbons, and this is why the lean limit is close to that of hydrocarbons in mass terms rather than the expected lean limit as half the stoichiometric equivalence ratio. One requirement of the ATEX Directive is that explosion protection must be provided for the worst case explosions scenario, which is that of the most reactive mixture. For gases and vapors, this is the mixture with the highest flame temperature, which is close to W ¼ 1.05. However, for propane and butane the most reactive mixture in large explosions occurs for richer mixtures, close to W ¼ 1.3. This is due to the development of cellular flames which are influenced by mixture Lewis numbers (ratios of conduction to diffusion in the flame front), and result in a maximum flame acceleration due to these effects for rich mixtures. The problem here is that, for explosion protection data such as venting (see below), the experimental data have often not been determined for the most reactive mixture, but rather for the mixture with the highest laminar burning velocity for small flame measurements. As this does not include the cellular flame acceleration effect, the venting guidelines may provide data that are not applicable to the most reactive mixtures. For dusts, the situation is even worse, as the maximum reactivity occurs well on the rich side of stoichiometric. For many dusts the maximum reactivity occurs around 500 g m3 [8, 9, 22, 23]; a comparison with the data in Table 16.1 shows that the most reactive mixture is for W 3–5, which are much richer mixtures than for gases. The reason why many dusts have their most reactive mixture for very rich mixtures has attracted very little attention in the literature, and the flame propagation mechanism that gives rise to this is not known. It is possible that these very rich dust/air mixtures propagate a flame through gasification of the dust in the rich reaction zone, producing a CO/H2 mixture. However, it is essential that explosion protection measures are developed for these very reactive rich mixtures.

16.4 Stoichiometry and Lean Flammability for Metal Dust Explosions

Whilst the above sections have referred to explosive mixtures of gases, vapors, mists and dusts that are of HC or HCO elemental composition, these are not the only explosion hazards. Metal dust explosions have occurred on several occasions, causing devastating explosions that normally result in the complete destruction of the manufacturing facility where they occur [9]. The stoichiometry of metal dusts can be derived in the same way as for HCO materials, with the complication that metal dusts (M) have three types of oxide as the final product of combustion: MO (Ca, Mg, Zn, Cu); MO2 (C, Si, S); and M2O3 (Al, Fe,

16.4 Stoichiometry and Lean Flammability for Metal Dust Explosions

Cr, U). Carbon, silicon and sulfur are not normally referred to as metals, but are available as dusts in their elemental form, and have the characteristics of metal dust explosions. The three equations for the stoichiometric combustion of these different metal oxides are: MO metal oxides have a stoichiometric A=F by mass ¼ 69:0=MWM MO2 metal oxides have a stoichiometric A=F by mass ¼ 138=MWM M2 O3 metal oxides have a stoichiometric A=F by mass ¼ 103:5=MWM

The stoichiometry and measured lean flammability limit for a range of metal dusts, together with the lean limit expressed as an equivalence ratio, are listed in Table 16.2. Most reference books on dust explosions do not provide the stoichiometry involved in metal dust explosions, but this is the best way to compare them with HCO dust explosions. The data in Table 16.2 show that many metal dusts are as reactive as hydrogen (WLL ¼ 0.14) in terms of the their lean limit as an equivalence ratio (Ca, Mn, Zn, S, Al, U). In addition, some metal dusts have WLL much less than that for hydrocarbon/air explosions, but not as lean as for hydrogen (Si, Fe). The reason for this high reactivity of metal dusts relative to hydrocarbons, as expressed in their lean limits, is because their heat release with air or oxygen is greater than that of hydrocarbons. This makes the flame temperature higher, and it is flame temperature that controls the flame propagation. The ratio of heat release for metals to that of hydrocarbons is approximately [9] Ca ¼ 3.2, Si ¼ 2.1, Fe ¼ 1.3, Mg ¼ 3.1, Cr ¼ 1.9, Cu ¼ 0.7, Al ¼ 2.8, Zn ¼ 1.8, and S ¼ 0.7. For most metals, the lean limit equivalance ratio is leaner than for hydrocarbons the greater is the heat release ratio, although sulfur is the exception to this rule. Since many metal goods are manufactured from

Table 16.2 Stoichiometry and lean flammability limits of metal dust explosions.

Metal

MW

A/FW¼1

g m3W¼1

Measured LL (g m3)

WLL

Ca Mg Zn

40 24 65

1.73 2.88 1.06

694 417 1130

Cu C Si S

63.5 12 28 32

1.09 11.5 4.93 4.31

1101 104 243 278

Al Fe

27 56

3.83 1.85

313 649

Cr U

52 238

1.99 0.43

603 2759

60 [9] 30 [9] 125 [9] 250 [4] 750 [4] 60 [4, 9] 30 [9] 30 [4, 9] 20 [23] 30–60 [9] 500 [9] 200 [23] ? 125 [23] 480 [4]

0.086 0.07 0.11 0.22 0.68 0.58 0.25 0.11 0.072 0.1–0.2 0.77 0.31 ? 0.05 0.17

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powders that are filled into a mold, metal dust explosions represent a common risk in manufacturing processes. The very low lean limit equivalence ratios for most metal dusts means that a much superior ventilation is required than might be expected but, unfortunately, very little guidance has been provided on this subject.

16.5 The influence of Temperature, Pressure, and Inerts on Lean Flammability Limits

All of the above discussion on lean explosion limits has involved explosions at ambient pressure and temperature conditions, with air as the oxidant; indeed, this is how ATEX and DSEAR define a flammable atmosphere. However, in many industrial processes the pressures and temperatures within a manufacturing processes may be much higher, placing them outside ATEX but not outside of the more general European framework of safety law. In terms of the lean limit, the effect of the ambient temperature and pressure can be predicted by using the critical lean limit flame temperature; this is 1400–1500 K for hydrocarbons, depending on which lean limit method of determination is used. The effect of pressure and temperature can also be predicted, from their effect on the flame temperature, by using adiabatic flame temperature prediction procedures. These show that, for lean mixtures with a flame temperature <1800 K, there is no influence of pressure on the flame temperature, as pressure will only influence the flame temperature in a region where molecular dissociation is important, and this requires temperatures >1800 K and near-stoichiometric mixtures. Indeed, experimental evidence acquired to date has not demonstrated any influence of pressure on the lean limit for hydrocarbons [8, 15, 27]. The influence of initial temperature is to increase the flame temperature and, if the critical lean limit temperature is the same, then the lean limit becomes leaner as the initial temperature increases. This effect for methane–air is shown in Table 16.3, with five lean limit temperatures from 1400–1600 K and inlet temperatures from 300–1000 K. This type of prediction of the influence of ambient temperature on the lean limit was first demonstrated in studies conducted by the US Bureau of

Table 16.3 The lean limit equivalence ratio as a function of reactant temperature for a range of critical lean limit temperatures for methane–air from 1400 to 1600 K [24].

Reactant temperature (K) 300 400 500 600 700 800 900 1000

1400 K

1450 K

1500 K

1550 K

1600 K

0.48 0.43 0.39 0.35 0.32 0.27 0.21 0.17

0.50 0.45 0.41 0.37 0.34 0.30 0.23 0.19

0.52 0.48 0.43 0.39 0.36 0.32 0.26 0.21

0.55 0.50 0.45 0.41 0.38 0.33 0.29 0.24

0.58 0.53 0.48 0.44 0.40 0.35 0.32 0.28

16.5 The influence of Temperature, Pressure, and Inerts on Lean Flammability Limits

Mines [14, 15], and the method was shown to be in agreement with the limited experimental data using flammability equipment inside furnace tubes [15, 28, 29]. To emphasize the importance of the initial temperature, methane at 1000 K with a critical lean limit temperature of 1400 K has a lean flammability range as wide as that of hydrogen at ambient conditions. The flammable range clearly depends on the initial temperature of the mixture, and explosion protection measures designed for ambient conditions cannot be used for protection where the process operating temperature is significantly higher than ambient. The action of inert gases in limiting the explosion range can also be predicted using the critical lean limit temperature. At this limiting condition, the oxygen level in the mixture is the limiting oxygen concentration, which depends on the inert being used (normally, nitrogen is used). The limiting inert gas concentration is that which, when added to a stoichiometric mixture, lowers the flame temperature to below the critical value so that no explosion can occur. If the critical limit temperature is 1500 K for the US Bureau of Mines flammability measurement procedure and 1400 K for the European and German methods, then there must be corresponding differences in the quantity of inert gas required (more for the European method) and in the critical oxygen level (lower for the European method). The agreement of the critical temperature approach with experimental results was first demonstrated using the US Bureau of Mines data [15] by Mullins and Penner [30], where methane air inerting was shown to produce a critical inert gas limit temperature of 1530 K for carbon dioxide, water vapor, nitrogen, and argon as the inerts. The amount of inert required depends on the flammability measurement method, and is 23, 29, 38, and 51 for CO2, water vapor, nitrogen, and argon, respectively, when using the US Bureau of Mines method [14]. However, the European standard lean flammability method [16] and the similar German DIN standard [17] give leaner limits and a lower critical temperature, and this will lead to higher levels of inerts being required. These can be predicted using a 1400 K critical temperature, and have also been experimentally determined [19, 31]. For the nitrogen inerting of methane–air explosions, the limiting mixture using the German DIN method is 44% nitrogen [19, 31], compared to 39% for the US Bureau of Mines method [15]. The data in Table 16.4 show comparisons for the limiting oxygen using nitrogen as the inert for the US Bureau of Mines data [14, 15] and the German DIN method [19, 31], which produces similar results to the European standard. For ATEX compliance, a higher level of inerting will be required than under previous practice based on the US Bureau of Mines data [14, 15]. Where the process operates at elevated temperatures, however, the level of inerting required will be higher or the critical oxygen level lower, and these can Table 16.4

Gas Methane Propane Ethylene

Comparison of limiting oxygen concentration measurements with nitogen as the inert. US Bureau Mines [14, 15]

German DIN [19, 31]

12.1% 11.4% 10.0%

9.9% 9.8% 8.6%

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be predicted using the critical limit flame temperature. To date, very few reports have been made of inerting data for explosion protection at elevated inlet temperatures.

16.6 Ventilation Requirements for Explosion Safety

The key explosion prevention safety measure is to ensure that an explosive atmosphere is never created. To achieve this, the stoichiometric mixture must be calculated, as shown above in terms of the A/F ratio by mass. For hydrocarbon gases, mists and pure hydrocarbon dusts, and for liquid alcohol vapors and mists at ambient conditions, the lean limit may be taken as W ¼ 0.4 of the stoichiometric condition or 2.5-fold the stoichiometric A/F ratio by mass. However, for HCO dusts and many metal dusts the results in Tables 16.1 and 16.2 show that, typically, the lean limit is W ¼ 0.2 or five-fold the equivalence ratio, or fivefold the stoichiometric A/F ratio by mass. Operations at leaner mixtures than for the lean flammability limit will be safe, but how far outside the lean limit is safe? This requires some guidance, as variations in concentration may mean that although the overall mixture may be outside the flammable range, there might be richer pockets that are flammable, and these could cause the whole mixture cloud to propagate an explosion. The problem with most explosion safety guidance is that it does not state explicitly the ventilation requirements in equivalence ratio terms. In the UN-based guidance, as in the New Zealand hazardous substances regulations [5], the proportion of vapor or gas to air must always be less than 10% of the lower explosive limit (LEL). For hydrocarbon and alcohol gases, vapors and mists, this would require ventilation levels that would achieve at least a 375 : 1 air : fuel ratio. However, this is considered to be unrealistically conservative, and also ignores the high cost of forced ventilation systems, where electrical motor power consumption is a linear function of the air flow. The UK HSE has provided some practical guidance on local ventilation flow rates for explosion prevention [32]. This guidance (para. 127) recommends 60 m3 of fresh air, measured at 16
C and 1 atmosphere pressure, and this should be introduced into an oven for every liter of solvent evaporated. This is aimed at a solvent concentration that will not exceed 25% of the lean flammability limit; that is, a safety factor of 4 on the lean limit. This should then be a design equivalence ratio of 0.1 if the lean limit is 0.4. For air at a standard atmosphere pressure the density is 1.22 kg m3 at 16
C, while a typical solvent liquid density (e.g., petrol) would be 750 kg m3. The regulated A/F ratio by mass ¼ (60  1.22)/(0.001  750) ¼ 97.6/1. The stoichiometric A/F by mass for HCs is 15/1, and hence the equivalence ratio of the ventilated system would be 0.15, or a safety factor of 2.7 on the lean limit (not the safety factor of 4 that the standard aims for). However, these standards were written when the US Bureau of Mines lean limit data was accepted, when the safety factor would have been 3.3. If the solvent was ethanol with a stoichiometric A/F of 9/1, then the equivalence ratio would be 0.09, or a safety factor of 4.4 on the lean limit. It is likely that the guidance was written with alcohol solvents in mind. If an oven where solvents were being used was hot, then the guidance would be inappropriate.

16.6 Ventilation Requirements for Explosion Safety

It is recommended that, for hydrocarbon and alcohol gases, vapors, mists and dusts, a safety factor of 4 should be used on the lean flammability limit. For hydrocarbons, this would be a safety factor of 10 on the stoichiometric value or a ventilation A/F ratio of 150/1 by mass. For HCO and metal dusts, the same safety factor of 4 on the lean flammability limit should be used, but the 0.2 typical equivalence ratio of the lean mixtures would result in a design for ventilation that achieves a factor of 20 on the stoichiometric A/F ratio. For many of these HCO dusts, the stoichiometric A/F is about half that for hydrocarbons, but fortunately this results in a similar actual ventilation mass flow for hydrocarbons and for HCO dusts, due to differences in the stoichiometric A/F. Appendix 1 of the HSE guidance on local ventilation [32] gives Equation 16.4 for the ventilation flow rate, V, for a single component solvent that achieves 14 of the lean flammability limit: V ¼ 1:5 w=sM m3 min1

ð16:4Þ

where s is the lower flammable limit as a volume concentration and M is the molecular weight of a compound that evaporates at w kg h1. However, this is a very restrictive equation that can be made more general if the stoichiometry and the lean limit are expressed as the mass ratio of A/F. If the fuel evaporation rate is w kg h1, then the required ventilation air flow is 150 w kg h1 for hydrocarbons, if 14 of the lean limit (W ¼ 0.4) is to be achieved, while using the standard ambient density of air of 1.2 kg m3 this is 125 w m3 h1 (equivalent to 2.08 w m3 min1). For ethanol, this would be 90 w kg h1 and for methanol 60 w kg h1. For a HCO or metal dust, where the lean limit is 0.2, then the ventilation air flow for w kg h1 flow of a HCO dust with a stoichiometric A/F of 6 would be 120 w kg h1 of air for the same safety factor of 4 as for the solvent guidance. The above estimates show that, because hydrocarbons require the greatest airflow (150 w kg h1), it is safer to design the ventilation for HCs as this will lead to an over-ventilation for HCO solvents and dusts. A ventilation system designed for alcohol solvents would be less safe if operated on hydrocarbon solvents, and this is not always made clear in guidelines relating to ventilation rates. The simplicity of this approach provides the same ventilation standards for gases, vapors, mists and dusts, and only requires the mass flow rate, w, of the gas leak, vapor release, mist flow or dust release to be known; the required ventilation is then 150fold the fuel mass release flow rate for all flammable substances. In particular situations where only a particular dust hazard or only an alcohol vapor was being used, and no change of substances was likely, then the lower ventilation for the equivalence ratio of the particular substance could be used. For any general-purpose ventilation for flammability risk removal, the worst case of pure hydrocarbons should be assumed, with an air flow ventilation of 150 w kg h1. It must be remembered that the above recommendations are only valid if the gas, vapor, mist or dust is in air at ambient conditions. If the risk analysis is for a process oven at 400 K, then the lean flammability limits at 400 K must be used. The data in Table 16.3 show that this requires about 12% leaner mixtures – and hence 12% more ventilation air – if the safety margin of the ventilation system is not to be reduced. It is

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recommended that a safety factor of 4 on the lean limit should be used, at the operating conditions of the enclosure that is being protected. The lean limits should always be used in equivalence ratio form, as these apply to gases, vapors, mists and dusts explosion hazards.

16.7 Applications of Lean Limit Stoichiometry to Two Explosion Risk Situations

Accidental explosions frequently occur when cleaning tanks that have been used to store a flammable fluid. One typical incident involved a naphtha storage tank, most of which was buried underground with only the domed roof above ground (A. Craven, private communication). On a hot sunny day, the naphtha was emptied out by displacement with water, after which the water was drained away such that the tank was filled with air. When a flame cutter was used to provide access for a pipe attachment to the roof of the tank, a violent explosion occurred as the metal was cut through, and the tank roof was ejected over the nearby town. The person with the flame cutter survived, mainly because the rest of the tank was underground. In order to analyze this incident, it is necessary to calculate how much naphtha had to remain in the tank attached to the wall and lid through surface tension to produce a flammable mixture, if it were to vaporize in the heat of the sun. If it is assumed that only naphtha on the roof of the tank was involved (none on the walls of the tank), then its required thickness, t, can be calculated for a tank of height Hand diameter D. It is assumed that the air density in the tank is 1.2 kg m3, the density of the naphtha was 750 kg m3, and the stoichiometric A/F ratio for a pure hydrocarbon such as naphtha was 15/1, with a lean limit W ¼ 0.4. Precise values are not required for this type of risk analysis. With these assumptions, the thickness of naphtha to just produce a flammable mixture can be shown to be t ¼ 0.000 043  H; for a normal 10 m-high tank this gives t ¼ 0.43 mm, which is typical of the surface tension thickness. In a similar way, it can be shown for dust explosions that a layer of flammable dust less than 1 mm thick can produce an explosion risk if the dust layer is disturbed by a gas flow or shock wave. In a dust manufacturing plant this leads to exacting standards of cleanliness, as any dust that can be seen on surfaces is potentially an explosion risk. The hazard of surface dust layers leads to the phenomenon of secondary dust explosions, where an initial explosion creates a wind ahead of the expanding flame and this disturbs the dusts that is lying in layers on floors, walkways, and pipe surfaces. The explosion then continues to propagate well beyond the original explosion location; in fact, the secondary explosion is often more devastating than the initial explosion. A BLEVE – a boiling liquid expanding vapor explosion – results from the fracture of a liquefied petroleum gas (LPG) storage tank. It is necessary to estimate the diameter of the fireball for the complete loss of the stored LPG mass M. The worst-case scenario, which has been replicated in experiments, is that the LPG spillage of mass M mixes with a stoichiometric amount of air and then encounters an ignition source at the center of the spillage. The hydrocarbon/air mixture then propagates a hemi-spherical

16.8 Liquid Fuel Tank Vapor Space Explosions and the Importance of the Flash Point

flame, which ceases once the limit of the spilled mass has been reached. After this, buoyancy lifts the fireball vertically. The mass of air and fuel for a stoichiometric explosion will be M(1 þ S), where S is the stoichiometric A/Fratio. When this mixture burns, the mass is conserved but the volume expands in proportion to the decrease in burnt gas density. The unburned gas to burnt gas density ratio, at constant pressure, is known as the expansion ratio, Ep, which is proportional to the ratio of burnt to unburned gas temperature. For some hydrocarbons, the Ep-value is 7.49 for 10% methane–air, 8.1 for 4.5% propane–air, and 8.6 for 7.5% ethylene–air. Taking LPG as a pure hydrocarbon with W ¼ 1 of 15/1 A/F ratio, and with the vessel failure being assumed to result in a hemisphere of liquid LPG, the hemi-spherical volume of burnt gases and hence the diameter may be estimated as: Burnt volume ¼ mass=burnt density Mð1 þ SÞ=ðrf Þ ¼

  1 p=6 D3f 2

Hence Df ¼ f½12Mð1 þ SÞ=ðprf Þg0:33 For S ¼ 15; rf ¼ ra =E and E ¼ 8:1 and ra ¼ 1:2 kgm3 Df ¼ 7:44 M 0:33

ð16:5Þ

Experimental results for Df are of this form but with some variation in the constant, depending on the investigators. Christou [33] reported a value for the constant of 6.48, while for large-scale experimental results with M up to 2000 kg, a value of 5.8 was identified [34]. The form of the experimental results was predicted based only on the use of stoichiometry, and the constant in the equation was reasonably predicted. The experimental constant was less than predicted, as not all the spillage had vaporized and the adiabatic expansion ratio was probably not achieved as there would have been some combustion inefficiency as well radiation heat losses. Another reason for the lower constant was that the equivalence ratio on ignition might be richer than stoichiometric, as the only requirement is that sufficient air has mixed to bring the spillage into the flammable range. It may be shown that, if the A/F ratio on ignition was rich – perhaps 10/1 – then this would reduce Ep to approximately 6. The constant in Equation 16.5 would be 5.94, based on adiabatic conditions and 100% evaporation of the LPG, which is very close to the experimental results.

16.8 Liquid Fuel Tank Vapor Space Explosions and the Importance of the Flash Point

A common explosion situation is the vapor space in a fuel tank, either for large-scale storage tanks or the fuel tanks in transport vehicles. Special interest has centered on

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explosions in the vapor space of aircraft fuel tanks, there having been about 24 such incidents since 1970. The most well known is TWA flight 800, to Paris from New York JFK, on 17 July 1996, when the ambient temperature at JFK was 22
C. The Boeing 747-131 blew up over the Atlantic ocean at an altitude of 4200 m. This occurred 11.5 minutes after an 8:19 pm take-off, and all 18 crew and 212 passengers were killed [35]. When the aircraft debris was recovered, an examination revealed the evidence of a vapor space explosion in the 60 m3 center wing tank. More recent explosions of this type have included a Thai Airlines B737 in 2001, and a UK RAF Hercules aircraft in Iraq in 2005. Likewise, the most recent data on the loss of an Air France aircraft flying from Rio de Janeiro to Paris over the south Atlantic ocean in 2009, have suggested the cause to be a fuel tank vapor explosion ignited by a violent thunderstorm. Burning debris seen by other aircraft, together with scorch marks on the recovered debris, supported this proposal. At least two fuel tank explosions in thunderstorms have been reported previously. A lighting strike on the fuel tank vapor release pipe near the wing tip can transmit the charge to produce a high-energy spark in the fuel tank. One notable feature of a fuel tank explosion is that there is no warning; it takes only a second to occur, which allows no time to send any Mayday signals, and this applied to the Air France incident. However, the key feature when investigating these explosions is not the search for the ignition source, but rather the explanation as to how a flammable vapor/air mixture occurred in the fuel tank. When a liquid fuel contained in a tank has air above it, the liquid is not mixed with the air and so its amount is irrelevant. Rather, it is the amount of vapor present that is important, and this depends not only on the temperature of the liquid fuel but also its type. The flash point of a liquid fuel is the temperature at which sufficient vapor is released to form a flammable vapor/air mixture above the liquid, so that when a spark or flame is present there will be a flash explosion. Thus, the flash point measurement is essentially a method of determining the lean flammability limit of a vapor/air mixture, in terms of the liquid temperature required to yield the minimum amount of vapor to produce a lean limit vapor/air mixture above the surface of the liquid. When the vapor is enclosed, as in a fuel tank, and air is admitted through the tank air vent, then this is essentially a closed-vessel explosion. Once the fuel is above its flash point, any spark will cause an explosion that would destroy both the vessel and the aircraft. The flash points of some common flammable liquids that are stored in fuel tanks are listed in Table 16.5. These data show that, as expected, the more volatile hydrocarbons have a lower flashpoint. Compounds present in gasoline have flash points well below zero, while compounds in diesel fuel have flashpoints well above 50
C. The main interest in fuel tank explosions relates to the practical fuels of gasoline, kerosene and diesel, and these have a variable composition and hence a range of flashpoints. The flashpoint equipment can also be used to measure a rich limit temperature as well as a lean limit temperature; for gasoline, it is the rich limit temperature that is closest to ambient (see Table 16.5). However, for gasoline both the rich and lean limit temperatures are well outside of ambient temperatures, apart from areas of the world where temperatures below 25
C occur, where a rich limit fuel tank explosion could occur.

16.8 Liquid Fuel Tank Vapor Space Explosions and the Importance of the Flash Point

One problem with gasoline is that both the European Union and the US have mandated the addition of ethanol as part of greenhouse gas control measures. So, when 85% ethanol is used in the blend (E85), the rich limit is brought within the range of winter ambient temperatures at 5
C, and the fuel tank explosion hazard is much greater than for gasoline (see Table 16.5). Diesel fuel is much less volatile than gasoline, and does not present any possible flammable vapor hazard at any practical ambient temperature, as the fuel would need to be heated to almost 100
C (see Table 16.5). Kerosene, however, lies between the two extremes of gasoline and diesel, neither of which presents any vapor space explosion hazard. For aviation kerosene, the minimum allowed flash point at 1 bar is 38
C; consequently, in areas of the world where the ambient temperatures are above 38
C a vapor space explosion is possible. Indeed, there have been several incidents of aircraft fuel tank explosions on the ground at airports in the tropics. Although, by definition, the standard flash point is measured at 1 bar pressure, in an aircraft the fuel tank vapor space pressure will decrease with altitude. A lower pressure enables more fuel vapor to evaporate, this makes a vapor space explosion possible at lower liquid temperatures. This effect is apparent from the data in Table 16.5, where the flash points of three different kerosenes at different pressures are shown. In the case of the TWA 800 disaster, the explosion occurred at an altitude Table 16.5 Flashpoints of some practical flammable liquids.

Liquid

Reference Pressure Flashpoint Liquid (bar) (
C)

Reference Pressure Flashpoint (bar) (
C)

Propane n-Hexane n-Octane

[15] [15] [15]

1 1 1

102 26 þ 13

Hexadecane

[15]

1

þ 126

Benzene

[36]

1

11

Diesel Diesel Aviation Gasoline Aviation Gasoline Kerosene

Cyclohexane [36]

1

17

Kerosene [30]

0.5

White spirit

[36]

1

þ 38

Kerosene [39]

1

Acetone

[36]

1

18

Kerosene [39]

0.5

Ethanol

[36]

1

þ 15

Kerosene [39]

0.2

E85 – 15/85 gasoline/ ethanol

[37]

1

18 lean

Kerosene [40]

1

Gasoline

[38]

1

þ 5 rich 43

Kerosene [40]

0.5

[30] [30] [39]

1 0.5 1

[39]

0.5

[30]

1

þ 97 þ 82 60 lean 25 rich 65 lean 35 rich þ 47 lean þ 87 rich þ 37 lean þ 70 rich þ 43 lean þ 85 rich þ 32 lean þ 75 rich þ 25 lean þ 52 rich þ 34 lean

þ 83 rich þ 22 lean þ 63 rich

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where the pressure was close to 0.5 bar. However, this would still require a fuel temperature of 37
C if the kerosene in Ref. [30] were to be used. As the ambient temperature was 22
C at the airport, the fuel must have been heated in order to create a flammable mixture. The heat source was found to be the heat rejected by the airconditioning units, which were mounted directly below the central fuel tank, and subsequent tests conducted by Boeing showed that these could indeed create liquid kerosene temperatures in excess of 100
C. In order to solve this problem, Boeing improved the thermal insulation between the air-conditioning units and the center wing tank.

16.9 Burning Velocity, Flame Speeds, Explosion-Induced Wind, and Closed-Vessel Pressure Rises

When a mixture of gas, vapor, mist or dust is in the flammable range, and is ignited, the properties of the flame are characterized by the laminar burning velocity, Su. This is the velocity of a near one-dimensional (large explosion diameter so that flame curvature effects are small) flame relative to the unburned gas. In an explosion situation, the unburned gas is initially stationary; however, the hot burned gas behind the flame front causes an expansion which pushes any unburned gas away from the flame. This is referred to as the explosion-induced wind. The velocity of the flame in an explosion relative to an observer is the flame speed, Ss, and the explosion-induced wind ahead of the flame is Sg. These terms are related by the following equations: Su ¼ Ss -Sg

ð16:6Þ

Ss ¼ Ep Su

ð16:7Þ

where Ep is the expansion ratio for constant-pressure explosions (gas leaks and flammable liquid spillages in the open, with no confinement). In the case of methane–air, for example, Su is 0.4 m s1 and Ep is 7.6, so that Ss is 3 m s1 and Sg is 2.6 m s1 [42]. Equations 16.6 and 16.7 may be combined to give: Sg ¼ ½1ð1=Ep ÞSs

ð16:8Þ

For adiabatic stoichiometric methane–air explosions, this gives Sg ¼ 0.87 Ss. This equation is also applicable if the flame speed is accelerated by turbulence generated by the interaction of the explosion-induced wind with obstacles. Andrews and Phylaktou [41] have shown that, for very high-speed explosions after the interaction with obstacles in a tube configuration, that the constant in Equation 16.8 is reduced slightly to 0.80, due to heat losses, but still applies for flame speeds up to the transition to detonation region. Thus explosions can induce very high-velocity winds ahead of the flame, and these are an important feature of explosions in congested volumes. If the flammable mixture occurs in a confined space, then the explosion will cause the pressure to rise; for adiabatic conditions (which only occur in spherical vessel

16.10 An Overview of Explosion-Protection Measures

explosions), the peak pressure, Pm, is related to the initial pressure, Pi, by Equation 16.9: Pm =Pi ¼ Ev

ð16:9Þ

where Ev is the expansion ratio at constant volume. This is greater than Ep, as the flame temperature at constant volume is greater than at constant pressure, due to the differences in the specific heats, Cp and Cv. This pressure ratio can be calculated for adiabatic explosions, and is 8.9 for 10% methane–air and 9.6 for 4.5% propane–air [4]. Some dust explosions may have considerably higher pressure ratios, for example 12 and 17.5 for aluminum and magnesium dust explosions, respectively [9]. It is clear that if an explosion hazard occurs in a confined space, and the mixture cannot be diluted outside the flammable range, then either the vessel must be able to withstand these very high pressure ratios or an explosion-protection measure must be used. It is rarely the case that full pressure containment is used, as the cost of including pressure-resistance characteristics in every vessel that might encounter an explosion risk would be prohibitive.

16.10 An Overview of Explosion-Protection Measures

With regards to the two explosion-prevention measures already discussed – ventilation or dilution/inerting – the aim of both is to ensure that no flammable mixture occurs in the confinement, so that no explosion can occur. However, if it is essential that the process to be protected employs a flammable atmosphere, such as a gas-fired oven or a coal mill, then these preventive measures cannot be employed and explosion-protection measures must be installed. For this, two main options exist, namely explosion suppression and explosion venting. Explosion suppression is essentially a very fast-acting, dry-powder fire extinguisher. In this case, a pressure transducer detects the initial pressure rise associated with an explosion such that, at typically 50–100 mbar, the pressure signal is used to open a fast-acting valve through which high-pressure nitrogen (typically 60 bar) drives the dry-powder extinguishing agent [8, 9, 43, 44]. As the vessel volume increases, the mass of suppressant must be increased, and this is achieved by either increasing the number of suppressants or increasing their size. Currently, three standard sizes of suppressant injection systems are available, by agreement between the manufacturers: 4 kg or 5 liter; 16 kg or 20 liter; and 35 kg or 50 liter. The typical suppressants used include sodium bicarbonate or mono-ammonium phosphate (MAP), and there are several commercial versions of these which differ in the fineness of the powder and the flow agents added. Explosion-suppression protection systems must be designed by the suppressant manufacturers, and tend to be more expensive than the alternative explosionprotection measure of venting. The main advantage, however, is that none of the material in the vessel is released; hence, this is the method of choice when dealing

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with toxic material explosion protection, or where an external vent flame cannot possibly be accommodated at the location of the explosion risk.

16.11 Vent Design for Explosion Protection for Compact Vessels

The most common explosion-protection device is venting, where an opening is placed in the vessel walls of area Av that fails at a static burst pressure Pstat, which is well below the failure pressure of the vessel to be protected. Experimentally based design equations are used to predict the reduced vessel peak pressure, Pred (reduced from the adiabatic closed vessel pressure rise). Pred must be less than the vessel strength for satisfactory protection. The main problem with this protection method is that the expansion of the burnt gases occurs outside the vent, in a very large jet flame. This means that vessels in the open can be protected in this way, where people can be excluded from the area of the vent jet flame. The sizes of these jet flames are large and are roughly (Ev  1) times the vessel volume, V. This is about seven times the vessel volume for hydrocarbon–air explosions and HCO-type dust explosions. Experimentally based correlations for the vent flame length [4, 45, 46] give the vent flame length, Lv, as Lv ¼ const. V0.33, where the constant is 8 for vertically discharging vents, and 10 for horizontal discharging vents. The maximum flame width is 1.3 (10V)0.33. If a vent is used to protect a vessel in an enclosure where people work, then a vertical discharge from the vent can be acceptable for high-ceiling factories (most gasfired ovens are protected in this way). However, in many cases workers must be protected by attaching a discharge duct to the vent, taking the discharge gases to a safe discharge point. This compromises the vessel being protected, however, and if the vent duct was the same size as the vent then there would be a very large increase in the overpressure, of the order of 1 bar [4, 8]. Consequently, the vent area must be increased from that required for a free vent and the discharge duct must be of at least equal, but preferably greater, area [4, 8]. The design guidance on the design of vents in Europe [45, 46] and the US [4] is based on the original work of Bartknecht [8], whose equations are based solely on his data. The design equations are not correlations of all experimental data, and include no safety margin on his experimental results. It is not generally realized that the currently used vent design equations are not even a correlation of Bartknecht’s own experimental results, but are the venting data for a 10 m3 vessel for propane and methane, and for a 1 m3 vessel for hydrogen. For propane, Bartknecht’s data [8] for volumes smaller and greater than 10 m3 have lower values of Pred [47]. For small volumes, the design equations will overpredict the required Av to achieve a desired Pred, when compared with other experimental results [47]. The vent design equations apply only to compact vessels with a length to diameter or minimum width ratio, L/D, of <2. This vessel shape limitation occurs because it is only for these compact vessels that it is realistic to assume that the flame spreads from a spark as a spherical flame during the venting process. Once the vessel L/D > 2, the flame touches the wall before the pressure has risen significantly, and explosion

16.11 Vent Design for Explosion Protection for Compact Vessels

predictions are then more difficult. Further investigations are required on vented explosions so that current design procedures can be improved or further validated. For compact vessels, the vent design equations of Bartknecht [8] are given by Equation 16.10 for gases: 0:5722 0:5817 Av ¼ ½ð0:1265 logKG 0:0567ÞPred þ 0:1754Pred ðPstat 0:1ÞV 2=3

ð16:10Þ

The first terms is valid for Pstat ¼ 100 mbar, while the second term gives an additional vent area when higher Pstat are used, which has had very limited validation [8]. The form of the equations for Pstat ¼ 100 mbar will be used to discuss the underlying physics of the venting process. Equation 16.10 for Pstat ¼ 100 mbar is more conventionally expressed in terms of the vent coefficient, Kava, which is V2/3/Av: 0:5817 1=kv ¼ ð0:1265 log KG 0:0567ÞPred

ð16:11Þ

Bartknecht [8] also expressed his design method as Equation 16.12 for Pred as a function of Av: Pred ¼ ð0:0778 log KG 0:0932ÞA1:719 V 1:146 bar v

ð16:12Þ

The mixture reactivity parameter in Equations 16.10–16.12 is KG, which is the maximum rate of pressure rise in a closed spherical vessel of 5 liter volume times the cube root of the vessel volume, KG ¼ dP/dtmax.V1/3 bar ms1. For dust explosions, the equivalent parameter in the 1 m3 ISO dust test equipment is the Kst parameter; this is defined in the same way as KG, but applied to the turbulent dust air mixtures in the test vessels. This reactivity parameter is rather empirical and can be made more general by normalizing to the initial pressure. This then allows the reactivity at different pressures to be compared, such that the units are then a velocity in the same way that the more conventional reactivity parameter, Su, is a velocity. KG and Kst would then be defined as KG ¼ d(P/Pi)m /dt.V1/3 m s1. This is required to properly present explosion data at elevated initial pressures and temperature, which has not been done in the literature [8, 9]. Bartknecht [8] provided experimental vented explosion data for methane, propane, coal-gas and hydrogen in vessels of different volume up to 60 m3. For these gases, it is preferable to use the actual constant that was developed in the experiments. The experimental data was plotted [8] as Equation 16.13: b Av ¼ a Pred

ð16:13Þ

The experimentally derived values of the constants a and b are shown in Table 16.6, together with values of KG from experiments in a 5 liter spherical vessel [8]. The alternative gas reactivity parameter, the laminar burning velocity, Su, is also listed in Table 16.6. The exponent b is shown in Table 16.6 to be similar for all the four fuels, and an average value of 0.5817 was used in Equation 16.11. However, the reactivity term “a” is clearly a function of KG, and a plot of “a” versus log KG gave a linear

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Table 16.6 Venting and reactivity constants for the four gases used by Bartknecht [8].

Gas–air

Methane Propane Coal gas Hydrogen

KG [8] (bar ms1 5 l)

a

b

55 100 130 550

0.164 0.200 0.212 0.290

0.5729 0.5797 0.5900 0.5850

KG Su [48] Ep Ev [4] Pm/Pi KG [4] KG (m s1) (Equation 16.14) (1 m3) 0.4 0.45 – 3.1

7.5 8.1 – 7.9

8.9 9.6 – 7.8

8.9 9.6 – 7.8

75 99 – 526

64 96 655

67 – – 693

correlation which is the reactivity term in Equation 16.11. It is important that when using Equation 16.11 in vent design that no other values of KG are used than those in Table 16.6; otherwise, the overpressure will not be that measured by Bartknecht [8]. For venting of the four gases in Table 16.6 it is preferable to use Equation 16.13 with the constants in Table 16.6 rather than Equations 16.10 or 16.11. Prior to the use by Bartknecht [8] of KG as the reactivity parameter for gaseous explosions, the laminar burning velocity, Su, was used [49], and indeed it may be shown that the two methods are directly related. For a spherical vessel with central ignition, the only assumption needed is that the rate of flame propagation is constant throughout the radial flame propagation. This is not strictly correct, as the increase in pressure and temperature (caused by the gases ahead of the flame being compressed by the flame propagation) changes the laminar burning velocity. However, the pressure effect decreases Su (Su  P0.5) and the temperature effect increases Su (Su  T2). The net result is that there is only a 20% change in Su throughout the flame propagation distance [50]. For flame propagation in a sphere of diameter D, there is minimal pressure rise in the first half of the flame travel. This may be shown by considering the fraction of the initial mass burnt when the flame is half-way across the radius of the vessel. The pressure rise in a closed vessel explosion is directly proportional to the fraction of the initial mass that has been burnt. For a sphere, the volume of burnt gases when the flame is half-way to the wall is 1/8 of the spherical volume. This volume is burned gases of density 1/7 of the unburned gases. Thus, when the flame is half-way across the radius there is only 1/56 of the initial mass burnt, or approximately 2%. Thus, 98% of the mass burn and 98% of the adiabatic pressure rise occurs in the second half of the flame travel. The maximum rate of pressure rise is then 98% of (Pm  Pi) divided by the time for the flame to propagate half-way across the radius at the assumed constant propagation rate. An expression for KG normalized pressure rise form, Pm/Pi, can now be derived: KG ¼ V 1=3 ½0:98 Pm =Pi 1=fðD=4Þ=SuEv g ¼ 3:16ðPm =Pi 1ÞSu Ev ms1 ð16:14Þ

The predicted KG based on Equation 16.14 is shown in Table 16.6, and these agree well with the results of Bartknecht for propane and hydrogen, but predict a higher value for methane. The agreement depends on the value taken for Su, as there is no agreed method for its measurement and the value is highly dependent on the

16.11 Vent Design for Explosion Protection for Compact Vessels

measurement method [48]. Also other experimental measurements in larger explosion vessels, including those of the present authors in a 1 m3 vessel, show significantly different results than those of Bartknecht (as shown in Table 16.6). However, to use the Bartknecht design equations for vented explosions the value of KG must be the same as that used by Bartknecht. If more realistic values of KG are desired to be used [4], then the original vent design equations for each gas (as shown in Table 16.6) must be recorrelated with new values for KG. It is not legitimate to alter the values for KG in Equation 16.10 as this will not produce the values of Pred that Bartknecht measured for his four standard gases. For gases where a value of KG may not be known but a value of Su is known, Equation 16.14 shows that the scaling should be KG1/KG2 ¼ (Su1/Su2)(Pm1/Pm2), which is the procedure adopted in the National Fire Protection Agency (NFPA) 68 [4]. In this equation, Pm is the gauge pressure, whereas in Equation 16.14 Pm is the absolute pressure, so the two equations are in agreement. For hydrocarbons and hydrogen there is no large variation in the values of Pm/Pi, and so the vent design equation (Equation 16.10) does not include the Pm/Pi value. However, for dust explosions Pm/Pi varies between 6 and 15, due to the large differences in calorific values of dusts. This means that the value of Pm/Pi must be included in the vent design correlation (this is shown later). There is a significant problem with the values of “a” in Table 16.6, as the value for hydrogen is only 1.8-fold that for methane, and the value for propane is 1.2-fold that for methane. The ratios of the reactivity constants should be in proportion to those for Su, and this is the case for methane and propane but not for hydrogen, where a ratio of 7.8 would be expected. Further studies on the validation of the vent design procedures for hydrogen is required, as Equation 16.10 is based on very limited data. At Pstat ¼ 100 mbar, Bartknecht carried out hydrogen explosions for only two vent sizes. Moreover, the hydrogen explosions were carried out in a 1 m3 vessel, whereas all the data for methane and propane were obtained in a 10 m3 vessel [8]. As the overpressures for methane and propane were substantially lower in smaller explosion vessels, this explains why there is no correlation in the values of “a” in Equation 16.13 with the laminar burning velocity. As there is ample other data for vented explosions using hydrogen, there is an urgent need to look at the validity of Equation 16.10 for hydrogen explosions. The use of hydrogen as a greenhouse gas mitigation measure, makes it more urgent that the design method for vented explosions is based on a wider set of data. For dust explosions, Bartknecht [8] provided an expression for Av as a function of Pred, Pm, Kst, and V for Pstat ¼ 100 mbar. All pressures are in bar. 0:569 Av ¼ 3:264  105 Pred Pm Kst V 0:753

ð16:15Þ

Equation 16.15 is valid for Pred < 2 bar and V < 1000 m3 and Kst < 800, and for all dusts. However, Bartknecht [8] showed that if dusts where Pm is >9 bar are excluded (which is mainly metal dust explosions), then the vent design equation becomes Equation 16.16, which is more compatible with that for gas explosion venting in Equation 16.11.

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0:5071 1=Kv ¼ 6:02  105 Pred Pm Kst

ð16:16Þ

The reason for the inclusion of Pm has been discussed above, and is due to the much wider variation of Pm for dusts than for gases. Thus, the Pm for gases is included in the constant of Equation 16.10. The form of the gas and dust venting equations for Pstat ¼ 100 mbar is given in Equation 16.17. n 1=Kv ¼ constant  Reactivity Parameter=Pred

ð16:17Þ

The value of n is given as 0.5071 in Equation 16.16, as 0.569 in Equation 16.15, and as 0.5817 in Equations 16.10 and 16.11. However, it will be shown that this exponent should depend on the flow equation for unburned gas through an orifice (the vent). For low overpressures, incompressible flow occurs and n should be 0.5, whilst for pressure greater than 0.9 bar a sonic flow occurs and n should be 1. The experimental value of n being slightly greater than 0.5 merely indicates that most of the experimental venting results of Bartknecht were for incompressible flow and low vent overpressures. The reactivity parameter used in the Bartknecht design equations is KG or Kst, but Equation 16.14 shows that this could easily be replaced by Su with appropriate changes to the constant. A form of Equation 16.17 with Su as the reactivity parameter was developed by Swift [51] and adopted as an alternative vent design procedure for some circumstances in NFPA 68 [4]. The physics behind the form of Equation 16.17 is based on the mass flow of unburned gas, displaced by the explosion, being forced to flow through the open vent which acts as an orifice plate flow. There is then a pressure loss across the orifice due to the flow of unburned gas through the orifice, and this is the cause of the overpressure Pred. The flow of unburned gas displaced by the spherical explosion is the explosion wind, Sg, times the area of the flame. However, the area of the flame increases as the flame develops and hence a simplification is required. This was provided by Runes [52], who suggested the limiting condition that the flame area could not be greater than the internal surface area of the vessel, As. This then gives a worst-case design procedure as the flame area has been taken as larger than in reality, and this should lead to an overprediction of the overpressure and hence a safe prediction procedure. In this analysis, the role of pressure waves interacting with the flame and accelerating it have been ignored. The reason for this is that, in compact vessels with the low vent burst pressure of 100 mbar, the pressure waves created when the vent bursts are small. Pressure waves generated by vent bursting are more important when Pstat is 0.5 bar or more. Longitudinal pressure waves may also occur for vessels with L/D > 2, particularly for side vents. If vent pipes are attached to vents from compact vessels, then there will also be a strong generation of pressure waves, especially if the vent pipe has a bend. None of this complexity needs to be included for compact vessel free vents with a low Pstat, which is the situation for which the design equations apply. The maximum unburned gas mass flow displaced by the flame is equated to the vent orifice plate pressure loss in Equation 16.18. This is shown for incompressible

16.11 Vent Design for Explosion Protection for Compact Vessels

flow through the vent, which is valid for low values of Pred, but for a higher Pred the compressible flow equation should be used and for sonic flow at the vent (Pred > 0.9 bar) the sonic flow orifice equation should be used. For sonic flow at the vent, the mass flow through the vent is directly proportional to Pred rather than the square root relationship in Equation 16.18 for incompressible flow. As Su ðEp 1Þ ru ¼ Cd Av ð2ru Pred Þ0:5

ð16:18Þ

0:5 0:5 Av =As ¼ ðSu ru ÞðEp 1Þ=½Cd ð2ru Pred Þ0:5  ¼ r0:5 u Su ðEp 1Þ=½ðCd 2 ÞPred 

ð16:19Þ 0:5 Av =As ¼ C1 Su ðEp 1ÞPred

ð16:20Þ

3 0:5 where C1 ¼ ½r0:5 u =ðCd 2 Þ ¼ 1:270 for ru ¼ 1.2 kg m , Cd ¼ 0.61 Equations for vent design involving the vessel surface area were also derived by Bradley and Mitcheson [53, 54]; these authors also derived Equation 16.21 for subsonic venting and Equation 16.22 for sonic venting.

A=S ¼ ½12:3=Pred 0:5 A=S ¼ ½2:4=Pred 1:43

ð16:21Þ for Pred > 1 bar

ð16:22Þ

The ratio A/S has been referred to as the Bradley number, B, by Molkov [54]. Here, A is the ratio CdAv/As and S is the ratio Su(Ep  1)/av, where av is the velocity of sound at the vent, which is an unnecessary term introduced to produce a dimensionless term. It may be shown that the form of Equation 16.21 is the same as Equation 16.20. 0:5 Av =As ¼ ð12:3Þ0:5 =Cd av Su ðEp 1ÞPred

With av ¼ 343 m s1 at 293 K and Cd ¼ 0.61, this becomes: 0:5 Av =As ¼ 0:0168 Su ðEp 1ÞPred

ð16:23Þ

0:5 Equation 16.23 can be simplified to Av/As ¼ C1Su (Ep  1) Pred , as in Equation 16.20, where C1 ¼ 0.127. The units of pressure are atmospheres in the Bradley and Mitcheson constants, and Pa in Equation 16.20. Conversion into Pa changes the value for C1 by (1.03  105)0.5 or 321, so that C1 is 5.39. This is larger than the value of C1 of 1.27 derived above in Equation 16.20 by a factor of 4.24. This was due to the use of a turbulence factor of 5 enhancement of Su by Bradley and Mitcheson to give an agreement of Equation 16.23 and the experimental data. 0:5 is It should be noted that C1 (Ep  1)Su has units of P0.5, so that C1Su (Ep  1) Pred dimensionless, and there is no need for the artificial dimensionless parameter S in the Bradley and Mitcheson equation (Equation 16.21). Swift [51] also has followed a similar approach, and his results when converted from his units of kPa to Pa gives a value for the constant for methane of 11.7. The Bradley and Mitcheson equivalent value for C1Su(Ep  1) for Su ¼ 0.4 m s1 and Ep ¼ 7.4 is 13.8, which is in good agreement with the results of Swift. Swift also had to insert a turbulence factor of 5

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increase in Su to achieve agreement with the experimental venting data. For methane, Equation 16.20 for the present approach gives a value for the constant of 3.25 with no turbulence factor; hence, for agreement with Bradley and Mitcheson a turbulence factor of 4.2 would be required, or 3.6 for agreement with Swift. If the results of Bartknecht in Equation 16.13 for methane are converted into the same form as Equation 16.20, then the constant is 10.7 which is lower than the Bradley and Mitcheson value and lower than the Swift value, Bartknecht used no turbulence factor and directly correlated his experimental results for a 10 m3 vessel. The present approach would need a turbulence factor of only 3.3 to agree with the Bartknecht results. This is not necessarily due to turbulence, but could be due to flame self-acceleration in the large-diameter 10 m3 vessel used by Bartknecht [8]. No effect of flame self-acceleration has been included in Equation 16.20. Molkov [55] has investigated turbulence factors for each set of experimental venting data in the literature, and produced a generalized venting correlation, incorporating these empirical turbulence factors for each experiment. The turbulence or flame self-acceleration created in a vented explosion is clearly a problem area in vent design. This becomes more complex if the vessel has obstacles that interact with the explosion-induced wind to create additional turbulence to that created by the vent flow. There is no satisfactory design methodology for vent design in these circumstances, and further studies are required in this area. Equations 16.19–16.23 can be converted into one involving V2/3 and then Kv by the simple relationship between the vessel surface area and its volume: As ¼ C2 V 2=3

ð16:24Þ

where C2 is 4.84 for a sphere, 6 for a cube, and 5.54 for a cylinder, with L/D ¼ 1 and 5.81 for L/D ¼ 2. If Equations 16.19 and 16.20 are combined, then the form of Equation 16.17 is produced: 0:5 1=Kv ¼ C1 C2 Su ðEp 1Þ Pred

This can be converted into a vent design equation using the relationship between Su and KG in Equation 16.14, and thus produce an equation with the form of Equation 16.11 and 16.16. The main difference in Equation 16.20 and in the design equation (16.11) of Bartknecht is the exponent for Pred, which is  0.58 in Equation 16.11 instead of 0.5 in Equation 16.20. This difference is due to the inclusion of experimental data by Bartknecht that is in the sonic flow regime, where the pressure exponent is 1 rather than 0.5. When Bartknecht’s data are inspected, they do not agree with the correlation above Pred ¼ 0.5 bar, as compressible and sonic flow regimes occur for a higher Pred. It is clear from the physics of the venting process that the overpressure is the pressure loss created by the flow of unburned gas through the vent, with the vent burst pressure effect being an additional overpressure. The current design procedures must be modified to take into account the vent flow regime changes as Pred increases. Bartknecht’s own data show the overprediction of the experimental data for Pred > 0.5 bar, and the current use of Equations 16.10, 16.11, 16.15 and 16.16 in

16.12 Vent Design for Long Vessels with L/D >2

design guides as valid up to 2 bar is not supported by the experimental data of Bartknecht [8] and others [47].

16.12 Vent Design for Long Vessels with L/D >2

A very limited and incomplete set of experimental results is available [4, 8, 45, 46, 56, 57] for vented explosions where the L/D is >2. However, the key result is embedded in the results of Bartknecht [8, 58], and this shows that a greater vent area for the same volume is required for L/D > 2. Bartknecht provided Equation 16.25 for the venting of vessels with L/D ¼ 2–5 for gas explosion venting, and Equation 16.26 for dust explosion venting. These equations express the results as the increase in vent area, DAv, in addition to the vent area, Av, according to Equations 16.10, 16.11, 16.15 and 16.16 for compact vessels of the same volume. These equations have been adopted in NFPA 68 [4]. DAv ¼ Av KG ðL=D2Þ=750

ð16:25Þ

DAv ¼ Av ð4:305 Pred þ 0:758ÞlogL=D

ð16:26Þ

The significant influence of L/D can be seen from Equation 16.25. For an L/D of 5 for methane with KG ¼ 55, this predicts that the vent area needs to be 66% greater than that for the same volume with L/D < 2. The reason for the higher vent area is that, with end ignition opposite the vent, there is an accelerated flame propagation towards the vent, which in turn produces a higher unburned gas flow through the vent and a higher pressure loss [59]. Ferrara et al. [60] have shown for explosions in a cylindrical vessel with an L/D of 2, that end-wall ignition opposite the vent has a higher overpressure than for central ignition, which has conventionally been the ignition position in all current vented data. This is unfortunate, as the ATEX requirements are that the worst-case explosion condition is evaluated, and for venting explosions this is not central ignition. Yet, all of the data in Bartknecht’s vented experiments were for central ignition. The largest data set for explosions in vessels with L/D > 5 is that of Rasbash and Rogowski [61, 62]. These authors investigated propane–air and pentane–air explosions in large L/D explosion vessels, from L/D ¼ 6–48, vented at the end of the tube with ignition at the other end. For an end vent area the same as the duct area (Kv ¼ 1), the overpressure was given by Equation 16.27, while for an end vent area less than the pipe area the overpressure was given by Equation 16.28. All of these studies were carried out with open vents. Pred ¼ 4:83 L=D mbar

ð16:27Þ

Pred ¼ 121 Kv mbar

ð16:28Þ

Equation 16.28 was valid for 2 < Kv < 32 and for 6 < L/D < 30.

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Phylaktou and Andrews [63] have analyzed the physics of flame propagation in long vessels using their experiments with closed large L/D vessels. This was used to produce an equation for end vented explosion in large L/D vessel that correlated with the results of Rasbash and Rogowski [61, 62], as well as their own experiments for methane, propane, ethylene, and hydrogen explosions. The equation that was derived by analysis of the physics of flame propagation is given in Equation 16.29. Pred ¼ rðKv =Cd ÞðL=DÞ0:5 S2u Ep ðEp 1Þ mbar

ð16:29Þ

For methane–air with Su ¼ 0.4 m s1, Ep ¼ 7.5, r ¼ 1.2 kg m3, and the vent discharge coefficient, Cd ¼ 0.61, Equation 16.29 reduces to Equation 16.30. Pred ¼ 96 Kv ðL=DÞ0:5 S2u mbar ¼ 15:3 Kv ðL=DÞ0:5

for methane

ð16:30Þ

Equation 16.29 showed a good correlation for the results of Rasbash and Rogowski [61, 62], in spite of their correlation in Equation 16.28 having no L/D term. A large body of additional vented data with large variations of Su was also shown to agree with Equation 16.29. Tite et al. [57] have investigated square ducts with L/D ¼ 3–15 and with Kv ¼ 1–4.4 for methane–air explosions. The square ducts had 0.61 and 0.92 m sides. The ignition was at the end wall, and end-venting and combinations of end- and side-wall-venting were investigated, but the precise venting geometries were not given. These authors correlated their results by Equation 16.31: Pred ¼ Pstat þ ½23 Kv wS2u ðL=DÞ1=3 =V 1=3 mbar

ð16:31Þ

where w is the weight per unit area of the vent cover (in kg m2). Equation 16.31 has some similarities with Equation 16.29; notably, there is an S2u dependence of Pred and a linear dependence on Kv, and there is also a difference in the L/D exponent. The data scatter for Equation 16.31 was very large, as the results clearly depended on where the vents were placed relative to the spark; moreover, as the vent locations were not indicated, their effects could not be isolated. This sensitivity was also investigated by Alexiou et al. [59], who showed that a side vent close to the spark gave a much lower overpressure than did a side vent further from the spark. Equation 16.31 has been adopted in the European vent design standard [45] for gases, which does not use the Bartknecht Equation 16.21, whereas NFPA 68 does [4]. Equation 16.31 is recommended for elongated enclosures with end and side vents. The uncertainty in vent design for large L/D enclosures is apparent in the European standard [45] where, in addition to Equation 16.31, Equations 16.32–16.34 are also given and the user is advised to design for whichever of the five equations gives the highest overpressure. Equations 16.31–16.33 are for venting at both ends of the vessel, or where side vents are close to the ends, while Equations 16.32 and 16.33 are for where side vents are spaced along the tube. Equations 16.32 and 16.33 derive from the studies of Tite et al. [57], and are a subset of the more general Equation 16.25. Equations 16.34 and 16.35 are from the studies of Pritchard et al. [53]. Pred ¼ 15 d Kv

for Pstat < 60 mbar for methane

ð16:32Þ

16.13 Conclusions

Pred ¼ 15 d Kv þ 150

for Pstat > 60 mbar for methane

ð16:33Þ

Pred ¼ Pstat þ 70 d Kv mbar;

for methane

ð16:34Þ

Pred ¼ Pstat þ 85 d Kv mbar;

for propane

ð16:35Þ

In the above equations, “d” is the maximum X/D between the ignition source and the vent, where X is the distance from the ignition point. The weakness in the use of d is that, normally, the ignition point is not known in any risk analysis. In practice X/D will be the distance between the end wall and the first side vent. For end venting (X ¼ L), Equation 16.32 is similar to Equation 16.30, with nearly the same constant, but the dependence on X/D is square root in Equation 16.30. In all of these equations for large L/D explosions, Kv is not the usual definition of V2/3/Av, but is simply the cross-sectional area of the vessel Ac divided by the vent area, Av. This is due to the geometry of the flame propagation, which is akin to a piston moving down the long vessel, giving rise to a strong axial wind ahead of the flame that moves through the vent and produces the large overpressure. With side vents, this unburned gas flow is bled off, so that the flow through the end vents is reduced and the overpressure falls. One feature of all these long vessel vented explosions is that they are associated with strong pressure oscillations, particularly with side venting [56, 59]. It is clear from the above that there is much confusion in the correlations for vent design for vessels with L/D > 2, and further studies are recommended in this area, with new experimental investigations.

16.13 Conclusions

The following points have been raised and discussed in this chapter: . .

.

.

Stoichiometric A/F ratios, on a mass basis, should be used in explosion safety evaluations, as they apply to gases, vapors, mist, and dust explosions. Lean flammability limits should be expressed on an equivalence ratio basis, which shows that hydrocarbon gases, vapors, mist, and dusts have the same lean limit equivalence ratio of close to W ¼ 0.45 at ambient conditions. Recent legislated methods for gas flammability measurements using a 100 mm flame travel criteria give about 15% leaner flammability limits than for the US Bureau of Mines method, where 1.5 m of flame travel is the criterion. The leaner limits under European flammability measurement procedures agree with measurements on hydrocarbon dusts for lean flammability in equivalence ratio terms. Liquid alcohol HCO vapors behave in a similar way to those of hydrocarbons, with a lean limit close to W ¼ 0.45. However, HCO dusts behave differently and have much leaner flammability limits than hydrocarbons, with WLL ¼ 0.25. The reason for this is that these dusts do not form a gas of the same composition as the solid material when they are heated, whereas liquid alcohol vapor has the same

j409

j 16 Explosion Safety

410

. .

.

.

.

.

composition as the liquid. Solid HCO dusts decompose on heating to yield hydrocarbon gases, and these propagate the flame in HCO dust explosions. The result is that HCO dusts have the same lean limit (in g m3 units) as hydrocarbon gases, but the WLL is much leaner than for hydrocarbons. Most metal dusts are extremely reactive, and have a lean flammability that is WLL ¼ 0.2. There is a dearth of explosion information on biomass dusts, there is an urgent need for additional data. Biomass plants with pulverized biomass are being constructed for the large-scale addition of powdered biomass to pulverized coal flames. Yet, there is a clear lack of data relating to the explosion hazards of such HCO dusts, particularly under milling conditions. Lean flammability limits are highly dependent on the temperature in a process, and may be predicted using the critical limit flame temperature of 1400 K from the European lean limit measurement procedures for hydrocarbons. This also enables the level of inert gas required to prevent a stoichiometric mixture from exploding to be predicted, which in turn enables the critical oxygen concentration for inerting to be predicted. Changes to the European standard on flammability measurements mean that inert gas levels will be greater than under previous guidance, which used the US Bureau of Mines data. The limiting oxygen concentration for inerting will also be at lower levels. Ventilation, or the dilution of mixtures, represent the main methods for use in explosion prevention. If the lean limit is expressed in A/F ratios by mass, then guidance for ventilation of flammable vapors, which requires the ventilation to achieve 14 of the lean limit, can be applied to any gas, vapor, mist, or dust. The ventilation rate should be 150 w kg h1, where w is the mass production of vapor or release of gases, mists, or dusts. Ventilation rates are highest for hydrocarbon vapors, and could be lower for alcohols and HCO dusts. However, it is recommended that if the hydrocarbon level venting is designed for, then the system will be safe for any flammable substances. Liquid fuels in storage tanks represent a particular explosion hazard, and this has resulted in several complete aircraft losses due to fuel tank vapor space explosions. In fuel storage, gasoline is too volatile and the vapor space lies outside the flammable range for all practical conditions. Diesel is not sufficiently volatile to form a vapor space explosion hazard at any practical condition. Kerosene is the only common fuel that can have a vapor space explosion at the ambient temperatures that occur in the tropics. When aircraft take off and climb, the pressure in the vapor space falls such that more fuel vapor is evaporated and the flash point decreases. Yet, for the TWA Flight 800 from New York the ambient temperature was 22
C, and the only way a vapor space explosion could have occurred was if the fuel was heated. The heat source proved to be the airconditioning units mounted below the central wing tank. Venting of explosions is the most cost-effective explosion protection measure. The vent design procedures used in US and European guidance is based on the results of Bartknecht [8]. These have some unusual features, in that the recommended design equation for gases is not a correlation of all vent data, but rather represents

References

the vent data for a 10 m3 explosion test vessel (apart from hydrogen, when a 1 m3 vessel was used). The hydrogen design data do not appear to take into account the much higher reactivity of hydrogen, and further studies are required in this area. The form of the vent design equation is shown to be predicted from the assumption that the overpressure arises from the displacement of unburned gas through the vent by the spherically expanding flame. The overpressure is then the pressure loss due to this flow through the vent. This approach shows that vent design procedures must be modified to take into account the changing flow regimes at the vent as Pred increases. For Pred > 0.3 bar, the flow at the vent is compressible, and for Pred > 0.9 bar is sonic. These flow regime changes are not accounted for in the current design procedures. The design procedures for venting of vessels with L/D > 2 are not reliable, and additional studies, including a more experimental approach, are required. Four equations for one gas are given in the European standards with the requirement to design for whichever gives the highest vent area, which indicates the uncertainty in current procedures. The most important influence of L/D is that larger vent areas are required for the same volume compared to a compact (L/D < 2) vessel. This is embodied in the Bartknecht equations for L/D > 2 and <5, and is not as apparent in the European standard.

.

References 1 European Parliament and Council (1994)

2

3

4

5

6

The Explosive Atmosphere. Directive (ATEX), 94/9/EC, 23 March. European Parliament and Council (1999) Minimum Requirements for Improving the Safety and Health of Workers Potentially at Risk from Explosive Atmospheres. Directive 1999/92/EC, 16 December. The UK Health and Safety Executive (HSE) (2002) The Dangerous Substances and Explosive Atmospheres Regulations and Approved Code of Practice, HSE Books. National Fire Protection Association (NFPA) (2007) Guide for Venting of Deflagrations, NFPA 68. New Zealand Government (2001) Hazardous Substances (Classes 1 to 5 controls) Regulations, New Zealand SR 2001/116. British Petroleum (2005) Fatal Accident Investigation Report-Isomerisation Unit Explosion Interim Report, Texas City, Texas, USA, issued 12 May.

7 The Buncefield Investigation: Third

8

9 10 11 12 13

14

15

progress report, 9 May 2006. Available at: http://www.buncefieldinvestigation.gov. uk. Bartknecht, W. (1993) Explosionsschultz, Grundlagen und Anwendung, Springer Verlag. Eckhoff, R.K. (2003) Dust Explosions in the Process Industries, Gulf/Elsevier. Tillman, D.A., Rossi, A.J., and Kitto, W.D. (1981) Wood Combustion, Academic Press. Macrae, J. (1966) An Introduction to the Study of Fuel, Elsevier, p. 20. Harker, J.H. and Backhurst, J.R. (1981) Fuel and Energy, Academic Press, p. 29. van Loo, S. and Koppejan, J. (2008) The Handbook of Biomass Combustion and CoFiring, Earthscan. Coward, H.F. and Jones, G.W. (1952) Limits of Flammability of Gases and Vapors, US Bureau of Mines, Bulletin 503. Zabetakis, M.G. (1965) Limits of Flammability of Gases and Vapors, US Bureau of Mines Bulletin 627.

j411

j 16 Explosion Safety

412

16 European Standard (2003) BS EN

17

18

19 20

21

22

23

24

25

26

27

28 29

1839:2003. Limits of Flammability Measurement Standard for Gases and Vapours. BAM (1986) DIN 51649. Bestimmung der Explosionsgrenzed von Gases und Gasgemischen in Luft, Tell 1. Schroder, V. and Molnarne, M. (2005) Flammability of gas mixtures Part 1 Fire Potential. J. Hazard. Mater., A121, 37–44. Razus, R. et al. (2004) Chem. Eng. Process., 43, 775–784. Barnett, H.C. and Hibbard, R.R. (eds) (1955) Adaptation of Combustion Principles to Aircraft Propulsion. Vol. 1 Basic Considerations in the Combustion of Hydrocarbon Fuels with Air. NACA RM E54107. Odgers, J. and Kretschmer, D. (1986) Gas Turbine Fuels and their Influence on Combustion, Abacus Press. Maisey, H.R. (1965) Gaseous and Dust Explosion Venting Part 2. Chem. Proc. Eng., 46, 662. Field, P. (1983) Explosibility assessment of industrial powders and dusts, HMSO, Department of the Environment, BRE. Andrews, G.E., Ahmed, N.T., Phylaktou, H.N., and King, P. (2009) Weak Extinction in Low NOx Gas Turbine Combustion. The ASME IGTI International Gas Turbine Congress, ASME Paper GT200959830, ISBN: 978-0-7918-3849-5. Coward, H.F. and Brinsley, F.J. (1914) The dilution limits of inflammability of gaseous mixtures Part 1, The determination of dilution limits. Part II The lower limits for hydrogen, methane and carbon monoxide in air. J. Chem. Soc., 105, 1859–1885. (a) Andrews, G.E. and Bradley, D. (1973) Proceedings of the 14th International Combustion Symposium, pp. 1119–1128; (b) Phylaktou, H.N., Andrews, G.E., and Herath, P. (1990) J. Loss Prevent. Proc., 3, 355. Hattwig, M. and Steen, H. (eds) (2004) Handbook of Explosion Prevention and Protection, Wiley-VCH. White, A.G. (1925) J. Chem. Soc., 127, 672–684. Hustad, J.E. and Sonju, O.K. (1988) Combust. Flame, 71, 283–294.

30 Mullins, B.P. and Penner, S.S. (1959)

31 32

33

34

35

36

37

38

39

40

41

42

43

Flammability, Explosion, and Detonation, Pergamon. Holtapples, K. et al. (2002) Chem. Ing. Tech., 73, 270–274. HSE Books (1998) HSG54, Maintenance, Examination and Testing of Local Exhaust Ventilation, 2nd edn. Christou, M.D. (1998) in Risk Assessment and Management in the Context of the Seveso II Directive (ed. C. Kirchsteiger), Elsevier, pp. 193–224. Roberts, A.F. (1982) Thermal radiation hazards from release of LPG fires from pressurised storage. Fire Safety J., 4, 197–212. Phillips, E.H. (1996) TWA Probe Advances, but no cause found. Aviation Week and Space Technology, 29 July, pp. 26–28. Fire Protection Association (1989) Properties of common flammable solvents used in coating materials, Fire Safety Datasheet 6019. Persson, H. et al. (2008) Fuel vapour composition and flammability properties of E85. SP Technical Research Institute of Sweden SP Report, p. 15. Fire Protection Association (1973) Properties of some common flammable liquids and gases, Fire Safety Datasheet 6011. Zabetakis, M.G. and Richmond, J.K. (1953) The determination and graphic representation of the limits of flammability of complex hydrocarbon fuel at low temperatures and pressures, 4th Symposium (International) on Combustion, The Combustion Institute, pp. 121–126. Shepherd, J. (1997) Jet A Explosion Experiments. Cal. Inst. Tech, 21 November 1997. Available at: www.galicit.caltech.edu. Andrews, G.E. and Phylaktou, H.N. (1993) Gas explosions in linked vessels. J. Loss Prevent. Proc., 6 (1), 15–19. Andrews, G.E. and Bradley, D. (1972) The burning velocity of methane-air mixtures. Combust. Flame, 19, 275. Moore, P. and Bartknecht, W. (1987) Extending the limits of explosion

References

44

45

46

47

48 49

50 51 52

53 54 55

suppression systems. Staub-Reinhalt. Luft, 47, 209–217. Moore, P. et al. (1984) Explosion suppression – It’s effectiveness and limits of applicability. VDI-Berichte, 701, 421–466. European Standard (2007) EN 14994:2007. Gas explosion venting protective systems. European Standard (2008) EN 14491:2008. Dust explosion venting protective systems. Kasmani, R.M., WIllacy, S.K., Phylaktou, H.N., and Andrews, G.E. (2006) 2nd International Conference on Safety and Environment in Process Industries, Naples, Italy. Andrews, G.E. and Bradley, D. (1972) Combust. Flame, 18, 133. Harris, R.J. (1983) The Investigation and Control of Gas Explosions in Buildings and Heating Plant, E & FN Spon Ltd, London. Bradley, D.B. and Mitcheson, A. (1976) Combust. Flame, 26, 201. Swift, J. (1989) Loss Prev. Proc. Ind., 2 (1), 5–15. Runes, E. (1972) Explosion Venting. Proceedings, 6th Symposium on Loss Prevention in the Chemical Industry, New York, p. 63. Bradley, D.B. and Mitcheson, A. (1978) Combust. Flame, 32, 221–236. Bradley, D.B. and Mitcheson, A. (1978) Combust. Flame, 32, 237–255. Molkov, V.V. (1995) Theoretical generalization of international experimental data on vented gas explosion dynamics. Phys. Combust. Explos., 165–181.

56 Pritchard, D.K., Allsopp, J.A., and Eaton,

57

58

59

60

61 62

63

G.T. (2001) Gas explosion venting in elongated enclosures. Available at: www.safetynet.de. Tite, J.P., Binding, T.M., and Marshall, M.R. (1991) Explosion relief for long vessels. Fire and explosion hazards. The Institute of Energy. Siwek, R. (1995) Proceedings of the First International Seminar on Fire and Explosion Hazards, Moscow, p. 213. Alexiou, A., Phylaktou, H., and Andrews, G.E. (1997) A comparison between endvented and side-vented gas explosions in large L/D vessels. Trans. Inst. Chem. Eng., Proc. Safety Environ. Technol., 75 (Part B), 9–13. Ferrara, G., Benedetto, A., Willacy, S., Phylaktou, H.N., Andrews, G.E., and Mkpadi, M.C. (2005) Duct-vented propane/air explosions with central and rear ignition. Eighth International Symposium, Association for Fire Safety Science, IAFSSS 2005, Beijing, pp. 1341–1352. Rasbash, D.J. and Rogowski, Z.W. (1960) Combust. Flame, 4, 301. Rasbash, D.J. and Rogowski, Z.W. (1963) 2nd Symposium on Chemical Processing Hazards, Institute of Chemical Engineers. Phylaktou, H. and Andrews, G.E. (1993) Prediction of explosion relief requirements for long enclosures based on totally confined tests. Presented at a meeting of the UK Explosions Liaison Group (UKELG), 1992. Also at 14th ICDERS, International Colloquium on the Dynamics of Explosions and Reactive Systems, Portugal.

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17 Flame Retardants: Chemistry, Applications, and Environmental Impacts Adrian Beard and David Angeler

17.1 Introduction

Flame retardants are chemicals which are added to combustible materials to render them more resistant to ignition. They are designed to minimize the risk of a fire starting in case of contact with a small heat source such as a cigarette, candle, or an electrical fault. If the flame-retarded material or an adjacent material has ignited, the flame retardant will slow down combustion and often prevent the fire from spreading to other items. Many common materials (e.g., paper, wood, textiles, plastics) can be easily ignited by smoldering items such as cigarettes or small flames from candles, matches, or lighters. The ease of ignition depends not only on material properties but also very much on the material thickness and orientation (e.g., horizontal or vertical ignition). These factors play a crucial role in fire tests (see Section 17.4). Flame retardants are meant to prevent the ignition from small ignition sources, and to slow fire spread during the early stages of a developing fire. They are not designed to stop a fully developed fire, although they may lower the rate of heat release. The regions where flame retardants function in a fire scenario are shown in Figure 17.1. Fire development in buildings in discussed further in Chapter 20 of this volume. Since flame retardancy can be achieved by different physical and chemical mechanisms, there is also a great variety in products which can be used to achieve a lowering of the flammability of materials. Flame retardants are commonly grouped by their “active” element, the most common of which are bromine, chlorine, phosphorus, and nitrogen. In addition, there is a group of “mineral” flame retardants based on aluminum and magnesium hydroxides. This variety of products is necessary, because the materials and products which are to be rendered fire-safe are very different in their nature and composition. For example, plastics have a wide range of mechanical and chemical properties and differ in combustion behavior;

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Figure 17.1 A typical fire development curve for an enclosed room with the associated risks along the time line and showing where flame retardants are designed to take effect.

therefore, they must be matched to the appropriate flame retardants in order to retain key material functionalities. Flame retardants are thus necessary to ensure the fire safety of a wide range of materials, including plastics, foam and fiber insulation materials, foams in furniture, mattresses, wood products, and both natural and man-made textiles. These materials are for example, used in parts of electrical equipment, cars, aeroplanes, and as building components.

17.2 Flame Retardant Groups by Active Element and Mechanism

In 2007, the global consumption of flame retardants was estimated at 1.8 million metric tons, which represented a value of US$ 4 200 million [1]. Their allocation to the different chemical groups is shown in Figure 17.2. 17.2.1 Flame-Retardant Mechanisms

By chemical and/or physical action, flame retardants will inhibit or even suppress the combustion process. They interfere with combustion during a particular stage of this process, for example, during heating, decomposition, ignition, or flame spread. The amount of flame retardant that must be added to achieve the desired level of fire safety can range from less than 1% for highly effective flame retardants up to more than 50% for inorganic fillers; typical ranges are 5% to 20%, by weight.

17.2 Flame Retardant Groups by Active Element and Mechanism

Flame Retardants by Value (million $ US)

Flame Retardants by Volume [metric tons] Other 11%

Other 10%

Brominated

Aluminum hydroxide 13%

23%

Aluminum hydroxide 40%

Brominated 34%

Antimony oxides 8% Chlorinated 7%

Organophosphorous 20%

Organophosphorous 11%

Chlorinated 7%

Antimony Oxides 16%

Figure 17.2 Global consumption of flame-retardant chemicals, by volumes and value. Data for 2007, from SRI Consulting [1].

Specific chemical reactions tend to be more efficient than physical effects: .

.

Reactions in the gas phase: The radical gas phase combustion process is interrupted by the flame retardant; this results in a cooling of the system, reducing and eventually suppressing the supply of flammable gases. Reactions in the solid phase: The flame retardant builds up a char layer and shields the material against oxygen, thus providing a barrier against the heat source (flame). Physical effects from flame retardants include:

.

.

.

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Cooling: Energy-absorbing (endothermic) processes triggered by additives and/or the chemical release of water cool the substrate to a temperature below that required for sustaining the combustion process. Formation of a protective layer (coating): The material is shielded with a solid or gaseous protective layer; this protects it from the heat and oxygen necessary for the combustion process. Dilution: Inert substances (fillers) and additives that evolve noncombustible gases dilute the fuel in the solid and gaseous phases.

17.2.2 Bromine and Chlorine

Together, these products are designated as halogenated flame retardants. Currently, there are about 75 different commercial brominated flame retardants (BFRs), and fewer chlorinated products. Their mechanism of action is based on a suppression of the radical reactions in the flame zone. They are available in various forms, and may be liquids, powders, or pellets. BFRs are commonly used to prevent fires in

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electronics and electrical equipment, an area which accounts for more than 50% of their applications; examples include the outer housings of television sets and computer monitors. Indeed, the internal circuitry of such devices can heat up and, over time, collect dust such that short circuits and electrical or electronic malfunctions may occur. As printed circuit boards are commonly manufactured from flammable epoxy resins, these require fire protection, and in this case crosslinked brominated epoxy resin polymers manufactured from tetrabromobisphenol-A (TBBPA) are often used. In addition, BFRs are applied in wire and cable compounds as well as other building materials, such as insulation foams. Fabric backcoatings for curtains, seating and furniture in transport and public buildings, as well as in domestic upholstered furniture, can also be treated with BFRs. The most important BFRs include (see Figure 17.3): .

.

.

.

Polybrominated diphenylethers (PBDE): Decabromodiphenylether (Deca- BDE) is the only commercial product currently in use from the PBDE family (at least in Europe, see Section 17.6.1). It has 10 bromine atoms attached to the diphenylether molecule, a high molecular weight of 960 amu, and a high thermal stability. Its major applications are in styrenic polymers, polyolefins, polyesters, nylons, and textiles. The use of two other commercial products of the PBDE family, PentaBDE and Octa BDE, was terminated in Europe in August 2004. Hexabromocyclododecane (HBCD) is a cycloaliphatic BFR. It is commonly used in foamed polystyrene for the insulation of buildings where very low loadings are sufficient to reach the prescribed fire safety level. For compact polystyrene (highimpact), higher loadings are necessary. Another application is in textiles, partly as a replacement for Deca-BDE. Tetrabromobisphenol-A (TBBPA) is mainly used in epoxy resins for printed wiring boards, where it is reacted into the polymer backbone and thus becomes a tightly bound part of the polymer. It is also used as an additive flame retardant mainly in ABS plastics, as an intermediate in the production of other brominated flame-retardant systems, in derivatives, in epoxy oligomers, and in engineering plastics for electrical and electronic devices. Others: Brominated polystyrene is commonly used in polyester and polyamides. It is a polymer itself, and is therefore immobile in the matrix. Brominated phenols (e.g., tribromophenol) are reactive flame retardants that are most often used as intermediates in the manufacture of polymeric brominated flame retardants. They can be used as end caps in brominated carbonate oligomers and brominated epoxy oligomers, which in turn are used as flame retardants. Tetrabromophthalic anhydride is often applied as a reactive flame retardant in unsaturated polyesters used to manufacture circuit boards and cellular phones. It also serves as a raw material for the manufacture of other flame retardants.

Chlorinated flame retardants are less common than their brominated counterparts. Chlorinated paraffins represent the largest group; these are straight-chain hydrocarbons (C > 10) that have been chlorinated up to a chlorine content by weight of 30–70%. Their global consumption for flame-retardant applications was estimated

17.2 Flame Retardant Groups by Active Element and Mechanism

Br

Br

Br

Br

Br

O Br

Br

Br Br

OH

HO

Br

Deca-BDE

Br

Br

Br

Br

Br

Tetrabromobisphenol-A

Br *

Br Br

Br

*

Br x

Br

Hexabromocyclododecane (HBCD) Br

n

Brominated polystyrene

O

Br

OH O

Br

Br x

O

Br

Tetrabromophthalic anhydride

Brominated phenols Cl

Cl Cl

Cl

Cl

Cl

Cl Cl

Cl

Cl

Chlorinated paraffins (example)

Cl

Cl

Cl Cl

Cl Cl

Cl

Dodecachlorpentacyclooctadecadiene (Dechlorane) Figure 17.3 Chemical structures of some common brominated and chlorinated flame retardants.

at 132 000 metric tons in 2007, with a value of US$ 290 million. Their major applications are plastics, rubber, coatings, adhesives, and textiles. In addition to chlorinated phosphate esters, very few specialty products have gained commercial importance; an example is Dechlorane plusÒ (CAS 13560-89-9), a tricyclic aliphatic compound which is used in various engineering plastics and thermosets. Antimony trioxide is commonly used in combination with BFRs or halogenated polymers such as poly(vinyl chloride) (PVC), where it produces a synergistic effect. The most important reactions take place in the gas phase, and are the result of enhancing the radical chain mechanism of the halogens.

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17.2.3 Phosphorus

Phosphorus-containing flame retardants (PFRs) are widely used in standard and engineering plastics, polyurethane foams, thermosets, coatings, and textiles. The class of PFRs covers a wide range of inorganic and organic compounds, and includes both reactive (chemically bound into the material) and additive (integrated into the material by physical mixing only) compounds (see Figure 17.4). They have a broad application field, and a good fire safety performance. The most important PFRs include phosphate esters, phosphonates and phosphinates, red phosphorus, and ammonium polyphosphate. Phosphate esters are mainly used as flame-retardant plasticizers in PVC (alkyl/aryl phosphates) and engineering plastics, particularly in polyphenylene oxide/high impact polystyrene (PPO/HIPS), polycarbonate/acrylonitrile butadiene styrene (PC/ABS) blends and polycarbonate (PC, e.g., triphenylphosphate, resorcinol- and bisphenol A-bis-(diphenyl) phosphate). The latter are widely used in information technology environments that require high fire safety levels, but other applications include phenolic resins and coatings. Additive chlorinated phosphate esters such as

O

O R1 O

R1 P

P O R3

O O R3

R1 P

O

O

O R3

R2

R2

R2

Phosphinate

Phosphonate

Phosphate ester

R1, R2, R3 are organic substituents, they can be the same or different

O

O HO

P

O

n

O P O

H

O

O

O P

O

O

ONH4 Ammonium polyphosphate Resorcinoldiphosphoric acid tetraphenylester (RDP) Cl

O

O P

O

Cl

O

Cl Tris-(chloroisoproyl) phosphoric acid ester (TCPP) Figure 17.4 Principal chemical structures of phosphorus-based flame retardants, and specific examples.

17.2 Flame Retardant Groups by Active Element and Mechanism

tris(2-chloroisopropyl) phosphate (TCPP) and tris(1,3-dichloroisopropyl) phosphate (TDCP) are used in flexible polyurethane foams for upholstered furniture and automotive applications. TCPP is also widely used in rigid polyurethane insulation foams. Certain phosphates, phosphonates and phosphinates are used as reactive PFRs in flexible polyurethane foams for automotive and building applications. Additive organic phosphinates represent a new class of flame retardant for use in engineering plastics, particularly in polyamides and polyesters. Specific reactive PFRs are used in polyester fibers and for wash-resistant flame-retardant textile finishes. Other reactive organophosphorus compounds can be used in epoxy resins in printed circuit boards. Flame retardant grades based on red phosphorus are mainly used in polyamide 6 and 66, and must meet stringent Underwriters’ Laboratories fire safety levels (UL 94 V0) at low dosages; they are particularly effective in glass fiber-reinforced formulations. Ammonium polyphosphate grades are primarily used in intumescent coatings, and also in rigid and flexible polyurethane foams and polyolefins (injectionmolded types), in formulations for unsaturated polyesters, phenolics, epoxies, and coatings for textiles. 17.2.4 Nitrogen

The largest group of nitrogen-containing flame retardants is based on melamine (see Figure 17.5): pure melamine, melamine derivatives, that is, salts with organic or inorganic acids such as boric acid, cyanuric acid, phosphoric acid or pyro-/polyphosphoric acid, and melamine homologues such as melam, melem, and melon. Nitrogen flame retardants are believed to act via several mechanisms. In the condensed phase, melamine is transformed into crosslinked structures which promote char formation; ammonia is released in these reactions. A mechanism in the gas phase may be the release of molecular nitrogen, which dilutes the volatile polymer decomposition products. Melamine is mainly used in polyurethane foams, NH2 ..... O

NH2 N N

N

H2N

N

NH2

H2N

NH2

HO

O N

NH2 ..... O

Melamine NH

N ..... H N

H2N

N

H N

H

Melamine cyanurate (MC) O P

OH

OH

Guanidine phosphate Figure 17.5 Examples of nitrogen-based flame retardants.

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whereas melamine cyanurate is used in nylons or in polypropylene intumescent formulations in conjunction with ammonium polyphosphate. The phosphate, polyphosphate, and pyrophosphates of melamine contain both nitrogen and phosphorus, and are used in nylons. In some specific formulations, triazines, isocyanurates, urea, guanidine and cyanuric acid derivatives are used as reactive compounds. 17.2.5 Mineral Flame Retardants

With an estimated global consumption of 730 000 metric tons in 2007 [1], aluminum trihydroxide (ATH) is by far the most widely used flame retardant. It is inexpensive, but usually requires high loadings in polymers of up to more than 60%. The flameretardant mechanism is based on the release of water, which cools and dilutes the flame zone. Magnesium hydroxide (MDH) is used in polymers which have higher processing temperatures, because it is stable up to temperatures of around 300
C; by comparison, ATH decomposes at about 200
C (Scheme 17.1). Fine-precipitated ATH and MDH (<2 mm) are used in the melt compounding and extrusion of thermoplastics such as cable PVC or polyolefins for cables. For use in cables, ATH (and more often MDH) are coated with organic materials to improve their compatibility with the polymer. Coarser-ground and air-separated grades can be used in the liquid resin compounding of thermosets for electrical applications, seats, panels and vehicle parts. Although other inorganic fillers such as talc or chalk (calcium carbonate) are not flame retardants in the true sense, they simply dilute the combustible polymer, thereby reducing its flammability and fire load.

Scheme 17.1 The principal reactions of aluminum and magnesium hydroxides.

17.2.6 Nanomaterials: Layered Clay Minerals and Carbon Nanotubes

Since the late 1990s, these “nanomaterials” have been intensively studied as potential new flame retardants. Nanocomposites are layered silicates based on aluminosilicate clay minerals (such as montmorillonite) that are composed of layers with gaps (gallery spaces) in between, and have the ability to disperse in polymers. Studies of flame retardancy with nanocomposites have focused on plastics such as polymethylmethacrylate (PMMA), polypropylene, polystyrene, and polyamides. Nanocomposites, in particular, prevent dripping and promote char formation, and carbon nanotubes (CNTs) and cage-like silicate structures such as “POSS” produce similar effects [2], and have therefore been used as synergists in some polymer/flame-retardant

17.3 Safety Regulations and Fire Test Standards

combinations. Unfortunately, these require special processing and so for the time being are not considered to become viable stand-alone flame retardants. A general update on current research in flame retardants is presented in Ref. [3]. 17.2.7 Other Flame Retardants and Synergists: Borates, Zinc Compounds, and Expandable Graphite

One major application of borates is the use of mixtures of boric acids and borax as flame retardants for cellulose (cotton), and of zinc borate for PVC and other plastics such as polyolefins, elastomers, polyamides, or epoxy resins. In halogen-containing systems, zinc borate is used in conjunction with antimony oxide, while in halogenfree systems it is normally used in conjunction with ATH, MDH, or red phosphorus. In some particular applications zinc borate can be used alone. Boron-containing compounds function via the stepwise release of water and formation of a glassy coating that protects the surface. Zinc compounds were initially developed as smoke suppressants for PVC (e.g., zinc hydroxystannate), although later it was found that they also acted as flame retardants in certain plastics, mainly by promoting char formation. Zinc sulfide shows synergistic effects in PVC, and can substitute antimony trioxide. Expandable graphite is manufactured from flake graphite by treatment with strong acids, such as sulfuric or nitric acid. The acid is trapped in the crystal layers of the graphite (“intercalated”) which, when heated, begins to expand up to several hundred cubic centimeters per gram, forming a protective layer for the polymer. Expandable graphite is used in plastics, rubbers (elastomers), coatings, textiles, and especially in polymeric foams. In order to provide perfect flame retardancy, the use of synergists such as ammonium polyphosphate or zinc borate is necessary. Unfortunately, the black color of graphite limits its applicability in some cases.

17.3 Safety Regulations and Fire Test Standards

With very few exceptions, the addition of flame retardants increases the cost of polymers or textiles, and also adds complexity to the production process. Therefore, flame retardants are generally used only when safety regulations require a certain level of fire safety. The most common approach is that laws define a fairly general safety level for example, for building materials or electronic equipment. The technical detail is specified in product standards, as for example a safety standard for computers. For certain high-risk parts within a computer this standard will make reference to fire test standards that define levels of flammability and reaction to fire. The decision is then left to the manufacturer of the finished article as to how the safety of the product is ensured, and whether noncombustible materials such as metals or polymers treated with flame retardants are used. The latter approach is often the most economical and technically attractive solution.

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Figure 17.6 Examples of different fire tests. (a) Cone calorimeter; (b) Flooring radiant panel; (c) Limiting oxygen index. Illustrations courtesy of Fire Testing Technology, London, UK.

Fire tests were developed to simulate the ignition behavior of materials or even real fire events. The sample size can vary in size from a small strip of material (e.g., 12.7 cm  1.27 cm; for the Underwriters’ Laboratories 94 test) up to boards of 1.5  1.5 m2 (European Single Burning Item Test, EN 13823), individual furniture items, or even complete furnished rooms (see Figure 17.6). It is important to note that the fire behavior of a material or product depends not only on intrinsic material properties such as the heat of combustion, but also very much on the type and size of the ignition source and the shape and geometric arrangement of the material.1) All of these factors together define a “fire scenario,” and materials can perform quite differently depending on the nature of such a scenario. It is for this reason why some materials perform well in some tests, but fail in other tests. Usually, specific fire tests are detailed for different applications (see examples in Table 17.1), because they are meant to simulate typical risks and scenarios. For example, a test for train seats may use a crumpled newspaper (or a gas burner mimicking the same) to simulate somebody trying to set fire to a seat on purpose (vandalism and arson). Alternatively, tests for electronic products mimic short circuits or other electrical failures leading to localized high temperatures. Troitzsch has compiled a detailed overview on many standards for reaction to fire tests [4].

17.4 Applications in Industry

Flame retardants are applied in many common products and items, because many of the natural and man-made materials used in the construction and design of articles are flammable, and may pose a fire risk. Safety regulations either apply to certain 1)

We all experience this when trying to set fire to chunks of wood that are resistant to small flames. Very small pieces of wood (“kindling”) are much more easily lit, and can then be used set much larger wood blocks on fire, after a while.

17.4 Applications in Industry Table 17.1 Examples of different fire test standards in different application areas.

Underwriters Laboratories UL 94 (global): Electric and electronic equipment EN 13823 (Europe): Construction materials EN ISO 11925-2 Ignitability test; Construction materials ISO 9705 (global): Construction materials BS 5852 (British): Upholstery and cover fabrics BS 5867 (British): Contract curtain EN 532 (Europe): Workwear and protective clothing DIN EN ISO 6941 (Germany): Textiles in general FAR 25.853 (US): Airplane interior FMVSS 302 (US)/DIN 75200 (Germany): Automotive FFA 16 CFR Part 1610 (US): Clothing textiles FFA 16 CFR Part 1615 (US): Children’s sleepwear

environments such as buildings, aeroplanes, and trains, or to certain types of product such as electrical and electronic equipment. The major industrial applications of flame retardants are briefly highlighted in the following sections. 17.4.1 Electric and Electronic Equipment

Polymers are an indispensable material for electrical and electronic items, because they provide electrical insulation and can be easily processed into a variety of shapes with a range of mechanical properties. With the exception of PVC, all commodity and engineering plastics are flammable and may require the use of flame retardants to pass safety requirements. In general, high-performance polymers have a very low flammability; for example, polytetrafluoroethylene (PTFE) is noncombustible, while even the other polymers of this group that are nonhalogenated, such as polyetheretherketone (PEEK), are highly fire-resistant. However, these high-performance polymers are not only much more expensive than common plastics but are also more difficult to process because of their high melting points. A list of typical flame retardants used in electrical and electronic equipment is provided in Table 17.2. 17.4.2 Construction

Whereas, the development of good compartmentation by, for example fire walls, has halted the occurrence of catastrophic fires that may spread over whole neighborhoods, today attention is focused on prevention during the early stages of a fire, and limiting the spread of deadly smoke through buildings. The use of flame retardants in construction products depends very much on national building codes and regulations. For buildings, different fire safety technologies exist that complement each other, but may also compete. For example, buildings can be protected by sprinklers,

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Table 17.2 Typical applications for flame retardants in electrical and electronic equipment.

Application

Polymer

Wire and cable

Polyvinyl chloride Polyethylene

Housings

Polystyrene High-impact polystyrene Copolymers Polyesters Polyamides

Electric components

Printed wiring boards

Epoxy resins

Flame retardants Halogen-free

Halogenated

Phosphate esters Aluminum hydroxide Magnesium hydroxide Aryl phosphates

Brominated diphenyl ethers

Phosphinates Phosphinates Magnesium hydroxide Melamine cyanurate DOPOa) Aluminum hydroxide Phosphinates

Brominated diphenyl ethers

Brominated benzacrylates Brominated polystyrene

Tetrabromo bisphenol-A

The brominated flame retardants are often used in combination with antimony trioxide. a) DOPO: Dihydro-oxaphosphaphenanthrene.

or by treatment with flame retardants, thus reducing the flammability of high-firerisk construction products such as foamed insulation plastics. Major applications for flame retardants in the construction sector are polymeric insulation materials, fire-stopping seals, electrical installations and cables and, to a lesser extent, also the treatment of wood for better fire ratings. One application which is specific to the construction sector is the protection of steel by using flame-retardant coatings. Steel loses its strength at temperatures above 500
C, which can be reached within 15 min in a severe fire. Thus, protection is required to provide people with more time to evacuate a building. An alternative to cladding with noncombustible boards or mineral-based layers is the application of intumescent coatings. These have the appearance of ordinary paint, but are in fact an elaborate composition of active ingredients which, in the case of fire, undergo a series of chemical reactions leading to the formation of a foam that solidifies and provides the steel with thermal insulation for up to 120 min. During this process, the coating may expand up to 100-fold its original thickness (see Figure 17.7). 17.4.3 Transport: Aeroplanes, Ships, Trains, and Road Vehicles

Fire safety requirements in transport are related to the risk level of a transport mode, and in particular to the ease of escape in case of fire. This is lowest in aeroplanes, which therefore have the highest safety requirements. Next come ships and trains, whereas road vehicles like buses and cars have the lowest fire performance

17.4 Applications in Industry

Figure 17.7 Example of an intumescent coating after a fire test. The height of the foam is approximately 10 cm, from an original coating thickness of only 1 mm. Photograph courtesy of Clariant Produkte (Deutschland) GmbH.

requirements. However, a number of tragic bus fires, coupled with the statistics on car fires, have proved that the assumption “. . . when there is a fire, you can just stop and get out” is, in many cases, not true. For example, in November 2008, twenty people were killed in bus fire near Hannover, Germany, because they were trapped, despite the fire service being at the scene with minutes of the fire starting. As in the construction sector, flame retardants can be used in the realm between the requirement for strictly noncombustible materials and no fire requirements; that is, they lend some protection but do not make materials noncombustible. In aeroplanes, where the strictest requirements including smoke toxicity are applied, common thermoplastics are only found to a limited extent. Flame retardants are commonly used in seat padding, cover textiles, arm rests and electrical installations, whereas large interior parts such as cladding are made from thermoset resins which have a low intrinsic flammability and so do not require the addition of flame retardants. Although requirements in trains are somewhat lower, in Europe a new harmonized set of standards has been developed for railway rolling stock which uses state-of-the-art fire testing (prEN 45545). This defines different hazard levels for trains, for example depending on whether they operate only above ground or underground and in tunnels. The fire safety requirements for cars are very low, especially for the passenger compartment. The most commonly used standard (in various forms worldwide) is the US American FMVSS 302, which dates back to 1972. Today, the main risk is that a fire in the engine compartment will spread to the passenger compartment where it comes into contact with a huge amount of thermoplastics, foam, and textiles.

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17.4.4 Textiles

This application area overlaps those described above, because textiles may be used as curtains and drapes in buildings, and as covers on furniture and passenger seats. Technical textiles and protective clothing are also used in the workplace by firefighters and others. The flammability of textiles covers a similar range to common thermoplastics, with some high-performance textiles that are virtually nonflammable (e.g., certain aramids, as used by firefighters), some highly flammable fibers such as cotton or polyolefinic fabrics and, of course, a range in between. Flame retardants are only used in textiles where specific requirements exist. As with the thermoplastics used in electronics, the flame retardants must match the polymer or natural fiber to be protected. Flame retardants can be added to man-made fibers during the processing steps, and some can even be integrated chemically into the polymer chain. The alternative is to treat the finished textile by applying the flame retardant in a coating or suspension/solution. Some natural fibers, such as cotton, may be chemically modified on the surface with phosphorus-based chemicals.

17.5 Environmental and Human Health Concerns

In the previous sections, the wide variety of flame-retardant formulations has been described, in terms of their chemical composition. Even within a single retardant category, the chemical structures may vary widely, as demonstrated by the presence of many congeners in brominated flame retardants, for example. Fire retardants used in the management of wildland fires may also be used in different combinations of chemical materials, depending on whether they are used by ground or aerial means. These differences in chemical structure may lead to different degrees of environmental impact, and consequently it may be difficult to extrapolate the impact derived from one product to the other, based on these differences. However, on a broad basis, the impact of different flame retardants based on organic or inorganic compounds can easily be separated. Both, ecotoxicologists and ecologists have long recognized the importance of monitoring the environmental impact of toxicants across different levels of biological hierarchy, including complementary laboratory and field studies of populations, communities and ecosystems to combine ecological realism with the full impact potential of chemical stressors. In recent years, research groups have used very different approaches to determine the environmental impact of fire-retardant chemicals. It is, therefore, often very difficult to generalize results based on studies of only a few flame-retardant products, and to determine the congruence of their impact on biota and ecosystems within and between geographical areas. However, recent reviews have provided unequivocal evidence of the adverse impact of some of these substances in the environment, thus highlighting the need for future

17.5 Environmental and Human Health Concerns

research [5, 6]. Current knowledge in this area will be summarized in the following sections. 17.5.1 Brominated Flame Retardants

The BFRs are organic substances frequently used in many industries. With regards to possible negative effects on human and animal health, attention has been mainly focused on the PBDEs, which may persist in the environment for extended periods of time and accumulate in all living beings. The PBDEs are highly stable lipophilic materials that resemble polychlorinated biphenyls (PCBs) in their structure, as well as their physical and chemical characteristics. Today, the most frequently used PBDEbased products are commercial decaBDE mixtures, although in the past octaBDE and pentaBDE mixtures have been used extensively. Most importantly, the use of several BFRs has now been restricted in Europe, and has declined in other regions of the world. The PBDEs persist within the environment, accumulate in food chains, and have toxic effects; they are, therefore, a potential health risk both for animals and humans. PBDEs have been shown to negatively affect processes of hormonal regulation in living organisms, and are regarded as so-called environmental “endocrine disruptors” [7]. While it is beyond the scope of this chapter to provide a detailed overview of physiological and genetic processes (because the underlying mechanisms have in many cases not been well investigated), it is necessary to highlight their negative effects on the disruption of thyroid hormone homeostasis in the serum and plasma of exposed experimental organisms (mainly experimental rodents). In vitro studies have also demonstrated the ability of these substances to bind to estrogen and androgen receptors, thereby affecting the regulation of sex steroid hormones. For example, male rats exposed to PBDEs show signs of androgenic degeneration, which manifest as a delayed onset of puberty, suppressed growth of the ventral prostate gland and seminal vesicles, and delayed preputial separation [8]. As a result of hormonal disruption, experimental animals often show signs of behavioral changes. Tests on rodents have also demonstrated neurotoxicity of some PBDEs, impacting the animals’ spontaneous behavior, learning, and memory capacity [9]. These effects seem to be particularly severe during early life stages, with the behavioral changes becoming more pronounced as the individuals age. There exists, therefore, a clear correlation between the age of individuals and the magnitude of behavioral changes as a result of exposure to PBDEs. These findings are of major concern because PBDEs are highly stable materials, with only a limited degradation capability. In fact, PBDEs are ubiquitous in the environment, and have even been found in remote areas where no heavy industrialization exists (e.g., in Greenland). The most frequently detected PBDE congeners in living organisms include, first and foremost, BDE-47, BDE-99, BDE-100 and BDE153, BDE-154 and BDE-183, with the most important congener in sediments being BDE-209 [5].

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Sediment is the main source of contaminants that affect biota in the aquatic environments. The load of PBDEs in sediments depends chiefly on the amount of industrial pollution in the area. High PBDE concentrations were found in sediment samples collected in the Schelde estuary in the Netherlands, and in the Tees estuary and rivers Skerne and Calder in the United Kingdom. Probably the highest contamination level in Europe was found in sediment samples collected downstream of a wastewater treatment plant at the Calder river, where high amounts of commercially manufactured decaBDE compound were detected. No PBDEs were detected in sediment samples collected in upstream-site control locations, which suggests that contamination from point sources stems from the companies that manufacture or use PBDE-based products. One of the least contaminated locations in Europe, on the other hand, is the Danube delta, where no PBDEs were detected (for a review, see Ref. [5]). As in the case of sediments, PBDE concentrations detected in fish correlate with the intensity of industrialization in the region. When monitoring the concentrations of BFRs in samples of bleak (Alburnus alburnus) from the Cinca river (Spain), adult fish caught immediately downstream of a highly industrialized region had at least two orders of magnitude higher concentrations of BFR compared to those originating from uncontaminated upstream sites. It is interesting to note that juvenile fish often have lower concentrations of BFRs in their tissues compared to adult or older specimens, which suggests that these substances bioaccumulate during an organism’s life span. PBDE concentrations in organisms also depend to a large extent on the trophic position of the organisms in food webs. For example, the highest PBDE concentrations were detected in predatory fish (e.g., in Wels catfish, Silurus glanis), while PBDE concentrations in the tissues of herbivorous or omnivorous fish with a predominantly vegetable diet (e.g., nose-carp, Chondrostoma regium) were markedly lower [10]. BFRs are also readily detected in the terrestrial environment. For example, when comparing PBDE concentrations in the eggs of wild peregrine falcons (Falco peregrinus) from Sweden, Lindberg et al. [11] showed levels in captive species to be substantially lower than in wild populations. Likewise, high concentrations of PBDE were detected in the eggs of the little owl (Athene noctua) in Belgium [12], especially of congeners BDE-99, BDE-153, and BDE-47. Given the long persistence of BFR in the environment, these results suggest a bioaccumulation of congeners during these predators’ life times, with biomagnification resulting from the food-web effects of BFR. Humans are undoubtedly the ultimate top-predators on Earth, and may thus inevitably be exposed to BFRs in the food chain (mainly via the consumption of fish and other foods of animal origin, as well from the air). Whilst initially those people involved in the production and use of BFRs may be especially at risk of excessive exposure (for a review, see Ref. [13]), the results of recent studies have also shown that those who have no obvious direct contact with BFRs may accumulate these materials in the body, to different degrees. Recently, concentrations in breast milk, blood serum and human adipose tissue have attracted intensive scrutiny, it having been suggested [5] that the USA population suffers from a much greater exposure to PDBEs than that of Europe, where the exposure is far less serious. However, it was acknowledged by the report authors that the different sizes of experimental groups

17.5 Environmental and Human Health Concerns

and different numbers of PBDE congeners monitored limited the interpretation of these results. Given the proven environmental repercussions of PBDEs in the environment, their use is today regulated, especially in Europe, with the use of penta- and octaBDE mixtures being very restricted, and even banned. Following an initial exemption of Deca-BDE from the directive on the “. . . restriction of certain hazardous substances from electric and electronic equipment” (RoHS, 2002/95/EC), it is no longer permitted in electric and electronic equipment in Europe (since July 2008). Despite recent action taken to reduce the burden of BFR contamination of the environment, the resultant problems will likely not diminish substantially in the short term, given the persistence of these contaminants in the environment. Today, increasing numbers of alternatives to brominated flame retardants are being developed by industry, mostly based on compounds of aluminum, phosphorus, and nitrogen. The good environmental and health profile of some of these new materials has already been proven in life cycle studies [14, 15]. 17.5.2 Wildland Fires and Retardants

The environmental issue of fire-retardant chemicals used to fight forest fires is fundamentally different from those of BFRs, because no a priori health issues for humans are apparent. Rather than bioaccumulation and biomagnification in food webs, the inorganic compounds are expected to impact on the abiotic component of ecosystem to which the organisms can respond indirectly. The impact of fire-retardant formulations used to fight wildland fires has been reviewed recently [6], with attention focused exclusively on long-term fire retardants (based mainly based on ammonium salts), and highlighting the paucity of studies examining environmental impacts at the community and ecosystem levels. The available information, which was based on acute and chronic toxicity tests in the laboratory, and using a wide spectrum of aquatic populations (algae, invertebrates, and fish), suggested that aquatic ecosystems would be particularly sensitive to the impact of fire retardants. Such sensitivity was attributed to the high concentrations of toxic unionized ammonia which were formed under the test conditions. Evidence also exists that cyanide toxicity may be of concern; hydrogen cyanide (HCN) can be formed from sodium ferrocyanide (this is added to fire retardants to minimize damage to storage, transport, and delivery systems) through photoactivation following irradiation with ultraviolet (UV) light [16]. A greater toxicity towards fish and amphibians was also noted when fire retardants containing sodium ferrocyanide were exposed to UV-B radiation. Because Calfee and Little [16] measured weakly aciddissociable CN (which may include the highly toxic HCN) and also metal–CN complexes (which are less toxic to aquatic life [17]), the amounts of free CN reported in these studies may have been overestimated. The same group also reported weakly acid-dissociable CN values in low retardant dilutions in a laboratory experiment that lasted for 24 h. Thus, the extrapolation to field conditions not only of these results but also those obtained from small-scale, laboratory-based toxicity testing, must be made with care.

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The natural environment is highly variable, with abiotic and biotic compartments interacting in many complex ways. These interactions may mediate the magnitude of impact and posterior recovery processes from anthropogenic disturbances. More recently, the environmental behavior of fire retardant impacts under natural conditions has been evaluated by using a set of artificially constructed temporary ponds, which were almost identical in their biophysical settings [18, 19]. In this way, it was possible to determine the impact of fire retardant contamination on water quality, and also on the composition of the zooplankton community. The rationale of using temporary ponds is based on the fact that these ecosystems can be dry during summer periods, which coincides with seasons having the highest risk of wildland fires. Permanent water bodies may be easily perceived by firefighters, and it is mandatory not to deliver fire retardants to aquatic ecosystems. However, dry temporary ponds are often difficult to distinguish from the surrounding terrestrial landscape, so the risk of them becoming accidentally contaminated with retardants is especially high. This risk may be further increased if adverse meteorological conditions (e.g., strong winds and smoke development) complicate the firefighting, especially by aerial means. Recently, both Angeler and Moreno [18, 19] monitored retardant impact using an experimental design which is commonly used in environmental impact assessment. In this case, the impact was monitored in contaminated ponds before and after the application of two concentrations of the commercially available Fire Trol 934, over three hydroperiods. The first hydroperiod served to establish natural background conditions in water quality and zooplankton community composition; these could then be benchmarked with those of the second and third hydroperiods, which were affected by the retardant. Two application concentrations were chosen to simulate environmentally realistic impact scenarios in ponds that could be situated in grasslands (Treatment level 1; T1) and shrublands (Treatment level 2; T2), respectively. Considering that a commercially available liquid concentrate of Fire Trol 934 is composed of >90% mass fraction of ammonium polyphosphates, it may not be surprising that the retardant impact was manifest in the extreme hypereutrophication of the ponds – that is, a dramatic impoverishment in the water quality. Immediately after fire retardant contamination, the mean total phosphorus concentration rose to 60.3 mg l1 in T1 and to 216 mg l1 in T2, while preimpact levels were maintained in the control. The total nitrogen level also rose to 49.3 mg l1 (T1) and 120.4 mg l1 (T2). After the sampling date, although the control remained at 0.56 mg l1, the concentrations of phosphorus and nitrogen compounds tended to decrease during the after-period, although none of these variables seemed to converge with concentrations in the control during the after-period. The lack of convergence was more pronounced for variables related to phosphorus compared to nitrogen compounds. It appeared that the nutrient surplus had fuelled the phytoplankton growth, which resulted in biomass values of 249.2 mg l1 in T1 and 650.2 mg l1 in T2. Secchi transparency and pH were also affected by a fire-retardant treatment effect, but electrical conductivity, NH3, dissolved oxygen concentration, water color and other variables were not susceptible to any change in nutrient loading. The impact-recovery patterns can be nicely visualized in multivariate ordination space. Figure 17.8 integrates the temporal dynamics of all of the above-mentioned

17.5 Environmental and Human Health Concerns

water quality variables in monthly intervals over the three hydroperiods, based on nonmetric multidimensional scaling (NMDS) analysis. NMDS analysis showed that the water quality of the Before and After periods overlapped in ordination space in the case of the control, but not in the case of the fire-retardant treatments. The distance between the clouds of points from the After period relative to the Before period increased with retardant treatment along NMDS dimension 1. The sampling dates in the retardant treatments showed an incomplete convergence of water quality relative to the Before and After data from the control. This suggests that water quality does not recover to preimpact conditions during the first two hydrological cycles comprising

Figure 17.8 Nonmetric, multidimensional scaling plots showing recovery patterns of water quality in two-dimensional ordination space in the control (a), treatment 1 (b), and treatment 2 ponds (c). The open and solid circles represent

the Before and After data, respectively. The numbers express the time trajectories in months. The stress level attained for the ordination was 0.08.

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the postmonitoring period. However, the T1 and T2 points were generally closer to controls in ordination space at the end of the monitoring period than shortly after the treatments, which indicated that some degree of recovery had occurred in the treated ponds. The high dispersion of data points along NMDS dimension 2 was similar for the control and retardant treatments. Angeler and Moreno [18] have shown that variability along NMDS dimension 1 clearly reflected anthropogenic stress as a result of fire retardant contamination, while NMDS dimension 2 was chiefly related to the seasonal variability of the ponds associated with their natural disturbance regime. In a subsequent study, Angeler and Morneo [19] studied fire retardant impact on zooplankton community structure (Figure 17.9). The retardant caused a decline in species richness and an increase in rotifers during summer and winter months relative to controls and pretreatment dates; moreover, the duration of these changes varied among retardant treatments. In NMDS analyses, the increased rotifer densities were reflected in loops that showed recurring deviations from, and, upon collapse, approaches to reference conditions, while the effects of the anthropogenic stressor persisted in the ponds. The amplitudes of fluctuation followed no regular patterns; it varied with retardant treatment level, and was higher in the third hydroperiod compared to the second in one of the treatments. The results of zooplankton community recovery were therefore uncoupled from those of water quality, and no clear signs of recovery were evident from the biological point of view. From a temporal perspective, the nondampened patterns observed in the ordinations suggested a new cause-and-effect mechanism for disturbance ecology, which the authors referred to as a “protracted press disturbance– roller coaster response” relationship. This model emphasizes stochastic oscillations in community composition, punctuated by periods in which the community approaches reference conditions. From the applied viewpoint, this model suggests that the accurate detection of perturbation, and the implementation of sound management and restoration strategies will require intensive sampling designs that span multiple hydroperiods in persistently degraded ponds. Aquatic communities perhaps serve as ideal model systems to monitor impactrecovery patterns of fire retardant impact, because of their relatively fast turnover times. As succession processes are slower in terrestrial ecosystems, it is more difficult to determine recovery trajectories in the short term. Both, Cruz et al. [20] and Luna et al. [21] recently determined the seed germination response in relation to different Fire Trol 934 concentrations. Retardant exposure also caused a significant decrease in total germination in all tested plant species, while exposure to the highest Fire Trol 934 concentration resulted in a complete inhibition of germination. However, the sensitivity to Fire Trol was seen to vary across species, and this differential species sensitivity may potentially lead to different impacts in the soil seed banks, depending on whether sites are burned or unburned. Exposure to retardants may affect the recruitment of shrubland species particularly during dry autumns, due to a limited leaching of these chemicals from the soil surface. Based on these findings, it was concluded that the retardant use could have potential longterm effects in the environment. Hence, the avoidance of fire retardant use was recommended at sites where particularly sensitive plant species were present.

17.5 Environmental and Human Health Concerns

Figure 17.9 Nonmetric, multidimensional scaling plots showing zooplankton community similarity and recovery trajectories from fire retardant stress between (a) control ponds, (b) ponds treated with 1 l m2 and (c) 3 l m2 of Fire Trol 934. The black and white symbols show the data from the precontamination and

postcontamination periods, respectively; numbers above the symbols indicate time trajectories in months. The inset in (a) shows a higher resolution of data points that highly overlapped in the original ordination. The stress attained in the ordination was 0.09.

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Much of the previous discussion has focused on so-called “long-term fire retardants.” Yet, there exists a “second class” of retardant chemicals which are used to fight wildland fires. Retardant foam formulations are mainly composed of anionic surfactants, with their toxicity depending on the length of the alkyl chains. While their adverse effects on biota has been documented – mainly in the form of alteration of gill morphology in fish, a reduction in surface water tension, reduced oxygen assimilation and an increased potential to uptake toxicants such as heavy metals and organic pollutants – ecosystem-level responses to foam contamination have hardly been studied. Boulton et al. [22] studied the accidental contamination of Australian rivers with a retardant foam, but were unable to detect a marked impact on the macroinvertebrate community structure and water quality. However, these authors acknowledged the very high environmental heterogeneity of their study streams, which could have masked any potential contamination effects. This study serves as an instructive example that assessment at the ecosystem scale can be complicated by site-specific factors that limit strong statistical inference. Despite these uncertainties, laboratory-based acute and chronic toxicity tests have revealed that foam formulations may be more toxic to wildlife than long-term retardant formulations [23–27].

17.6 Outlook

Flame retardants are an essential element of fire safety, and will continue to be chosen to protect otherwise flammable materials in applications with fire risks. However, due to environmental and health concerns regarding some of the chemicals used, the industry is becoming increasingly focused on developing more environmentally benign products. Along with the general trend to substitute halogenated flame retardants, reactive systems which become part of the target polymer or textile as wells as oligomeric or polymeric flame retardants are seen as favorable, because they have no potential to migrate out of the finished products. Despite a busy patent literature, new flame retardant molecules which achieve commercial success are rare, because the technical hurdles in addition to the flame-retarding effect are high. In addition to compatibility with the target polymer and its processing, many applications demand high mechanical, electrical, or endurance criteria. Since the mid 1990s, nanomaterials such as nanoclays or CNTs have attracted much research interest as a potential new generation of flame retardants. However, these nanomaterials have not yet achieved commercial success, mainly because their beneficial effects are somewhat limited and their application requires some extra effort in the polymer processing.

17.7 Summary

By providing intrinsic fire safety with no maintenance requirements, as are needed for sprinklers and other technical systems, flame retardants are indisputably an

References

important element of fire safety. By providing a much higher degree of fire safety, flame retardants prevent injuries and fatalities from otherwise highly flammable materials in electronic products, construction materials, furniture and textiles around us. Flame retardants act in the very early stages of a fire: they are meant to prevent ignition from a low-energy source such as an electric spark or a match. In addition, they will slow down the flame spread of a developing fire. A wide range of inorganic and organic chemical substances will yield a flame-retarding effect, with products based on aluminum, bromine, chlorine, phosphorus, and nitrogen being the most commonly used. There are, however, concerns related to chemical release into the environment and potential health effects. Brominated flame retardants have been the focus of this scrutiny, with environmental organizations having brought these to widespread public attention. There have also been findings of phosphate ester flame retardants in indoor air and dust. The new European chemical legislation REACH (Registration, Evaluation and Authorization of Chemicals, Regulation 1907/2006/EC) will further increase the information on hazards and risks of flame retardants, in addition to European risk assessments and existing scientific literature. In industries such as consumer electronics and computers, there has recently been a strong trend towards replacing halogenated flame retardants. Fire retardants for wildland applications may have marked effects on ecosystems, because the substances used are purposefully distributed in high amounts. Common wildland fire retardants are based on ammonium salts, which will cause eutrophication in aquatic ecosystems with subsequent effects on species abundance and distribution. Laboratory-based acute and chronic toxicity investigations have revealed that foam formulations may be more toxic to wildlife than long-term retardant formulations.

References 1 Fink, U. (2008) Flame Retardants Market

2

3

4

5

Report. SRI Consulting. Available at: www.sriconsulting.com. Vannier, A., Duquesne, S., Bourbigot, S., Castrovinci, A., Camino, G., and Delobel, R. (2008) The use of POSS as synergist in intumescent recycled poly (ethylene terephthalate). Polym. Degrad. Stabil., 93, 818–826. Morgan, A.B. and Wilkie, C.A. (2007) Flame Retardant Polymer Nanocomposites, John Wiley & Sons, ISBN: 0471734268. Troitzsch, J. (2004) Plastics Flammability Handbook, Hanser Publishers, Munich, ISBN: 3-446-21308-2. Mikula, P. and Svobod
a, Z. (2006) Brominated flame retardants in the environment: their sources and

effects (a review). Acta Vet. Brno, 75, 587–599. 6 Gim
enez, A., Pastor, E., Zarate, E., Planas, E., and Arnaldos, J. (2004) Long-term forest fire retardants: a review of quality, effectiveness, application and environmental considerations. Int. J. Wildland Fire, 13, 1–15. 7 Trachsel, M. (2007) Consensus Platform “Brominated Flame Retardants” Final Document, Swiss National Research Program “Endocrine Disruptors,” Basel. Available at: http://www.nrp50.ch/uploads/ media/finaldocumentenglish_03.pdf. 8 Stoker, T., Cooper, R.L., Lambrecht, C.S., Wilson, V.S., Furr, R.J., and Gray, L.E. (2005) In vivo and in vitro antiandrogenic effects of DE-71, a commercial

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9

10

11

12

13

14

15

16

polybrominated diphenyl ether (PBDE) mixture. Toxicol. Appl. Pharmacol., 207, 78–88. Eriksson, P., Viberg, H., Jakobsson, E., Örn, U., and Fredriksson, A. (2002) A brominated flame retardant 2,20 ,4,40 ,5pentabromodiphenyl ether: uptake, retention and induction of neurobehavioral alterations in mice during a critical phase of neonatal brain development. Toxicol. Sci., 67, 98–103. Erdogrul, Ö., Covaci, A., and Schepens, P. (2005) Levels of organochlorine pesticides, polychlorinated biphenyls and polybrominated diphenyl ethers in fish species from Kahramanmaras. Turkey Environ. Int., 31, 703–711. Lindberg, P., Sellstr€om, U., H€aggberg, L., and De Witt, C.A. (2004) Higher brominated diphenyl ethers and hexabromocyclododecane found in eggs of peregrine falcons (Falco peregrinus) breeding in Sweden. Environ. Sci. Technol., 38, 93–96. Jaspers, V., Covaci, A., Maerovoet, J., Dauwe, T., Voorspoels, S., Schepens, P., and Eens, M. (2005) Brominated flame retardants and organochlorine pollutants in eggs of little owls (Athene noctua) from Belgium. Environ. Pollut., 136, 81–88. Sj€ odin, A., Patterson, J.R., Erson, D.G., and Bergman, A. (2003) A review on human exposure to brominated flame retardants - particularly polybrominated diphenyl ethers. Environ. Int., 29, 829–839. Marzi, T. and Beard, A. (2006) The ecological footprint of flame retardants over their life cycle. A case study on the environmental profile of new phosphorus based flame retardants. Flame Retardants 2006 Conference. Interscience, pp. 21–30. Beard, A. and Marzi, T. (2006) Sustainable phosphorus-based flame retardants: A case study on the environmental profile in view of European legislation on chemicals and end-of-life (REACH, WEEE, RoHS). CARE Innovation Conference, Vienna, November 2006. Calfee, R.D. and Little, E.E. (2003) The effects of ultraviolet-b radiation on the

17

18

19

20

21

22

23

24

25

toxicity of fire-fighting chemicals. Environ. Toxicol. Chem., 22, 1525–1531. American Society for Testing and Materials (ASTM) (2001) ASTM D6696-01, Standard Guide for Understanding Cyanide Species, ASTM, West Conshohocken, PA. Angeler, D.G. and Moreno, J.M. (2006) Impact-recovery patterns of water quality in temporary wetlands after fire retardant pollution. Can. J. Fish. Aquat. Sci., 63, 1617–1626. Angeler, D.G. and Moreno, J.M. (2007) Zooplankton community resilience after press-type anthropogenic stress in temporary ponds. Ecol. Appl., 17, 1105–1115. Cruz, A., Serrano, M., Navarro, E., and Moreno, J.M. (2005) Effect of a long-term fire retardant (Fire Trol 934 (R)) on the germination of nine Mediterranean-type shrub species. Environ. Toxicol., 20, 543–548. Luna, B., Moreno, J.M., Cruz, A., and Fern
andez-Gonz
alez, F. (2007) Effects of a long-term fire retardant chemical (FireTrol 934) on seed viability and germination of plants growing in a burned Mediterranean area. Int. J. Wildland Fire, 16, 349–359. Boulton, T.J., Moss, G.L., and Smithyman, D. (2003) Short-term effects of aerially-applied fire-suppressant foams on water chemistry and macroinvertebrates in streams after natural wild-fire on Kangaroo Island, South Australia. Hyrobiologia, 498, 177–189. Buhl, K.J. and Hamilton, S.J. (1998) Acute toxicity of fire-retardant and foam suppressant chemicals to early life stages of chinook salmon (Oncorhynchus tshawytscha). Environ. Toxicol. Chem., 17, 1589–1599. Buhl, K.J. and Hamilton, H.J. (2000) Acute toxicity of fire-control chemicals, nitrogenous chemicals and surfactants to rainbow trout. Trans. Am. Fish. Soc., 129, 408–418. Gaikowsky, M.P., Hamilton, S.J., Buhl, K.J., McDonald, S.F., and Summers, C.H. (1996) Acute toxicity of three fire-retardant and two fire-suppressant foam

References formulations to the early life stages of rainbow trout (Onkorhynchus mykiss). Environ. Toxicol. Chem., 15, 1365–1374. 26 McDonald, S.F., Hamilton, S.J., Buhl, K.J., and Heisinger, J.F. (1995) Acute toxicity of fire control chemicals to Daphnia magna (Straus) and Selenastrum capricornutum

(Printz). Ecotoxicol. Environ. Saf., 33, 62–73. 27 McDonald, S.F., Hamilton, S.J., Buhl, K.J., and Heisinger, J.F. (1997) Acute toxicity of fire-retardant and foam-suppressant chemicals to Hyalella azteca (Saussure). Environ. Toxicol. Chem., 6, 1370–1376.

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18 Loss Prevention and Safety Promotion in Industry Ales Bernatik

18.1 The Problems of Major Accident Prevention: An Introduction

In this chapter, the term “major accident” is defined as an occurrence such as a major emission, fire, or explosion resulting from uncontrolled developments during the course of operation of any establishment covered by the Seveso II Directive [1], and leading to serious danger to human health and/or the environment, immediate or delayed, inside or outside the establishment, and involving one or more dangerous substances. Although the greatest risks of major accidents follow from the handling of a wide range of chemical substances in the chemical industry, other industries also utilize a considerable number of dangerous substances and activities. A large number of these risk sources occur in small and medium-sized enterprises (SMEs), which form the backbone of the national economy. A major accident results in unfavorable effects on specific targets, namely the population, the environment, and property (Figure 18.1). 18.1.1 Accidents in the Past, and Legislation

In recent years, a number of major accidents have occurred worldwide, with perhaps the best known being Feyzin (France, 1966), Flixborough (UK, 1974), Seveso (Italy, 1976), Bhópal (India, 1984), and Houston (USA, 1989). The majority of significant accidents have been described in specialized literature in detail (e.g., Ref. [3]). Information on any major accidents that take place within the countries of the European Union is collected at the Joint Research Center of the Major Accident Hazards Bureau (MAHB) in Ispra, Italy. The Major Accident Reporting System (MARS) was established to handle the information on major accidents submitted by Member States of the EU to the European Commission, in accordance with the

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Unfavorable effects

Effects on the population – population injury – employee injury – employment loss – psychological effect – well-being loss

Effects on the environment

Economic effects

– contamination outside the establishment • air • water • soil – contamination inside the establishment • air • water • soil

– property damage – investment loss – output loss – legal liability – negative image

Figure 18.1 Unfavorable effects of major accidents associated with hazardous technological processes [2].

provisions of the Seveso Directive [4]. Currently, MARS holds data on more than 603 major accident events (Figure 18.2). Following many accidents worldwide, and especially after the accident at Seveso in 1976, the Directive 82/501/EEC on the Major Accident Hazards of Certain Industrial Activities – designated as the Seveso Directive – was prepared and issued in the framework of the EU. Subsequently, during 1996, an amendment to the Seveso Directive 96/82/EC – the Control of Major Accident Hazards Involving Dangerous Substances – was made, which was referred to as “Seveso II.” Modifications to the

Number of Accidents

60 50 40 30 20 10 0 1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Year of Occurence Figure 18.2 Number of major accidents in the European Union, as reported to the European Commission (MARS database) [4].

18.1 The Problems of Major Accident Prevention: An Introduction

Seveso II Directive during year 2003 (No. 2003/105/ES) were connected with reactions to major accidents in previous years. including the release of cyanides from a tailing pond of a mine at Baia Mare in Rumania (2000), a fire in a firework storage facility in Enschede in the Netherlands (2000), and a fire and explosion in a French factory for fertilizer production in Toulouse (2001). Examples of large-scale industrial accidents represent a warning against postponing the solution of these problems. Whilst, invariably, it can be determined from accident statistics that the most frequent cause of an accident is human error (in up to 80% of cases), a variety of the most frequent causes and consequences of accidents have been identified as: Causes:

Consequences:

.

.

. . . .

human error material defects chemical reaction other causes external influences

. . . .

water pollution toxic emissions fires air pollution explosion

Among the most frequent consequences of major accidents, fire is perhaps the most apparent, as documented by the statistics for industrial fires in the Czech Republic, which have caused considerable financial losses, injuries, and fatalities (see Table 18.1 and Figure 18.3).

Table 18.1 Statistical data relating to industrial fires in the Czech Republic [5].

Year

No. of fires

Damage (million CZK)

Fatalities

Injured

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

1138 1016 1650 1511 1368 1221 1013 975 991 955 957 817 809 883 751

83.7 311.1 314.2 528.5 302.2 930.8 75.5 317.9 1 052.3 2 688.0 433.5 545.7 529.3 649.5 852.9

1 0 4 3 2 0 0 3 2 2 3 1 1 2 2

97 85 81 123 107 149 94 91 81 53 78 67 59 93 60

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1800 1600 1400 1200 1000 800 600 400 200 0 1993 1994

1995 1996

1997 1998

1999 2000 2001 2002 2003 2004 2005 2006 2007

Figure 18.3 Numbers of industrial fires in the Czech Republic [5].

18.2 Major Accident Risk Assessment 18.2.1 General Principle of Major Accident Risk Assessment

The quantitative risk assessment (QRA) of major accidents has been utilized in a variety of Handbooks, the most significant of which have included Lees (2005) [3], CCPS (1989) [6], CCPS (2001) [7], TNO (1997) [8], and TNO (1999) [9]. When making detailed assessments of major accident risks to the population, the methods described in the following sections are those most frequently used. The primary method of integrated risk analysis is that of Chemical Process Quantitative Risk Analysis (CPQRA) [6]. This technique was developed for the needs of the chemical industry on the basis of experience in the nuclear, aircraft, and electronics industries, although its recommended procedure is equally applicable to other types of industry. CPQRA represents a tool for risk quantification and reduction by means of partial methods and procedures. A second, equally well acknowledged, approach to comprehensive risk assessment is the Dutch scheme, “CPR 18E Guidelines for Quantitative Risk Assessment,” which otherwise is known as the “Purple Book” [9]. This consists of two parts: (i) a risk assessment for stationary installations; and (ii) a risk assessment for the transport of dangerous substances. Selected text dealing with the assessment of stationary sources of risk are presented briefly later in the chapter. The method known as ARAMIS (Accidental Risk Assessment Methodology for IndustrieS, within the framework of the Seveso II directive) was developed as part of a project of the EU Fifth Framework Programme between 2002 and 2004. The suggestion was that ARAMIS would provide a harmonized methodology for risk

18.2 Major Accident Risk Assessment

assessment, the main goal being to reduce any uncertainties and variability in the results, and also to include an assessment of risk management efficiency into the analysis. Thus, ARAMIS must be considered as a global tool for carrying out effective risk identification and analysis with an entire series of preprepared and recommended steps. The methodology is freely available at: http://aramis.jrc.it [10]. In general, the following steps must be taken in a major accident risk analysis and assessment: . . . . . .

An identification of the sources of risk (hazards). The formation of possible scenarios of events and their causes that may lead to major accidents. An estimation of the effects of possible scenarios of major accidents on human health and lives, and also on the environment and property. An estimation of the probability of major accident scenarios. The determination of risk level. An assessment of the acceptability of a major accident risk.

With regards to these principles, the Ministry of the Environment of the Czech Republic has issued a recommended diagram on risk assessment for the purpose of safety reports (Figure 18.4). The safety report represents a basic document prescribed according to the Seveso II directive to industrial establishments concerned with handling dangerous substances that are present, or are likely to be present, at the establishment in quantities higher than 2% of the relevant “qualifying quantity,” and includes the results of a detailed assessment of the major accident risks. The diagram in Figure 18.4 represents a possible approach to assessing the risks of major accidents (with which the present author identifies completely). The basic steps in risk assessment are detailed in the following sections. 18.2.2 A Brief Overview of Partial Methods of Risk Analysis

The key question of risk analysis is to select a suitable method, and this is why a brief overview of the available methods is provided below. A series of methods, many of which are modified versions of previously used techniques, enables a risk analysis to be made, by incorporating 62 of the best-known methods of risk analysis [12]. At this point, it should be emphasized that the majority of these methods are designated as “partial” because they merely assist in particular steps of the entire risk analysis process. An example is in hazard identification, when assessing consequences or probability assessment (Table 18.2). These most frequently used methods have various applications, depending on the extent and complexity of the process, and consequently provide a variety of results as they differ in their demands in relation to the working team and the time available. Some methods may be interrelated or overlap each other, while others might be incomparable. The selection of a method is influenced by several factors, including the goal and type of study, the experience of the working team, the availability of the required information and, of course, the economic costs.

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Checklist of hazardous installation

Process condition

Identification of risk sources

Another standard method Information on dangerous substances, process condition, localization, etc.

Detailed information on installation, its function and effects of

Selection of risk sources for Quantitative Risk

Selection method for QRA published in Purple Book Identification of potential accident causes

External impacts Info: Purple Book, failure statistics, Reliability Date

Physical and toxic effects, consequences on people, property and environment risk probability – meteosituation

Methods: HAZOP,What-if

Analysis of human factor Formation of accident scenarios Methods: ETA, FTA Estimation of scenarios probability

Risk estimation for people, property and environment

1. No

Methods of modeling: ALOHA, Superchem, EFFECTS, TerEx, Proteus and another software Calculation of social risk

Risk assessment acceptable risk

Criteria of acceptability: Existing establish.: 10 -3 / N2 New establishment: 10 -4 / N2

End

Methods DOW: Fire & Explosion Index Chemical Exposure Index

2. No

Organization precaution on exposed population

Yes Technical precaution on risk sources

Evaluation of condition for next establishment operation by authorities

Figure 18.4 Framework of risk assessment methodology. Ministry of the Environment of Czech Republic [11].

The methods of risk assessment can be classified as either qualitative and quantitative [12]; alternatively, they may be categorized as: . . .

deterministic: based on the quantification of accident consequences; probabilistic: based on the probability or frequency of accident; and a combination of the deterministic and probabilistic approaches.

18.2 Major Accident Risk Assessment Table 18.2 An overview of the most widely used partial methods of risk analysis.

Name of method

Abbreviation

Relative Ranking Safety Review Checklist Analysis Preliminary Hazard Analysis What-If Analysis What-If/Checklist Analysis Hazard and Operability Analysis Failure Modes and Effects Analysis Fault Tree Analysis Event Tree Analysis Cause–Consequence Analysis Human Reliability Analysis

RR SR CL PHA WI WI/CL HAZOP FMEA FTA ETA CCA HRA

In general, deterministic methods are used for the analysis of the whole industrial establishment, whereas probabilistic methods are used for the analysis of a chosen part of the establishment and require a more detailed – and thus more demanding – analysis. A current trend in risk assessment is the hierarchization of results, especially in easily applicable methods; in this case, the results are presented as hazard level indices (so-called “indexing” or “screening” methods). For those risk sources that have the worst indices, it is recommended that a detailed analysis be made by using high-precision methods. A similar new approach to the risk assessment of whole industrial establishments consists of selecting the major risk sources in the first phase, whilst in the second phase a detailed QRA of the most hazardous installations is selected. Both of these approaches are aimed at limiting the number of installations to be assessed in detail within an industrial establishment, so as to simplify the entire risk analysis and to concentrate attention especially on the major risk sources. Although, as yet, a single method has not been developed to carry out the entire whole risk analysis; in practice several methods can be combined to achieve such an effect. 18.2.2.1 Selection Method The selection method according to the CPR 18E Purple Book [9] was developed to reveal those installations that contribute most to a risk. The installations selected in this way must be considered in the detailed QRA, the individual steps of the selection method being as follows:

1) 2)

3)

An establishment is divided into independent installations (separated units). A hazard related to each installation is determined on the basis of the quantity of a substance, the operational conditions, and the properties of the dangerous substances. The indication code A expresses the level of real hazardousness of the installation. A hazard related to the installation is determined for a set of points in the surroundings (on the boundary) of the establishment. The hazard related to the

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4)

installation within a certain distance is determined on the basis of a known indication number and distance between the point being assessed and the installation. The level of hazard in the point being assessed is deduced from the value of selection code S. For the QRA analysis, installations are selected on the basis of the relative value of selection code S.

18.2.2.2 Dow’s Fire and Explosion Index Over the past thirty years, the “Fire and Explosion Index” (F&E Index) has been steadily improved, to a point where it can be used to predict the relative level of risk of losses of the unit or installation being assessed, from the point of view of a possible fire and explosion. Broadly speaking, the F&E Index represents a means of classifying (in relative terms) the hazards of key units and installations. Today, the index – which is used both by the Dow Company and others – is recognized as a leader among indexing methods in the chemical industry, and is capable of furnishing key data that enables an assessment of the overall risk of fire and explosion. When using the F&E Index, the main aims are to [13]:

1) 2) 3)

quantify any expected damage as a consequence of a fire and explosion (not a maximum loss, but rather a maximum probable loss); identify those installations that could contribute to the occurrence and escalation of an accident; and present the results of the F&E Index to the management team.

If an installation has an F&E Index greater than 128, then a further risk analysis will be required. A listing of F&E Index values, compared to a description of the degree of hazard encountered, is provided in Table 18.3 [13]. 18.2.2.3 Dow’s Chemical Exposure Index The chemical exposure index (CEI) is a relatively simple method for assessing the potential exposure of people in the vicinity of chemical plants, where a realistic possibility of release of dangerous chemical substance exists. Whilst it is very difficult to determine an absolute level of risk, the CEI enables the relative comparison of various risk sources [14]. The CEI can be used for installations intended for the storage and/or treatment of toxic substances, both for new projects and existing installations. If the value of the CEI is greater than 200, then the unit will requires a further assessment of the degree of hazard. Table 18.3 F&E Index degrees of hazard [13].

F&E Index range

Degree of hazard

1–60 61–96 97–127 128–158 159–upwards

Light Moderate Intermediate Heavy Severe

18.2 Major Accident Risk Assessment

The methods of hazard and operability analysis (HAZOP), fault tree analysis (FTA), and event tree analysis (ETA) are introduced in the following sections, as these often follow screening and indexing methods to specify the possible causes of accidents, or the probability of an accident occurring. 18.2.2.4 Hazard and Operability Analysis (HAZOP) This method was developed to identify and assess process hazards, and also to identify operational problems. It is used most frequently during the course of a process and/or after its design stage; it may also be used beneficially with existing processes. For HAZOP, an interdisciplinary team (five to seven members) utilizes creative, systematic steps to reveal deviations from the project that may lead to undesirable consequences. For this, a series of predetermined words is employed – the so-called “key words” (such as “less,” “more,” “no,” “also,” “part,” “other,” “reverse,” “early,” “late”) – that are combined with process parameters. For instance, the key word “No,” in conjunction with the parameter “Flow,” gives a deviation of “No flow.” The results of team discussions are written into a table where the individual columns represent causes, consequences and protective measures for process deviations. The main disadvantages of this method are its high time consumption and labor intensity [2]. 18.2.2.5 Fault Tree Analysis (FTA) This is a deductive method that seeks out individual accidents and system failures, and determines the causes of such events. FTA is a graphical model of various combinations of failures in installations and human errors that may result in a main system failure, called a “top event.” FTA is also suitable for extensive systems, as it is able to determine a complete list of minimum failures. The model is based on Boolean algebra (gates “and,” “or,” and others) in seeking a minimum failure leading to a top event; the outcomes are types of failure and quantitatively assigned probabilities of system failures, if the probabilities of primary causes are known. The study can be carried out by one or more analysts, who can recommend safety improvements in the process. The method is not suitable for early phases of design and is also time-consuming, with the demands increasing depending upon the complexity of the system [2]. 18.2.2.6 Event Tree Analysis (ETA) This method graphically expresses the possible consequences of an accident following on from an initiation event. The outcomes are sequences of accidents, a series of failures and faults leading to an accident (system function success or failure is assessed). Sequences of accidents represent logical combinations of events, and can be transferred to a fault tree model and further quantitatively assessed. The method is suitable for the analysis of comprehensive process that has several types of safety system. Although the analysis can be carried out by one analyst, two to four analysts are often preferred. The analysts can utilize the outcomes for recommendations to reduce the probability and/or mitigating consequences of potential failures [2].

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18.2.3 Scenario Probability Assessment

In order to determine the probability of individual scenarios for major accidents, generic values stated in specialized literature based on historical data are often used. One of the most frequently used publications is the Purple Book [9], in which the events that occur during the course of an installation damage and release of a dangerous substance – the so-called loss of containment (LOC) – are described. For these cases of failures, the assumed frequencies of occurrence determined on the basis of past accidents are stated. The methodology thus presents the failures in installation that contribute to a societal risk, and must be taken into account during quantitative risk assessment. Examples of data relating to failure frequencies, together with values for pressure vessels and pipes are included in Tables 18.4 and 18.5. 18.2.3.1 Direct Ignition In order to model the consequences of the release of flammable substances, it is necessary to estimate the probability of direct ignition or delayed ignition. Examples of values of probability of direct ignition for stationary sources are listed in Table 18.6. 18.2.4 Scenario Consequence Assessment

For detailed modeling of the release of dangerous substances, and the consequences of fires, explosions and spreading toxic clouds, an entire series of computer programs can be used [15]. Among the best known of these are included ALOHA, RMP Comp, SAFETI, PHAST, EFFECTS, DAMAGE, and CHARM. Some of this software is freely available on the Internet (e.g., ALOHA, RMP Comp), but others have been produced commercially by companies concerned with risk analyses (e.g., the Dutch company TNO, for EFFECTS and DAMAGE; or the Norwegian company DNV, for PHAST Table 18.4 Failure frequencies for stationary vessels [9].

LOC for stationary vessels G.1 Instantaneous release of the complete inventory G.2 Continuous release of the complete inventory in 10 min at a constant rate of release G.3 Continuous release from a hole with an effective diameter of 10 mm Installation (part)

Pressure vessel Process vessel Reactor vessel LOC ¼ loss of containment.

G.1 Instantaneous

G.2 Continuous, 10 min

G.3 Continuous, Ø 10 mm

5
107 y1 5
106 y1 5
106 y1

5
107 y1 5
106 y1 5
106 y1

1
105 y1 1
104 y1 1
104 y1

18.2 Major Accident Risk Assessment Table 18.5 Failure frequencies for pipes [9].

LOCs for pipes G.1 Full-bore rupture – outflow is from both sides of the full-bore rupture G.2 Leak – outflow is from a leak with an effective diameter of 10% of the nominal diameter, a maximum of 50 mm Installation (part)

G.1 Full-bore rupture

G.2 Leak

Pipeline, nominal diameter <75 mm Pipeline, 75 mm  nominal diameter  150 mm Pipeline, nominal diameter >150 mm

1
106 m1 y1 3
107 m1 y1 1
107 m1 y1

5
106 m1 y1 2
106 m1 y1 5
107 m1 y1

and SAFETI). Details of the program EFFECTS are described in the following section, as an example. The Dutch EFFECTSGIS 5.5 model represents a high standard of modeling for the consequences of major accidents. The program combines two accepted models for the calculation of physical effects following the release of dangerous substances, namely EFFECTS and DAMAGE. In this situation, EFFECTS allows the manifestations of accidents to be determined, such as pressure waves, heat radiation, and gas concentration, while DAMAGE allows the consequences of accidents to be determined, such as human death rate, first-degree and second-degree burns, lung injury, eardrum damage, and others. The advantage of linking these two models into one program is that the combined system will include comprehensive calculations that cover the range from initiation physical effects to accident consequences. The results are presented in both text and graphical forms [16]; an example of the computer screen when using EFFECTS is shown in Figure 18.5. 18.2.5 Risk Acceptability 18.2.5.1 Probit Function Calculations of individual and societal risks include an assessment of the probability of human death for any given exposure. Such probability is calculated by using the

Table 18.6 Probability of direct ignition for stationary installations [9].

Source

Substance

Continuous

Instantaneous

K1-liquid

Gas, low reactivity

Gas, average/high reactivity

<10 kg s1 10–100 kg s1 >100 kg s1

<1000 kg 1000–10 000 kg >10 000 kg

0.065 0.065 0.065

0.02 0.04 0.09

0.2 0.5 0.7

Note: K1 liquid ¼ flammable liquid having a flash point <21  C and a vapor pressure at 50  C <1.35 bar (pure substances) or 1.5 bar (mixtures).

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Figure 18.5 Example of the computer screen during operation of the program EFFECTSGIS [16].

probit function, which is a type of dose–response model dependence expressed by an equation. The relationship between the probability of an effect and the exposure usually results in a sigmoid curve; however, such a curve will be replaced with a straight line if the probit is used instead of the probability. The probability of death, PE, caused by events of the type BLEVE (Boiling Liquid Expanding Vapor Explosion), Jet fire and Pool fire, together with the proportions of people affected inside and outside the buildings (FE,in and FE,out, respectively), are illustrated diagrammatically in Figure 18.6. The probability of death from heat radiation exposure is calculated by means of the probit function, given by the following relationship [9]: Pr ¼ 36:38 þ 2:56
ln ðQ 4=3
tÞ

ð18:1Þ

where: Pr is the probit function corresponding to the probability of death () Q is heat radiation (W m2) t is the exposure time (s) It should be noted that: .

BLEVE results from the sudden failure of a vessel containing liquid at a temperature well above its normal (atmospheric) boiling point. A BLEVE of flammables results in a large fireball.

18.2 Major Accident Risk Assessment

BLEVE Pool fire Jet fire

in flame envelope yes PE = 1 FE,in = 1 FE,out = 1

no Q ≥ 35 kW/m2

no

PE = f (Q,t) FE,in = 0 FE,out = 0,14 × PE

yes PE = 1 FE,in = 1 FE,out = 1

Figure 18.6 Calculation of the probability of death for exposure to Boiling Liquid Expanding Vapor Explosion (BLEVE), pool fire and jet fire [9]. . . .

The expression “in the flame envelope” means within the contours identical with the contours of lower explosion limit (LEL) at the moment of ignition. The time of exposure t is equal to the duration of the fire; however, for calculations the time of exposure is determined as 20 s as a maximum. The presumption is that people inside a building are protected from heat radiation until the building is set on fire. The ignition threshold for buildings is determined at 35 KW m2. If the building is on fire, all people inside are presumed to die in the fire. Hence, FE,in ¼ 1 if Q is >35 kW m2, and FE,in ¼ 0 if Q is <35 kW m2.

When calculating societal risk it can be assumed that, when outside the building, people are protected from heat radiation by their clothing, until the clothing is set on fire. The protection afforded by the clothing reduces the number of people who will die by a factor of 0.14, compared to no such protection. The ignition threshold for clothing is determined as 35 kW m2, and people will die if their clothing is ignited at this boundary value. Hence FE,out ¼ 1 if Q is >35 kW m2, and FE,out ¼ 0.14
PE if Q is <35 kW m2. 18.2.5.2 Calculation and Result Presentation From a comprehensive point of view, a risk is understood to be a relationship between the expected loss (health damage, loss of life, loss of property, etc.) and the uncertainty of loss considered (usually expressed by the probability or frequency of occurrence of an unexpected event). The results of a QRA are the individual risk and the societal risk (Figure 18.7): .

The individual risk represents the frequency of death of an individual in connection with a case of installation failure (LOC). It can be assumed that the individual is not protected, and will be exposed to unfavorable conditions during the whole period of exposure. The individual risk is represented by contours in a topographic map.

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Figure 18.7 Presentation of individual risk contours and societal risk curve [9]. .

The societal risk represents the frequency of such events during the course of which more persons lose their lives together. The societal risk is represented by means of F–N curves, where N represents the number of deaths, and F is the cumulative frequency of events accompanied by N or more deaths.

The criteria for determining individual and societal risks are illustrated in Figure 18.8.

ALARA

1E-05

1E-07

1E-08

ACCEPTABLE

1E-06

(b) Societa Risk Criterion 1E-2 1E-3

Frequency F (x>=N)

1E-04

NON-ACCEPTABLE

(a) Individual Risk Criterion 1E-03

1E-4 NON-ACCEPTABLE 1E-5 1E-6 ALARA 1E-7 1E-8

ACCEPTABLE

1E-9 1

10

100

1000

10000

Number of Fatalities

Note: ALARA - As Low As Reasonably Achievable Figure 18.8 Theoretical examples of criteria for (a) individual and (b) societal risk [17].

18.3 Major Accident Risk Management

18.3 Major Accident Risk Management

The need to assess and manage major accident risks follows from several factors, especially from a large number of accidents that have occurred in the past, leading to pressures to reduce risks associated with various process installations. Furthermore, this requirement is initiated by the need to prevent against accidents in landuse planning – that is, to approve the location of new establishments in relation to inhabited and protected areas, by the need to improve emergency preparations, and so on. By carrying out a risk assessment, and following measures to reduce the risk, it may be possible to contribute towards the prevention of accidents, the mitigation of accident consequences on human lives, property and the environment, and also to prevent the unsuitable location of a new establishment in the vicinity of population centers and areas protected from an environmental point of view. Risk assessment should be carried out during the preparatory phase of the building of a new establishment, and during a major accident investigation so as to prevent accident reoccurrence. Assessment should also be conducted during the phase of establishment operation when the risk assessment contributes to a better awareness on risk sources, accident consequences, and threatened target groups. Prepared accident scenarios are used to instigate improvements in emergency plans, and to prepare for effective interventions in the case of an accident. Risk assessment forms part of the comprehensive risk management in industrial establishments. The basic areas of risk management are shown schematically in Figure 18.9, from which it is evident that, in addition to risk source identification and risk assessment, it is the other activities – notably installation operation and maintenance, operator training, audits, incident investigation, and emergency planning – that contribute mainly to risk prevention and management.

Risk management program elements emergency planning

operating and maintenance procedures

risk mitigation

operator training

internal and external audits

risk assessment incident investigation

hazard identification

Figure 18.9 Diagram of elements of risk management in an industrial establishment [18].

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18.3.1 Possibilities of Reducing Major Accident Risks

On the basis of the results of a risk assessment, priorities for reducing risks can be deduced in the phase of risk management. This area is necessary in order to sustain the development of a mature society, and the basic approaches to reducing risks associated with process installations are as follows: . . . .

the replacement of a dangerous substance with a less-dangerous substance; a change in technology to become more advanced, with the presence of smaller quantities of any dangerous substances; reducing the stock of dangerous substances to a necessary minimum; and the effective separation of the quantity of dangerous substances in the establishment (e.g., by remote-controlled valves), thus decreasing the quantity of a releasing substance.

Moreover, an entire series of barriers can be used to reduce risks, to eliminate or limit risks, and to prevent the transfer of risks. In effect, the safety barriers can be subdivided into technical and organizational measures: Technical measures: These are measures in installation design that lead to an increase in operational safety (e.g., safety valves, sprinklers, automatic regulation, safety basins, double-shell containers). Organizational measures: These are measures in work organization, regulation, technological processes and procedures (including relevant technical equipment) that lead to an increase in operational safety. Yet, another division of safety barriers can be identified, namely into active, passive, and human action barriers. From this point of view, automatic systems that eliminate human factors are more suitable; examples include detection and alarm systems, automatic systems (such as blocking and stop systems), fire and explosion protection systems, protection against the release of dangerous toxic substances, further safeguards against intrusion and unauthorized manipulation, and central stations including the indication of protection system functionality. The majority of these technical safety measures are referred to as “preventive” (precautions) because they contribute to a reduction in major accident occurrence. An additional group of safety measures consists of protective means and emergency response means that mitigate and limit the consequences of a major accident. These include, in general, fixed technical means (fixed extinguishing systems, ventilation systems), mobile technical means (pumps, fans, telescopic platforms, scum boards), transport means and special mechanisms (earth-moving machines, truck tanker fuels, fire extinguishing tankers), emergency response and emergency materials, personal protective equipment, and staffing (strength of staff ready to respond). With regards to safety measures, employee alarm and warning systems, as well as systems and methods of notification of entities involved in the case of an emergency must be included.

18.5 Summary

18.4 Outlook

The need to solve the issues of major accident prevention has followed on from longterm developments in this area when, initially, stationary risk sources with the greatest content of dangerous substances were dealt with. At present, the limits on dangerous substances covered by the Seveso II directive have been reduced such that, when attention is turned simultaneously towards mobile risk sources, the number of accidents that occur while transporting dangerous substances has grown. The next phase is surely to pay greater attention to those risk sources that are unclassified within the context of the Seveso II directive but which may, owing to their location, represent significant societal risks.

18.5 Summary

This chapter has detailed the issue of major accident prevention, first by proposing possible approaches to quantitative risk assessment, and then by focusing on systems of risk management in industrial establishments. The aim of major risk prevention is to operate process installations at an acceptable societal level. Risk acceptability limits can differ depending on the maturity of the state, the size of establishments, and a number of other factors which must, nonetheless, be included into any analysis and assessment of risk. Hence, the benefits of carrying out risk assessment may be summarized: .

. . . . .

.

Risk assessment provides information on the identification of hazards towards possible targets of impact. Such targets may be the employees and the installations of the establishment, the surrounding human population, and the environment. Information may be presented on possible preventive means and risk reduction priorities. The preparedness for accidents is assessed, providing a source of information for the preparation of emergency plans. The conditions of existing legislation may be satisfied. Advantages can be ensured when negotiating agreements with insurance companies. A contribution may be made to the prevention of accidents, thus reducing the costs of mitigating accident consequences, of compensation to affected people, and paying penalties should the environment become polluted. The suitable publication of the results of risk assessments will lead to an increased awareness of employees and the general population. Moreover, the image of the establishment would receive a major boost.

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References 1 Commission of the European Com-

2

3

4

5

6

7

8

9

munities (1996) Council Directive On the Control Major Accident Hazard of Industrial Activities, 96/82/EC. Center for Chemical Process Safety (1992) Guidelines for Hazard Evaluation Procedures, AIChE, New York, ISBN: 0-8169-0473-1. Mannan, S. (ed.) (2005) Lees’ Loss Prevention in the Process Industries, Hazard Identification, Assessment and Control, 3rd edn, Elsevier, ISBN: 0-7506-7555-1. Major Accident Hazards Bureau (MAHB) (2008) Major Accident Reporting System (MARS) Data. Available at: http:// mahbsrv.jrc.it. Ministry of Interior (2007) Statistical Yearbook. Ministry of Interior, General Directorate of Fire & Rescue Service of the Czech Republic. Available at: http:// aplikace.mvcr.cz/archiv2008/english. American, Institute of Chemical Engineers (1989) Guidelines for Chemical Process Quantitative Risk Analysis CPQRA, Center for Chemical Process Safety of the American, Institute of Chemical Engineers, New York. ISBN: 0-8169-0402-2. American Institute of Chemical Engineers (2001) Layer of Protection Analysis: Simplified Process Risk Assessment, American Institute of Chemical Engineers CCPS, New York. Yellow Book (1997) Methods for the Calculation of Physical Effects Due to Releases of Hazardous Materials (Liquids and Gases), CPR 14E, 3rd edn, TNO. Purple Book (1999) Guidelines for Quantitative Risk Assessment, CPR 18E, TNO, The Hague.

10 ARAMIS (2004) Accidental Risk

11

12

13

14

15

16

17

18

Assessment Methodology for Industries in the framework of the SEVESO II directive, User Guide, contract number: EVG1-CT-2001-00036, December. Available at: http://aramis.jrc.it.  (2001) Kostra metodologie MZP hodnocenı rizik zavazne havarie pro zpracovanı bezpecnostnıho programu. Available at: http://www.mzp.cz. Tixier, J., Dusserre, G., Salvi, O., and Gaston, D. (2002) Review of 62 risk analysis methodologies of industrial plants. J. Loss Prevent. Proc., 15, 291–303. AIChE (1994) Manual – Dow’s Fire & Explosion Index, Hazard Classification Guide, 7th edn. AIChE (1994) Manual – Dow’s Chemical Exposure Index, 1st edn, AIChE, New York. Bernatik, A., Zimmerman, W., Pitt, M., Strizik, M., Nevrly, V., and Zelinger, Z. (2008) Modelling accidental releases of dangerous gases into the lower troposphere from mobile sources. Process Saf. Environ., 86 (B3), 198–207, ISSN: 0957-5820. TNO (2004) Program EFFECTSGIS 5.5., TNO Environment, Energy and Process Innovation, The Netherlands. Available at: www.mep.tno.nl. Christou, M.D., Struckl, M., and Biermann, T. (eds) (2006) Land Use Planning Guidelines in the context of Article 12 of the Seveso II Directive 96/82/EC as amended by Directive 105/ 2003/EC, JRC. Babinec, F. (2001) Bezpecnostnı inzenyrstvı (learning text), VUT Brno.

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19 Fire Safety Michael A. Delichatsios

19.1 Introduction

The widespread use of performance-based codes and design methods for buildings and infrastructure necessitates prediction of the consequences and likelihood of fire in order to assure life safety, to reduce property losses, and to limit environmental damage. Such prediction requires reliable information concerning the growth and spread of the fire, the environment within which the fire occurs, and the interactive response to the fire of people, of the fire safety systems, and of the structure or in general the designed object. In order to predict the influence of fire on people, fire safety systems, structure and the environment, information is required concerning the ignition, fire spread and fire growth, heat fluxes and products of combustion and their dispersion. Interaction with fire safety systems requires knowledge of the function and reliability of the systems within the specified environment, together with the quality and reliability of any procedural or human intervention. Human behavior can have a major impact on the outcome of fire events, and knowledge is needed of the population, its distribution and response, as well as the likely behavior of those involved in all aspects of intervention, system maintenance and management (engineering and procedural). Finally, the geometry and operational characteristics of the designed object (e.g., building, infrastructure) and its environment are necessary inputs to the implementation of any performance prediction. Where risk-based calculations are performed, additional data are needed. As well as predicting the immediate consequence of a fire event in terms of the tangible damage caused by the impact of heat and products of combustion, a risk assessment might examine the likely frequency of the event, the possible impact of the fire on the community (e.g., amenity disruption, business interruption) and the environment,

Handbook of Combustion Vol.1: Fundamentals and Safety. Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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and also recommend control or mitigation measures that take into account a broad range of issues including costs, perceptions, and social acceptance. Reliabilities and uncertainties related to properties might be required, together with specifications of distributions and statistical (or predicted) frequencies. Where risk calculations are based on absolute rather than comparative levels of risk, data on acceptance criteria are needed. It is not always possible, nor desirable, to obtain data that are specific to the designed object. Often, the fire engineer will require generic data that will ensure that the designed object is suitable for a range of activities or uses (e.g., the product from a factory might change), or will wish to select appropriate values without knowing the exact parameters (e.g., the heat release for a fire in an office). An extreme case of the need for generic values is in human behavior, where the population and its response can be specified through a probabilistic approach. To implement the required calculations and analysis, data should be available from measurements in real fires, in experiments and tests, from computer models, from reliability assessments, from statistical records, from fire incident reports, and from surveys. The data should be readily accessible, and their source, reliability and limitations should be acknowledged.

19.2 Classification of Data

Guidance for classifying the data is given in Figure 19.1, which illustrates the dependencies and interactions of the components involved in fire safety engineering (FSE). The aim of this technical specification is to provide a list of data, possible sources and some comments related to the adequacy of these data, as well as some suggestions for future needs. Thus, the following classification of data needed for FSE is proposed: (A) Specification of the designed object (see Section 19.3.1) A.1 Geometry A.2 Construction

Figure 19.1 Dependencies and interactions.

19.3 List of Data

A.3 Function A.4 Environment. (B) Fire physics and chemistry (see Section 19.3.2) B.1 Fire spread and fire growth, heat fluxes and toxicity B.1.1 Ignition/extinction B.1.2 Fire spread and fire growth B.1.3 Heat fluxes B.1.4 Combustion products: smoke and toxic gases. B.2 Dispersion of fire products (effluents) B.2.1 Interaction with designed object building geometry B.2.2 Interaction with passive systems (containment) B.2.3 Interaction with active systems B.2.3.1 Detection B.2.3.2 Alarm B.2.3.3 Sprinklers and other methods B.2.3.4 Smoke management. B.2.4 Interaction with the wider environment. (C) Human behavior/Human factor (see Section 19.3.3) C.1 Occupant response C.2 Designed object preparedness for fire. (D) Risk assessment (see Section 19.3.4) D.1 Hazard identification D.2 Consequence severity and likelihood D.3 Control and mitigation D.4 Acceptance criteria. Table 19.1 provides three-column data where, for each category, the left-hand column describes the type of data (including a list of parameters or properties for which data are or should be currently available), and the center column describes the source of the data, including available test methods. The comments in the right-hand column are limited to issues which are considered essential for those wishing to use the data.

19.3 List of Data 19.3.1 Part A: Specification of the Designed Object

For a new object, information on its design will be provided to the fire engineer by the design team. The latter will include the client, the designer or architect, various specialist consultants and representatives of the approval authority, the owner’s insurance company, and the fire brigade. For an established object, information will be available from the owner, the designer, the manager and the occupier, or by survey (see Table 19.1).

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Table 19.1 Specifications of designed objects.

Type of data

Source

Comments

Occupancy

Design team

1 Classification

Local building codes

The information given in this section is for guidance only, and is not exhaustive. The nature of the information required will depend on the objective of the study, and sources of information vary from job to job and country to country [1, 9–11] Additional information will be needed to integrate FSE with the other building services to address sustainability issues.

2 Function 3 Special usage Location 1 Proximity to other buildings/ objects 2 Proximity to fire station 3 Topography 4 Unusual features Size, shape and configuration 1 Number of floors 2 Floor footprint, layout and height 3 Access routes and facilities 4 Ventilation and airflows

Manager and occupier

Structure 1 Structural design details, including design loads 2 Construction methods 3 Construction materials 4 Voids, shafts, ducts, openings 5 Unusual features Ignition sources 1 Electrical 2 Heating appliances 3 Special hazards Linings and contents

Fire safety provisions

Design team Design plans Local plans Survey office Manager and occupier Design team Specialist consultants Design plans Manager and occupier CAD (computer-aided design) Design team Construction Standards Design plans Designer

Design team Specialist consultants Design plans Design team Manager and occupier Design plans Design team Specialist consultants Design plans System design standards Manager and occupier

19.3 List of Data Table 19.1 (Continued)

Type of data

Source

Management and use 1 Inspections and replacements 2 Emergency procedures

Design team Manager and occupier Emergency service providers Schedules, plans and procedures Design team

3 Staff training Maintenance – frequency and adequacy

Environmental conditions 1 Prevailing wind patterns 2 Precipitation patterns Financial value 1 Capital value 2 Replacement value 3 Community value Social value 1 Heritage 2 Source of employment 3 Local amenity 4 Emergency mitigation Limitations 1 Planning requirements 2 Building requirements 3 Insurance requirements 4 Occupational health and safety requirements 5 Environmental impact

Comments

Manager and occupier Maintenance standards Maintenance schedules National survey office Specialist consultants Internet GIS reports Design team Economic consultant Owner and Manager System designers, manufacturers Design team Local government representative Owner and Manager Specialist consultants Relevant statutory authority Relevant regulations, codes and standards Insurance company

19.3.2 Part B: Fire Physics and Chemistry

Data for fire physics and chemistry can be divided into two distinct categories: . .

Data on the state of the fire Data on the dispersion of fire products.

Data on the state of the fire are further broken down into four categories, namely ignition and extinction, fire spread, fire growth, heat fluxes, and products of combustion (causing obscuration and toxicity). The dispersion of fire products is

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affected by interactions with a number of different systems that are categorized in terms of: . . . .

the spatial design of the object; passive resistance to fire spread; active resistance to fire spread; and the wider environment beyond the designed object and adjacent structures.

19.3.2.1 Fire Spread and Fire Growth, Heat Fluxes, and Products of Combustion 19.3.2.1.1 Ignition/Extinction in Table 19.2.

Data relating to fire ignition and extinction are listed

19.3.2.1.2 Fire Spread and Fire Growth Data relating to fire spread and fire growth are listed in Table 19.3. 19.3.2.1.3 Heat Fluxes

Data relating to heat fluxes are listed in Table 19.4.

19.3.2.1.4 Combustion Products: Smoke and Toxic Gases Data relating to combustion products such as smoke and toxic gases are listed in Table 19.5. 19.3.2.2 Dispersion of Fire Products 19.3.2.2.1 Interaction with Designed Object (Building) Geometry Data relating to interaction with designed object (building) geometry are listed in Table 19.6. 19.3.2.2.2 Interaction with Passive Systems (Containment) Data relating to interaction with passive systems (containment) are listed in Table 19.7. 19.3.2.2.3 Interaction with Active Systems systems are listed in Table 19.8.

Data relating to interaction with active

19.3.2.2.4 Interaction with the Wider Environment the wider environment are listed in Table 19.9.

Data relating to interaction with

19.3.3 Part C: Human Behavior/Human Factors

Human factors that need to be considered in fire engineering can be considered in two groups: . .

the response of those exposed to or involved in a fire; and the human element involved in the preparedness of the designed object for a fire situation [1, 5, 6, 10, 12].

19.3 List of Data Table 19.2 Ignition/extinction.

Type of data

Source

Comments

Flaming self-ignition

1 Cone calorimeter

Parameters or properties may include

2 Controlled-atmosphere apparatus

1 Thermal properties

3 LIFT apparatus

In general, ignition time is measured at different heat fluxes. In some advanced apparatus (TGA and controlled-atmosphere apparatus), burning rates and pyrolysis rates are measured at reduced oxygen concentrations. Mathematical models of various complexity are used to deduce properties.

2 Thermal response parameter 3 Ignition time 4 Ignition temperature 5 Critical mass flux 6 Critical heat flux 7 Pyrolysis kinetics 8 Gaseous kinetics

4 `Intermediate scale apparatus 5 SBI test 6 EFH apparatus (Australia) 7 TGA; DSC 8 Mathematical models

Flaming piloted ignition Similar list of parameters and properties are included as for flaming self-ignition. Ignition to smoldering Similar list of parameters and properties are included as for flaming self-ignition. Extinction Similar list of parameters and properties are included as for flaming self-ignition plus: Critical mass flux for extinction Premixed gases ignition Mixture temperature Flammability limits

Similar to above

Same comments apply as for flaming self-ignition

Similar to above

Same comments apply as for flaming self-ignition

Similar to above

Extinction parameters are measured in controlledatmosphere apparatus.

a)

Explosion sphere

EFH ¼ early fire hazard; DSC ¼ differential scanning calorimetry; LIFT ¼ lateral ignition and flame spread; SBI ¼ single burning item; TGA ¼ thermogravimetric analysis.

19.3.3.1 Occupant Response Data relating to occupant response are listed in Table 19.10. 19.3.3.2 Designed Object Preparedness for Fire Data relating to designed object preparedness for fire are listed in Table 19.11.

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2 ISO room corner

3 SBI

Properties and parameters measured or deduced:

1 Ignition parameters shown in Table 19.2 2 Mass burning rates 3 Heat of pyrolysis 4 Mass pyrolysis rates 5 Actual heat of combustion 6 Radiative fraction 7 Stoichiometric ratio 8 Upward flame spread rates 9 Lateral flame spread rates 10 Fuel geometry (pool, rack storage, vertical wall, corner, flooring materials, external facades) 11 Enclosure geometry and openings 12 Vitiation effects

a)

1 Ad hoc tests

Heat release rate history

FRP ¼ flooring radiant panel. Other abbreviations as Table 19.2.

13 Mathematical models

4 Cone calorimeter 5 Controlled-atmosphere apparatus 6 LIFT apparatus 7 Intermediate-scale apparatus 8 SBI 9 EFH 10 Factory mutual global approval tests 11 FRP test 12 E-84 flame spread tunnel test (Underwriters’ Lab)

Source

Fire spread and fire growth.

Type of data

Table 19.3

Specifying the heat release rate is an important challenge for fire safety engineering. Interpretation of data from tests and mathematical models is found in literature and also in ISO technical committee TC 92, including the validity and uncertainty on some of these data.

Comments

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19.3 List of Data Table 19.4 Heat fluxes.

Type of data

Source

Comments

Heat fluxes

1 Ad hoc tests and measurements

All data from Table 19.3 are useful here and more specifically: 1 Heat release rate 2 Fuel geometry

2 ISO room test

Full-scale tests and intermediate-scale tests provide data to validate mathematical models, which use as inputs measurements from small-scale tests. Soot concentrations can be estimated using a smoke apparatus and appropriate modeling.

3 Radiative fraction 4 Vitiation effects In addition, the following parameters are desirable: 5 Effective radiation temperature 6 Soot concentrations inside flames a)

3 SBI 4 Intermediate scale apparatus 5 Smoke apparatus 6 Mathematical models.

SBI ¼ single burning item.

Table 19.5 Smoke and toxic gases.

Type of data

Source

Comments

All data from Tables 19.2 and 19.3 are needed.

1 Ad hoc tests and measurements

Additionally, the following data are essential:

2 ISO room test

1 Smoke yield

3 SBI test

Full-scale tests and intermediate-scale tests provide data to validate mathematical models, which use as inputs measurements from small-scale tests. Soot concentrations can be estimated using a smoke apparatus and appropriate modeling. ISO Technical Committee 92 provides documentation of the quality of these measurements.

2 Toxic gases yield 3 Toxicity potency 4 Vitiation effects

4 5 6 7 8 9

Intermediate-scale apparatus Smoke apparatus Cone calorimeter Controlled-atmosphere apparatus Tube furnace Mathematical models

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Table 19.6 Interaction with the designed object.

Type of data

Source

Comments

1 Fuel type

1 Ad hoc tests Mathematical modeling

These data are needed for dispersion calculations using any kind of calculation methods, simple or computational, such as: 1 Well-mixed 2 Zone

2 Fuel geometry 3 Enclosure geometry including ventilation The rest of the data are provided from Section 19.3.1, Tables 19.3, 19.4, and 19.5 including generic design fires

3 CFD [15]

4 LES [15] a)

CFD ¼ computational fluid mechanics; LES ¼ large eddy simulation.

Table 19.7 Passive systems.

Type of data

Source

Specification of designed object Data defined in Section 19.3.1 (Part A) Heat fluxes on building elements. All data defined in Table 19.4 Smoke and combustion product movement and density Data defined in Tables 19.5 and 19.8D Thermal parameters of combustible and noncombustible elements Mechanical and integrity properties of combustible and noncombustible material Design loads for the building, such as mechanical, wind and snow

As defined in Section 19.3.1

Comments

As defined in Table 19.4

This information is input for evaluating fire resistance and integrity

As defined in Tables 19.5 and 19.8D

These data are input for evaluating the need for barriers to smoke and toxic products

Provided in [1, 2, 10–12] including tests and mathematical modeling. Provided in [1, 2, 10–12] including tests and modeling. Provided in Section 19.3.1 (Part A)

.

19.3 List of Data Table 19.8 Active systems.

Type of data

Source

Comments

A Detection

1 Ad hoc tests

From Section 19.3.2.1 all information is available for calculating the environment around a detector

1 Fire signature (velocity, temperature, smoke and gas concentration near the device). These data are provided in Table 19.6

2 Mathematical modeling

2 Detector characteristics: sensitivity, geometry, location

3 Detector response characteristics are provided in ISO Technical Committee TC21 4 Product standards 5 Product literature Provided by ISO Technical Committee TC21

B Alarm

Alarm characteristics

C Sprinklers and other methods 1 Fire signature (velocity, temperature, smoke and gas concentration near the device). These data are provided in Table 19.6 2 Extinction properties of fuel provided in Table 19.2 3 Detector characteristics: sensitivity, geometry, location 4 Modification of fire by action of protection system

D Smoke management including ventilation 1 Fire signature (velocity, temperature, smoke and gas concentration in the enclosure). These data are provided in Table 19.6 2 Smoke management device characteristics. 3 Effects on fire growth

Include: – Product standards – Product literature 1 Ad hoc tests 2 Sprinkler (or other protection) response characteristics are provided in ISO Technical Committee TC21 3 Factory mutual tests for sprinkler interaction with fire 4 Mathematical modeling of sprinkler fire interactions Include: – Product standards – Product literature 1 Ad hoc tests

2 Smoke management response characteristics are provided in ISO Technical Committee TC21 3 Mathematical modeling including fire interactions Include: – Product standards – Product literature

Alarm is activated manually or by the detectors.

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Table 19.9 Interaction with the environment.

Type of data

Source

Comments

Smoke and combustion product volume and density

As defined in Sections 19.3.1 (Part A), Tables 19.5 and 19.8D

These data are input for evaluating the movement of smoke and toxic products of combustion in order to calculate exposure of people, flora and fauna to toxic and hazardous products.

Data relating to topography and environmental conditions defined in Section 19.3.1 (Part A)

Mathematical modeling of chemical fires

Data defined in Tables 19.5 and 19.8D Sensitivity of environment to heat, smoke and toxic products

WHO standards National standards Tests Mathematical modeling

In sensitive environments this might include data for evaluating long-term effects of the impacts of fire on, for example, vegetation regrowth and subsequent changes to surface water flows

19.3.4 Part D: Risk Assessment

The data required for risk assessment is generally the same as that required for simple fire engineering methods. However, for risk assessment calculations of uncertainties will be taken into account, and probability distribution functions of the model input data might be required. Where data on system reliability are not available, statistical data are used to derive event frequencies. 19.3.4.1 Hazard Identification Data relating to hazard identification are listed in Table 19.12. 19.3.4.2 Consequence Severity and Likelihood Data relating to consequence severity and likelihood are listed in Table 19.13.

19.3 List of Data Table 19.10 Occupant response.

Type of data

Source

Comments

Designed object specification, including width of openings.

These data are provided in Section 19.3.1 (Part A)

Occupant characteristics:

Designed object characteristics (staff training, specific uses e.g., no. of cinema seats) from Section 19.3.1 (Part A)

Details of escape routes, especially widths, lengths, numbers of doors and stairways are needed for evacuation calculations Occupant characteristics might be those of the general community or influenced by the building use and staff training

– Densities and distribution – Familiarity with designed object – Alertness, responsiveness – Mobility – Walking speed – Social affiliation – Physical limitations Detection time

Surveys National Building Codes

Alarm time

Recognition time

For automatic detection, data from Table 19.8A For manual detection, data from Tables 19.3, 19.4, and 19.5 Surveys Statistics For automatic alarm, data from Table 19.8B For manual alarm, data from surveys, statistics, mathematical models For automatic alarm, data from Table 19.8B Designed object characteristics from Section 19.3.1 (Part A) Surveys Statistics Social studies Mathematical modeling

The nature of the detection and the presence and training of fire wardens will influence the detection time. It might be necessary to consider multiple cues. Where the alarm is given manually, there might be a delay between detection and alarm Recognition time will depend on the nature of the alarm, object characteristics (e.g., building classification) and occupant characteristics (e.g., asleep, awake)

(Continued)

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Table 19.10 (Continued)

Type of data

Source

Comments

Response time

Designed object characteristics from Section 19.3.1 (Part A) Surveys Statistics Social studies Mathematical modeling

Travel time – On level – Stairs – Slopes – Queuing

Mathematical modeling Surveys Evacuation drills and studies

There is often a delay after cue recognition before occupants start to escape. The delay will depend on the building use, the occupant characteristics (including their relationships to each other) and the nature of the cue. Once occupants start to move towards an exit, movement time lends itself to mathematical modeling. Travel time can be influenced by information provided by the communication system.

19.3.4.3 Control and Mitigation Data relating to control and mitigation are listed in Table 19.14. 19.3.4.4 Acceptance Criteria Data relating to acceptance criteria are listed in Table 19.15.

19.4 Risk Analysis and Fire Protection Engineering Based on Software Agents

FireSERT at the University of Ulster has developed a methodology to simplify quantitative risk assessment by generating one of its components – event trees including probabilities and consequences – automatically. Event tree generation is the most robust method for analyzing safety problems, being widely used in areas such as the nuclear industry, offshore oil and gas industries, and the chemical industry. However, the process of producing event trees for quantitative risk assessment is a laborious process. Figure 19.2 illustrates the development of automated event trees for fire risk assessment, using a commercial agent-based model program. In this research, the practice of examining only the limited range of design fires that could lead to a worst-case scenario is extended so as to include as many different fires as possible. The process of including all possible fires and variation of parameters guarantees that uncertainties can also be taken into consideration. More ambitious and advanced have been the studies of Professor U. Ruepell [14], at the University of Darmstadt, where an agent enabled model integration of fire protection in the building industry is being developed.

19.4 Risk Analysis and Fire Protection Engineering Based on Software Agents Table 19.11 Preparedness for fire.

Type of data

Source

Comments

Fire safety system maintenance effectiveness

Data on designed object maintenance schedules and procedures from Section 19.3.1 (Part A)

Human error is an integral part of the assessment of management procedures

Designed object characteristics (staff training procedures) from Section 19.3.1 (Part A) Manufacturer’s literature Surveys Designed object characteristics from Section 19.3.1 (Part A) Condition of escape routes from Section 19.3.1 (Part B) Fire brigade records Local traffic conditions Statistics

The presence of effective wardens and explicit instructions can reduce premovement and travel times

– Active and passive systems Warden/EWIS effectiveness

Fire brigade effectiveness

– Brigade alarm time – Brigade travel time – Brigade procedures

On arrival at the fire scene, the brigade will supervise and expedite evacuation procedures

Table 19.12 Hazard identification.

Type of data

Source

Comments

Ignition sources and ignition frequency data

Data as identified in Section 19.3.1 (Part A) Recognized reliability databases Plant/operation records Statistics Data sources as defined in Section 19.3.1 (Part A) Ignition properties as identified in Table 19.2 Chemical Material Safety Data Sheets Plant/operation records Access security Statistics

Data on reliability of specific ignition sources are not always available

Fuel sources and likelihood of ignition

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Table 19.13 Consequences.

Type of data

Source

Comments

Inputs to consequence characterization prediction models

Data as identified in Parts A (Section 19.3.1), B (Section 19.3.2), and C (Section 19.3.3)

Scenario frequency data – probabilities of failure of all parts of the fire safety system design Likelihoods of occupancy scenarios

Statistics

For detailed, quantified risk assessment ranges of values (e.g., material properties, sprinkler response times) and their distributions are required but are not always readily available. System failure frequency can be estimated using fault tree analysis and component failure rates

Manufacturer’s literature Recognized reliability databases

Table 19.14 Control and mitigation.

Type of data

Source

Comments

Control and mitigation design data

Data as identified in Parts A (Section 19.3.1), B (Section 19.3.2), and C (Section 19.3.3)

Cost data Perceived risk

–

Beyond scope of this chapter Beyond scope of this chapter

Table 19.15 Acceptance criteria.

Type of data

Source

Comments

Risk to life Human exposure criteria – death, injury; fire, explosion, hazardous chemicals Environmental exposure criteria – fire, explosion, hazardous chemicals

Statistics [1, 5, 6, 10–12] [1, 5, 6, 10–12]

– –

[1, 5, 6, 10–12]

–

19.5 Conclusions

In recent years, much progress has been made in the scientific and engineering design of fire safety in the built environment. Specifically, technologies and methods developed over the past thirty years can now be effectively incorporated into fire safety

References

Figure 19.2 Graphical representation of the risk assessment model for a simplified system (smoke control and evacuation can be included as additional agents).

design This chapter has illustrated the need for data, and has listed the data required to evaluate the components of FSE (as shown in Figure 19.1), to ensure that building fire safety is reliable, and that costs are reasonable. It is fortunate that the availability of several barriers to mitigate the frequencies and consequences of fire, although making fire safety a complicated science, allows for an efficient fire safety environment based on the tremendous progress over the past thirty years. This development provides a freedom of design while simultaneously advancing levels of safety.

References 1 DiNenno, P. and Beyler, C. (2002) The

2 3 4 5

6

SFPE Handbook of Fire Protection Engineering, 3rd edn, NFPA. Babrauskas, V. (2001) Ignition Handbook, SFPE, USA. Drysdale, D. (1998) Introduction to Fire Dynamics, John Wiley & Sons, Inc. Quintiere, J. (2006) Fundamentals of Fire Phenomena, John Wiley & Sons, Inc. Yung, D. (2008) Principles of Fire Risk Assessment in Buildings, John Wiley & Sons, Inc. Hasofer, A., Beck, V.R., and Bennetts, I.D. (2006) Risk Analysis in Building Fire Safety Engineering, Elsevier.

7 Horrocks, A.R. and Price, D. (2001) Fire-

8

9

10 11

Retardant Materials, CRC Press, Woodhead Publishing. Karlsson, B. and Quintiere, J. (1999) Enclosure Fire Dynamics (Environmental and Energy Engineering), CRC Press. British Standards Institute (2008) BS 9999:2008 Code of practice for fire safety in the design, management and use of buildings, British Standards Institute. International Standards Organization (2008) TC 92 Fire Safety engineering. Australian Building Control Board (2005) International Fire Engineering Guidelines, ABCB, Australia.

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12 Rasbash, D., Ramachandran, G., Kandola,

B., Watts, J., and Law, M. (2004) Evaluation of fire safety, John Wiley & Sons, Inc. 13 Morgan, A. and Wilkie, C. (2009) Fire Retardancy of Polymeric Materials, 2nd edn. 14 R€ uppel, U. (ed.) (2007) Vernetztkooperative Planungsprozesse im

Konstruktiven Ingenieurbau – Grundlagen, Methoden, Anwendungen und Perspektiven zur vernetzten Ingenieurkooperation. Springer-Verlag, Berlin. 15 Yeo, G.H. and Yuen, K.K. (2009) Computational Fluid Dynamics in Engineering, Elsevier, Inc.

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20 Smoke Spread in Buildings Lizhong Yang and Laixi Wu

20.1 Introduction

In general terms, when a building catches fire there are three ways in which people may be either hurt or killed: (i) as a result of extra-high temperatures; (ii) through asphyxia caused by extra-low oxygen concentrations; and (iii) via exposure to carbon monoxide (CO) [1]. (Please note: additional information relating to fire safety is provided in Chapter 22.) Within a fire scenario, however, the ways in which people are hurt or killed will differ greatly at different positions within the building. When a building is on fire, it can be separated into two fields: (i) the firesource-field, which is the room where the fire actually occurred; and (ii) the non-firesource-field, which may include long passages and remote rooms that are interconnected with the firesourcefield by a variety of openings. The coupling of the three routes by which people may come to harm represents the mechanism of the firesource-field that is influenced directly by combustion. Consequently, the grade of risk will depend on various factors that influence the combustion, including (among others) the venting, the fuel, the position of the firesource, and the room size. In the non-firesource-field, which includes the long passage and remote rooms, the temperature will be lower, as proven by bench-scale (1 : 4) experiments [2], whereas the oxygen concentration will be relatively high such that people would not necessarily die. Unfortunately, however, the concentrations of toxic gases such as carbon monoxide (CO) and sulfur dioxide (SO2) may be sufficiently high as to prove fatal, and the presence of toxic gases is clearly the main cause of death in the non-firesourcefield. An example of this occurred in a fire at the Hillhaven nursing home in Norfolk, Virginia in October 1989, when a total of thirteen people were killed, eleven of whom were subsequently shown to have died from CO intoxication. Importantly, the factors that influence risk grading in non-firesource-fields are quite different from those in the firesource-field, and include the spatial distribution of toxic gases and the structure of the building, as well as various barriers and openings. (Please note: details of CO toxicity are provided in Vol. 2 Ch. 13.) Clearly, the harmful mechanisms

Handbook of Combustion Vol.1: Fundamentals and Safety Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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and major factors that influence risk grade differ widely between the firesource-field and non-firesource-field. In recent years, many studies have been conducted on the non-firesource-field [3, 4], with in particular the group at the NIST (National Institute of Standards and Technology) having investigated the transport of high CO concentrations to locations remote from the burning compartment [5]. Smoke movement in the long passage (e.g., the tunnel) has also been extensively studied [6–8], with some having tried to predict CO concentrations in compartment fires [9]. Likewise, the dynamics of smoke movement and management have been investigated worldwide, with both zone and field models having been developed to demonstrate smoke evolution in fires. As a consequence, a number of reports have been made regarding smoke and toxic gas movements in the compartment and adjacent corridor in both small-scale and full-scale tests [10–13]. For example, Lattimer [10] and Wieczorek [11] each conducted experiments with the compartment in a side-on configuration to the hallway, by varying the opening size, the air inlet duct diameter, fire size, and the soffit height of the inlet and exit. Based on the results obtained, it was concluded that the existence of a deep oxygen-deficient upper-layer in the adjacent space would contribute significantly to the transport of fatally high levels of CO. It was also noted that, in the side-on configuration, a spatially nonuniform distribution of CO existed in the adjacent space, while higher CO levels were measured along the wall opposite the compartment. The corner vortices were also seen to impact on the oxidation of CO in the adjacent space. Later, Soo-Young et al. [13] investigated the characteristics of smoke movement in office buildings, aiming to analyze the influence of fire-induced toxic gas on the building occupants when involved in a fire evacuation. For this, tests were carried out in five compartment rooms and an adjacent corridor that was 20 m long. A downward movement of the smoke layer was detected synchronously only after the smoke front had reached the end of corridor, which indicated that the structure and the flow mode had greatly influenced the smoke movement. Subsequently, Hu et al. [14] performed full-scale tests in a 100 m-long underground corridor with both beamed and smooth ceilings. The results showed that the smoke was exhausted more efficiently when the air-supply openings were some distance from the smoke exhaust openings. In contrast, when the air-supply and smoke exhaust openings were close together, the smoke control was very poor, even when the exhaust rate was much higher. Despite many studies having been completed to date, non-firesource-fields remain largely understudied. Although the influence of vents on smoke concentration has been studied previously [15], the effects of openings on the long passage and remote room have been investigated only minimally. Consequently, a bench-scale (1 : 4) experimental facility, which was based on a typical building structure in China and had the configuration of a “fire room-long passage-remote room,” was designed. A series of experiments was then conducted to investigate not only the spatial distribution rules of smoke but also the effect of various openings on smoke distribution. For this, the Fire Dynamics Simulator software package, as a numerical simulation, was applied to recently reported recurring full-scale test results, and further investigations were carried out. In this way, the characteristics of smoke

20.2 Experimental Facilities

movement could be monitored by using a numerical simulation, and the data compared directly with experimental measurements. Although the risk analysis of building fires is currently a “hot spot” of research [16–18], very few studies have been conducted on risk analyses of the non-firesourcefield. Hence, the idea of an “index of toxicity” was introduced and used to analyze such risks in non-firesource-fields.

20.2 Experimental Facilities

The bench-scale (1 : 4) experimental facility, which is shown schematically in Figure 20.1, consisted of a fire room, a remote room, and a long passage that simulated a long corridor (or equivalent) in a building. The interior dimensions of the fire room were 70 cm (length)
70 cm (height)
90 cm (width), while the walls were 7.5 cm thick and constructed from steel plate with an asbestos filling to insulate against the heat. A round opening (A) of diameter 13 cm was located on the foreside of the fire room, with the center of A located 7.5 cm from the floor. At the right-hand side of the fire room was a heat-resistant glass window through which the experiments could be observed. An operational window (F), which could be sealed during the tests, was located to the left-hand side of the fire room. The size of opening B, which connected the fire room and the long passage, was 23 cm (width)
45 cm (height). The long passage was constructed from 3 mm-thick steel plate, and was 40 cm wide
80 cm high
430 cm long. The end of the passage, near to the fire room, was sealed completely. At the other end of the passage was opening D; this was 15 cm wide, but its height could be changed from 0 cm to 40, 50, 60 or 70 cm, during the experiments.

L4 E

Long passage

Fire Room

C

L1

L2

Remote Room

L3

D

B G

F

A

E D

F

c

Remote Room

A Long passage

B

Fire Room

Figure 20.1 A sketch map of the experimental facility (units in cm).

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The inner size of the remote room was 60 cm (length)
60 cm (width)
80 cm (height). The height of opening C (18.3 cm wide), which connected the remote room and the long passage, could be changed from 40 to 70 cm (increments of 10 cm). Opening E (25 cm wide
25 cm high) was located at the foreside of the remote room, The distance between the upper side of E and the ceiling of the remote room could be changed from 10 to 30 cm (again, with increments of 10 cm). During the experiments, 50 or 100 g diesel oil was poured into a rectangular iron oil-pan (15 cm long
15 cm wide) that was located at the center of the fire room, 8 cm from the floor. An electronic balance (model SL-8000) was used to record the mass change of the fuel during the experiment, so as to assure the comparability of experiments. In order to study the spatial distribution of toxic gases during smoke movement, three typical horizontal positions, L1, L2, and L3, were selected along the center line of the long passage. Sampling probes were then attached at each horizontal position, vertically from 20 to 70 cm, at 10 cm intervals. In addition, at the horizontal center of the remote room, a horizontal position L4 was selected at which four sampling points were set vertically (at heights of 30, 40, 60, and 70 cm). The arrangement of these probes is shown in Figure 20.1. During the experiments, variations in gas concentrations were recorded at each probe, using an M9000 combustion gas analyzer (Shanghai ENCEL Instrument Co., Ltd) to record the data. This analyzer, which operates on an electrochemistry principle, is capable of simultaneously measuring CO, O2, CO2 and other gases, with an error of 5%. The data acquired at one sampling point were recorded in a single experiment, with repeated measurements (two- to fourfold) being made to provide additional data; average values were then calculated and used for the data analysis. The installation and calibration of the instruments was carried out prior to the experiments. The data were recorded from the point when the diesel oil fuel (see Chapter 19 for details of toxicology) was ignited with a burning paper slip. When the CO concentration has fallen to less than 10% of the maximum value recorded in the experiment, the recording was stopped. During the experiments the ambient temperature ranged from 19 to 26  C.

20.3 The Spatial Distribution Rule of Toxic Gases in the Long Passage 20.3.1 Vertical Distribution Rule of Toxic Gases

From Figure 20.2, it can be seen that, during the experiment, the consumption rate of the fuel is uniform. In general, CO, CO2, and O2 have been selected as the subjects of these research investigations, as these are considered key factors in the assessment of gas toxicities. Data recorded at the six locations of each position, following the combustion of 100 g fuel, are shown graphically in Figure 20.3. These data show that:

20.3 The Spatial Distribution Rule of Toxic Gases in the Long Passage

100

Mass / g

80 60 40 20 0 0

300

600

900

1200

1500

1800

t/s Figure 20.2 Fuel mass variation of the tests. .

.

The toxic gases were transported and spread in distinct vertical layers, with a lower layer from the floor to about 400 mm high, and an upper layer from the height of about 400 mm to the ceiling. The evolution of gas concentrations in the two layers was clearly different: the concentrations of CO and CO2 in the upper layer increased (while that of O2 decreased) in line with the incremental height of the probe. However, concentrations in the lower layer were not sensitive to the height, and the data were very similar between the heights of 200 and 400 mm. The distribution of gases in the lower layer was clearly approximately uniform, but this was not the case in the upper layer. During the earlier stages of the test, the vertical-layer gas distribution was distinct and became clearer as the test proceeded. Meanwhile, the concentration of CO or CO2 in the upper layer rose (that of O2 fell) rapidly, while that of the lower layer increases at a relatively lower speed. During the latter stages, the vertical-layer gas distribution grew indistinct and the concentration gap degressed. It was found, through careful analyses, that the smoke movement was driven by the fire plume and ceiling jet formed from the fire combustion, and that the gases traveled mainly in the upper layer under the drive of heat. However, gas transport in the lower layer was caused by the different gradient of concentrations, and was mainly held by the diffusivity of the gases. The results indicated that the hazard of toxic gases in fires transferred from the upper layer to the lower layer with time; that is, the lower layer would be relatively more dangerous during the latter stage of a fire.

20.3.2 Horizontal Distribution Rule of Toxic Gases

Gas concentrations at the same horizontal level, respectively 300, 500, and 700 mm above the three positions, were recorded in the 50 g fuel tests (Figure 20.4); these data showed that:

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1.8

Concentration of CO2 /%

Concentration of O2 /%

21.0

20.5

20.0 700mm 600mm 500mm 400mm 300mm 200mm

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(i) CO Figure 20.3 Concentrations measured at the probe. (a–c) Position 1; (d–f) Position 2; (g–i) Position 3. The values shown are the probe heights.

20.3 The Spatial Distribution Rule of Toxic Gases in the Long Passage 1.8 1.6

Concentration of CO2 /%

Concentration of O2/%

21.0 20.5 20.0

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Concentration of CO2 /%

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(i) CO Figure 20.4 Concentrations measured at the probe. (a–c) 700 mm; (d–f) 500 mm; (g–i) 300 mm. Positions 1, 2, and 3 are designated L1, L2, and L3 in the text.

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.

.

The gases concentrations at the same horizontal levels differed to a lesser degree, and underwent a similar evolution process. It was concluded that the gases were influenced strongly by the fire effluent, and could be transported rapidly from the fire origin to remote locations, with minimal dilution and diffusivity. The rank of the species concentrations from the three positions was different at the three different levels. At a height of 700 mm, the concentrations of CO or CO2 at Position 1 were higher than those at the other two positions, while at 500 mm, concentrations at Position 2 were higher than those at the other positions. At the 300 mm level, the concentrations of CO or CO2 at Position 3 were much lower than those at Positions 1 and 2. One reasonable explanation of the collected data might be that, at 700 mm Position 1 is close to opening B, which is 450 mm above the floor, such that the fire effluent will first flow directly to the location. At 500 mm, Position 1 did not encounter the smoke front from the fire compartment through opening B, while Position 3 was exposed to the exit (where the outside air supply passes through), such that the gases there were diluted. At 300 mm, Position 3 was also influenced by air flowing from the exit. Hence, it was concluded that the vents and openings causing air flow in the building would play an important role in the horizontal gases distribution.

20.4 The Spatial Distribution Rule of Toxic in the Remote Room

In a typical building fire scenario, many victims are often located at positions far away from the firesource. As toxic gases can move into remote rooms through various openings, and their concentrations may be very high even at remote locations, it is likely that the outcome may be tragic if those people at such locations are either unaware of the situation, or underestimate the risk of the toxic gases. The fact that the spatial distribution of CO in remote rooms can be strongly influenced by not only the area but also the position of the openings, has been studied systematically in a series of bench-scale experiments. 20.4.1 Influence of Opening C

In these experiments, the size of opening B was fixed at 23 cm (width)
45 cm (height), and was located at the central position; the size of opening D was fixed at 20 cm (width)
40 cm (height), and located at the upside of the surface, while opening E was completely sealed. The width of opening C was fixed at 18.3 cm, but its height was altered from 40 to 70 cm, with increments of 10 cm, so as to study its impact on the spatial distribution of CO in the remote room. In Figures 20.5 and 20.6, “m” is the height of C, and “p” is the height of the probes; hence, “m70p60” means that the probe height is 60 cm, and the height of opening C is 70 cm.

20.4 The Spatial Distribution Rule of Toxic in the Remote Room 0.05 m70p70 m60p70 m50p70 m40p70

0.04

0.03

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0.01

Concentration of CO/%

Concentration of CO/%

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m70p60 m60p60 m50p60 m40p60

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0.03

0.02

0.01

0.00 0

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600 800 1000 1200 1400 1600 1800 2000

(c)

t/s

Figure 20.5 Influence of opening C on the spatial distribution of CO in the remote room.

As the data in Figure 20.5 show, the CO concentration increases with the height of opening C. Hence, the lower the height of C, the longer it takes for the CO concentration to reach a peak value. If the height of C is <40 cm, the time taken will be 200 s more than that taken when the height is >40 cm. Clearly, the lower the opening C height, the more difficult it will be for smoke to enter the remote room from the long passage. Each of these phenomena prove that the height of opening C has a major influence on the movement of CO. As the opening height is increased, the peak CO concentration will be much higher, but the time taken to reach that value will be much shorter. Based on the data shown in Figure 20.6, it is clear that the distribution of CO concentration in the upper region of the remote room will be uniform, with very similar values for p70, p60, and p50. However, the difference in peak value at different probes will be strongly influenced by the height of opening C, with peak values of p70 and p60 declining in line with decreases in opening C height; however, when the peak value of p50 is approached, the difference will be increased again. 20.4.2 Influence of Opening D

In these experiments, the size of opening C was fixed at 18.3 cm (width)
50 cm (height), the size of opening E was fixed at 25 cm (width)
25 cm (height), and

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0.040 m70p70 m70p60 m70p50

0.04

0.03

0.02

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m60p70 m60p60 m60p50

0.035

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Concentration of CO/%

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Concentration of CO/%

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(b)

m40p70 m40p60 m40p50

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t/s

(c)

(d)

Figure 20.6 The rules of spatial distribution of CO in a remote room.

w (the distance between the upper side of opening E and the ceiling of the remote room) ¼ 20 cm. The width of opening D was fixed at 15 cm, while its height, h, was altered from 0 to 40, 50, 60, and 70 cm, so as to study the influence of D on the spatial distribution of CO. In Figures 20.7 and 20.8, “h” symbolizes the height of D, and “p” the height of the probe; hence, “h70p70” means that the height of D was 70 cm, and the probe height 70 cm. As shown in Figures 20.7 and 20.8, the CO concentration is increased in line with the decrease in the height of opening D. Because the height of D has a major influence on the movement of smoke, the lower the height of D the more difficult it would be for the smoke to be emitted from the “building” through D; likewise, the easier it would be for smoke to enter the remote room. As shown in Figure 20.4, if the height of D were to be fixed, then the curves of p70 and p60 would be very similar, which in turn would indicate that the CO distribution in the upper region of the remote room was uniform. However, as shown in Figure 20.8, the concentration of p40 was notably higher than p30, indicating that the CO concentration would vary widely in the lower regions of the remote room – a situation very different from that in the upper region. When the smoke emerges from the fire room it spreads along the long passage to the remote room. In these experiments, the size of opening C was fixed; consequently, the higher the height of D, the less smoke would enter the remote

0.16 0.15 0.14 0.13 0.12 0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00

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h70p70 h60p70 h50p70 h40p70 h0p70 h70p60 h60p60 h50p60 h40p60 h0p60

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t/s

(c) w = 10 cm

Figure 20.7 The influence of the height of opening D on the spatial distribution of CO at the upside of the remote room. (a) w ¼ 30 cm; (b) w ¼ 20 cm; (c) w ¼ 10 cm.

Concentration of CO/%

room and more would emerge from the building. Yet, at the same time more fresh air would access the remote room through the lower part of openings D and C, and this would dilute the smoke and reduce the CO concentration. It is for this reason that the CO concentration in the remote room was seen to decline with the height of opening D.

0.12 0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00

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(a) w = 30 cm

Concentration of CO/%

Concentration of CO/%

Concentration of CO/%

20.4 The Spatial Distribution Rule of Toxic in the Remote Room

2500

t/s

Figure 20.8 Influence of the height of opening D on the spatial distribution of CO at the lower part of the remote room.

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20.4.3 Influence of Opening E

In these of experiments, the size of opening D was fixed at 15 cm (width)
60 cm (height), and the size of opening C was fixed at 18.3 cm (width)
50 cm (height). The size of opening E was fixed at 25 cm (width)
25 cm (height), while the distance between the upper side of E and the ceiling of the remote room, w, was altered from 10 to 30 cm (with increments of 10 cm) so as to study the influence of E on the spatial distribution of CO in the remote room. In Figure 20.9, “p” symbolizes the height of probe; hence, “w30p70” means that the distance between the upper side of E and the ceiling of the remote room was 30 cm, and the probe height was 70 cm. As shown in Figure 20.9, the CO concentration was seen to increase in line with the height of E. Likewise, the larger the value of w, the more difficult it was for the smoke to emerge from the remote room, and the easier for it to accumulate. In addition, the influence of dilution was also decreased with w. Hence, the location of opening E was seen to have a major influence on the spatial distribution of CO in the remote room. The spatial distribution rule of CO in the remote room was also influenced by other factors, including the room size, and the area and position of the openings. In the lower region of the remote room the smoke was greatly influenced by fresh air, which entered from ambient through openings E or D and C, such that the CO was diluted. In contrast, in the upper region of the remote room the influence of fresh air was much less. Additionally, smoke in the upper part of the remote room was derived directly from the long passage, whilst that in the lower part was derived from the sedimentation and accumulation of smoke in the upper region. It was this reason that the CO distribution was seen to uniform in the upper region of the room, but to change dramatically in the lower region.

20.5 Simulation of Smoke Movement to the Non-Firesouce-Field

Many fatalities in building fires occur in multicompartments and locations remote from the fire room, and are caused by victims inhaling toxic species. In order to 0.08

Concentration of CO/%

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w10p70 w20p70 w30p70 w10p60 w20p60 w30p60

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(b) Lower part of the remote room.

Figure 20.9 The influence of opening E on the spatial distribution of CO in the remote room. (a) Upper part of the remote room; (b) Lower part of the remote room.

20.5 Simulation of Smoke Movement to the Non-Firesouce-Field

investigate the characteristics of smoke movement from the original compartment fires to the adjacent corridor and the target room, a fire dynamics simulator (FDS) software package was employed. This numerical simulation approach was applied to the recurring full-scale test results reported recently, and further investigations were carried out. Based on the results obtained, it was concluded that smoke transport from a remote location to the fire vicinity in the low-layer, as well as smoke movement to the remote target room, occurred very quickly. Indeed, this was considered to be the main reason why many fatalities occurred at locations remote from the fires. Investigations into the characteristics of smoke movement by both numerical simulation and experimental analysis should provide a clear understanding of this phenomenon. 20.5.1 Structure of Building

In Case 1, the experimental data were taken from Soo-young et al.’s full-scale tests. Figure 20.10a shows the basic construction of the test building (more detailed information is available in Ref. [13]). The simulation configuration was the same as the test. As shown in Figure 20.10a, there was one fire room and a corridor with an exit. The fuel room (designed using ISO 9705) was 2.4 m
3.6 m
2.4 m, while the adjacent corridor was 20 m long and connected vertically to the burn rooms and three other rooms. The door of the corridor was 0.8 m
2.0 m in size, and was opened during both the test and simulation. In Case 2, the structural configuration with a target room adjacent to the corridor was also simulated, as shown in Figure 20.10b. Here, two target rooms (C and D) were designed to be in the middle of the corridor, with two other rooms near to the exit; all rooms were 2.4 m
3.6 m
2.4 m in size. Each target room contained a window that could be either opened or closed in order to study the effects of ventilation on the outside environment. In Case 3, the target rooms were set in a location adjacent to the corridor, as shown in Figure 20.10c. One target room (E1), which was 2.4 m
3.6 m
2.4 m in size, was designed to be close to the exit. Each target room contained a window that could be either opened or closed in order to study the effects of ventilation on the outside environment. 20.5.2 Numerical Simulation 20.5.2.1 Software Package In these studies, computational fluid dynamics (CFD) simulations were applied using FDS version 3.0 [10] in order to study the movement of smoke in the structure. In this case, FDS, which was based on large eddy simulation (LES) [1] with a postprocessing visualization tool, SMOKEVIEW, had been developed to simulate the movement of smoke and hot gases in an enclosure fire. A series of comparisons and verifications was also carried out for this model with test data during the past few

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Fire Room Door Corridor 20 (a) Case NO.1 (Data is taken from Reference 13) 3.6

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20 (c) Case NO.3

Figure 20.10 Schematic diagram of the simulation. (a) Case 1 (Data from Ref. [13]); (b) Case 2; (c) Case 3.

years, with the model being applied to study different fire scenarios in the fire safety research community [11–14]. In these studies, the same configurations as used in the experiments were set for the numerical simulation, and the simulation data selected to compare with the measured results. As the rate of heat release has been recognized as a key factor when determining predicted results, the input was specified as 1000 kW, with a t2 growing model that separately followed the corresponding experiment; this meant that the heat release rate of the fire would grow with the square of time. A uniform grid system size of 0.5 m
0.5 m
0.5 m was assigned in total domain with simulations up to

20.5 Simulation of Smoke Movement to the Non-Firesouce-Field

300 s, taking into consideration the heavy workload of the calculations. The time steps were adjusted automatically by the program, so as to satisfy the convergence criterion during the calculations. The simulations, which were carried out using a personal computer, required approximately 30–40 h of processing time. 20.5.2.2 Simulation Arrangement Initially, two conditions were set for the simulation. The first case was designed to verify the consistency of the test data and the simulation results, and to study the characteristics of smoke movement in the adjacent corridor. In this case, only the door of the fire room was open to the corridor, and the doors of the other rooms were closed. Cases 2 and 3 were used to develop an understanding of the pattern of smoke movement in the building. 20.5.3 Result

When, initially, the results obtained from the simulation and tests were compared to clarify the situation, there was one notable characteristic, namely that the smoke propagation sequence near the ceiling showed a turbulent flow. The downward movement of smoke layer was detected synchronously only after the smoke front had reached the end of corridor. The simulation results were consistent with those of the test data produced by Soo-young et al. Figure 20.11 shows the general smoke movement characteristic that is first to reach the corridor end, and then descends until a boundary bar is formed between the smoke and the air. Figures 20.12 and 20.13 show the results of Cases 2 and 3, where the window of the target room remained open. Clearly, the smoke was able to move to the target room in different times. In Figure 20.11, it can be seen that smoke at a height of 0.9 m and 1.5 m first reaches the opposite room, followed by the remote target room D, the middle target room C, and the nearby target room B; however, at height of 2.1 m the

Figure 20.11 The side elevation of smoke movement at centerline of the corridor in the simulation. (units in meters). (a) 72 s; (b) 112 s.

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Figure 20.12 Result of smoke movement in the simulation, Case 2 (units in meters). (a) The planform; (b) The side elevation.

smoke reached the remote target room D later than the other two rooms. The sequence of smoke transport indicates that smoke in the lower layer will transport from the remote location to the nearby location, and this is the cause of many of the fatalities that occur at locations remote from fires [2, 3]. These results are to be expected from the above analysis. From Figure 20.12, it can be seen that the smoke first reaches the corner, and then turns to the corridor in the y-axis direction, to reach the remote target room E before reaching room D. These results are the same as obtained with the above data, and can be explained in similar fashion as the previous analysis.

Figure 20.13 Result of smoke movement in the simulation, the planform, Case 3 (units in meters).

20.5 Simulation of Smoke Movement to the Non-Firesouce-Field 120

Time / s

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Height / m Figure 20.14 The time of smoke descending to the height of 0.9 m, 1.5 m, and 2.1 m.

Figure 20.14 shows the time taken for the smoke to descend to a specific height. Clearly, such descent would occur more quickly in the remote target room D than in the middle and nearby target rooms, B and C; this indicates that, in a fire, a remote location from the fire origin would be more hazardous to the building occupants. The time for smoke to descend into room E was longer than that for room D, although in the latter case the room height of only 1.5 m was less than that of the nearby rooms C and B. An open or closed window was shown not to play a significant role on smoke movement from the fire origin to the target room, nor on the smoke-descent velocity in the target room. Based on the principles of fire safety engineering, the above-described results are of particular importance with regards to occupants being able to escape from a building fire. Normally, in a fire scenario an occupant would escape from the corridor to the exit. However, in many buildings the exit is located at the end of a corridor, remote from the fire origin. Unfortunately, the escape destination of the occupants may incorporate the more hazardous locations of smoke, there being not only greater amounts of smoke and toxic gases but also a lower smoke interface with the respiratory tract. Within a fire scenario, those occupants in a remote target room may be at greater danger, as they may be the last to receive an alarm signal and consequently may encounter toxic smoke very quickly, especially if they were asleep at the fire onset. On a statistical basis, most fatalities caused by smoke inhalation in a fire scenario occur at a location remote from the fire origin. This is essentially due not only to an inadequate design of the building structure but also to the smoke moving to remote locations that invariably will include the corridor and target room.

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20.6 Risk Analysis [19] 20.6.1 Index of Toxicity

In an effort to analyze the risk of CO distribution in a non-firesource-field, the concept of “peak-width time” has been proposed, this being the sum of the time when the concentration is higher than the integral median. Others have suggested the use of an improved parameter of “total toxicity”, which takes into account both the CO concentration and the time involved, and is calculated by multiplying the “peak-width time” by the integral median. In general terms, however, neither parameter has a clear and practical meaning. The peak-width time employs a parameter which has the dimension of time to evaluate the risk of fire smoke, but this is impractical and also under-rates the hazard of fire smoke. In the same way, “total toxicity” cannot truly reflect the toxicity of the smoke, because it is more closely resembles a mathematical rather than a practical parameter, which is related to the more “concrete” hazard of the concentration of toxic smoke. As difficulties have been encountered in the application and popularization of these terms, a third concept – the index of toxicity, It – has been proposed which incorporates both the time and the critical concentration [19]. In order to calculate It, it is first necessary to select one critical CO concentration, Ccrit, after which, based on the relationship between concentration and time, It can be determined (see Equation 20.1 and Figure 20.15): Ð ts It ¼

tb

CðtÞ  dt

ðts tb Þ  Ccrit

ð20:1Þ

where C(t) is the CO concentration which changes with time, ts is the time before which C(t) is higher than Ccrit, tb is the time after which C(t) is higher than Ccrit, and Ccrit is the preselected critical concentration. As It is a dimensionless parameter that takes into consideration both the exposure time and hazardous concentration of CO, it can be used to analyze the toxicity of smoke at any location. In smoke, the major toxic gas is CO, the toxicity of which is dependent on both the time of exposure and concentration. When exposed to CO for long periods of time (120–180 min), the critical concentration for CO is 200 ppm; however, if the CO concentration in ambient air is in excess of 6900 ppm, then people will begin to suffer severe respiratory problems following only a very brief exposure (1–2 min) [20]. As Ccrit is based not only on statistical data but also on the safety code of different countries, a value of 200 ppm was selected in this chapter, which is appropriate in China. It is then possible, by using Equation 20.1, to calculate It at different locations and under different conditions. In this way, based on different studies, Ccrit can be selected for a variety of research purposes.

20.6 Risk Analysis

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0.16 0.14

Concentration of CO/%

0.12 0.10 0.08

Ccrit

0.06 0.04 0.02 0.00

tb

-0.02 -500

0

ts

500

1000

1500

2000

2500

3000

3500 t/s

Figure 20.15 Sketch map of the index of toxicity, It.

20.6.2 The Application of It in the Risk Analysis of Fire

2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2

L1 L2 L3

Index of Toxicity

Index of Toxicity

As shown in Figure 20.16, in the long passage, a clear delamination occurs, with a high-toxicity zone apparent in the upper region (50–70 cm) and a low-toxicity zone in the lower region (20–40 cm). The It in the low-toxicity zone was much smaller than in the high-toxicity zone where, generally speaking, It was seen to increase with the region’s height. Within the low-toxicity zone, It was shown to be uniform and to vary little with the regions height. Thus, in a fire scenario the evacuee should

20

30

40

50

60

70

Height of probe / cm

(a) Figure 20.16 The distribution of It in the long passage.

2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3

p70 p60 p50 p40 p30 p20

1

2

Horizonontal / Position

(b)

3

j 20 Smoke Spread in Buildings

3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2

p70 p60 p50

Index of Toxicity

Index of Toxicity

496

45

40

50

55

60

65

3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4

m40 m50 m60 m70

70

50

60

Height of opening C / cm

70

Height of Probe / cm

(a)

(b)

Figure 20.17 The influence of opening C on It in the remote room.

reduce their height as much as possible, by crouching or crawling, so as to reduce the risk. Horizontally, L2 is an inflexion and the slope of It will change notably. In addition, instead of increasing with the long passage height, the It-values in this region will be very close to each other, which indicates that the risk will be relatively high at this point. Such It-values also imply that, if certain smoke-control measures were to be implemented in this region, then security would be improved notably and the evacuee safety much improved. Vertically, in the upper region of the long passage, the height of 40 cm is an inflexion, and It will increase much more quickly when the height exceeds this value. Thus, the plane of height 40 cm may be regarded as an interface between the highand low-toxicity zones. As shown in Figure 20.17, the distribution of CO concentration may be notably influenced by opening C. In fact, as an approximation, It will increase linearly with the height of C such that, in the upper part of the remote room It will be uniform; the situation is quite different, however, in the upper part of the long passage. As shown in Figure 20.18, the height of D has a major influence on It in the remote room; typically, It will decline linearly with the height of opening D. 6.5 6.5

P30 P40 P60 P70

Index of Toxicity

5.5

5.5

5.0 4.5 4.0 3.5 3.0

5.0 4.5 4.0 3.5 3.0

2.5

2.5

2.0

2.0 1.5

1.5 -10

h0 h40 h50 h60 h70

6.0

Index of Toxicity

6.0

0

10

20

30

40

50

60

70

80

30

40

50

60

Height of opening D / cm

Height of probe / cm

(a)

(b)

Figure 20.18 The influence of opening D on It in the remote room.

70

4.4 4.2 4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6

p30 p40 p60 p70

Index of Toxicity

Index of Toxicity

20.7 Conclusions

10

20 Height of opening E / cm

30

4.6 4.4 4.2 4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6

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w 10 w 20 w 30

30

(a)

40

50 60 Height of probe / cm

(b)

Figure 20.19 The influence of opening E on It in the remote room.

Vertically, there are clearly two zones: (i) the high-toxicity zone at the upper part (60–70 cm); and (ii) the low-toxicity zone at the lower part (30–40 cm). In this case, It will be uniform at the upper part, but will vary widely at the lower part. This characteristic of the distribution of It contrasts with the parameter’s characteristics in the long passage. Generally speaking, from a vertical aspect It will be increased with the height of the probe, although there is an inflexion at a height of 60 cm. Subsequently, It will increase with the height of the probe if the latter is <60 cm, but it may fall if the probe is higher than 60 cm. In other words, the peak value of It is not always at the highest position. As shown in Figure 20.19, the opening E has a major influence on It, and the relationship between It and the height of opening E is quite complex. Although there remains much to be done to identify the details of this relationship, based on the limited data available it has been proposed that a delamination would occur, based on the analyses of It in the remote room.

20.7 Conclusions

When on fire, a building can be separated into two fields, namely the firesource-field and the non-firesource-field. A series of bench-scale experimental investigations has been carried out to study the spatial distribution rule and risk analysis of toxic gases. Based on the results of experimental studies, in a non-firesource-field the CO concentration will increase with the height of the probe. Likewise, for a long passage the CO concentration will increase in line with the distance from the firesource, whilst for a remote room the existence of various openings may greatly affect the CO concentrations. Certain differences have been identified in distribution rules between the long passage and the remote room. In the former case, smoke concentrations will vary greatly in the upper regions, but be uniform in the lower regions. Yet, distribution in a remote room will be exactly the opposite, with a uniform content in the upper regions

70

j 20 Smoke Spread in Buildings

498

but widely varying contents in the lower. Based on these differences, it is vital that in all research investigations the long passage and remote room should be treated differently. Moreover, at local positions the presence of various openings has demonstrated clear influences on the pattern of toxic gas concentrations. In the present studies, based on full-scale experiment data reported recently by others, the characteristics of transport of smoke from the compartment fires in the adjacent corridor and the target room were studied. For this, a numerical simulation approach – the FDS software package – was applied to recurring experiment results, and further investigations carried out. Based on the results of these analyses, it became clear that both smoke transport from a remote location to the fire vicinity in the lower layer, and smoke movement to a remote target room, occurred very quickly, and that this was the cause of the many fatalities that occurred at locations quite remote from the fire. When the index of toxicity, It [19], was used to analyze smoke toxicity in a nonfiresource-field, it was concluded that consistency with experimental data was possible, and that a functional relationship was in fact much simpler. In addition, It represented a dimensionless unit that was related to both hazard concentrations and time, and was also much more convenient and practical to apply. The concept of It remains to be studied and developed further.

Acknowledgments

The authors acknowledge those graduate students who have contributed substantially to the information in this chapter, including Dr Huang Rui, Dr Fang Tingyong, Dr Feng Wenxing, and Ye Junqi. These studies were supported by the National Natural Science Foundation of China (Grant Numbers 50536030 and 50576095), and the Program for New Century Excellent Talents in University. The support of these foundations is greatly appreciated.

References 1 Fan, W.C., Sun, J.H., Lu, S.X., Huo, R.,

3 Chow, W.K. and Yin, R. (2006) Smoke

Yao, B., Hu, T.Z., Fang, Z.B., Sun, Z.H., Yang, L.Z., Song, W.G., Chen, T., Liao, G.X., Yuan, H.Y., Hu, Y., Jiang, Y., and Zhang, H.P. (2004) Risk Evaluation Methodology of Fire Science, Press of University of Science and Technology of China, HeFei, p. 543. 2 Feng, W.X., Yang, L.Z., and Ye, J.Q. (2008) Experimental research on the spatial distribution of toxic gases in the transport of fire smoke. J. Fire Sci., 26 (1), 45–62.

movement in a compartmental fire. J. Fire Sci., 24 (6), 445–463. 4 Yii, E.H., Fleischmann, C.M., and Buchanan, A.H. (2005) Experimental study of fire compartment with door opening and roof opening. Fire Mater., 29 (5), 315–334. 5 Lattimer, B.Y., Vandsburger, U., and Roby, R.J. (1997) The transport of high concentration of carbon monoxide to locations remote from the burning compartment. Report NIST-GCR-97-713,

References

6

7

8

9

10

11

12

National Institute of Standards and Technology, Gaithersburg, MD, p. 350. Tokunaga, T., Amano, K., Oiwa, D., Uchiyama, S., Mizuno, M., Ohmiya, Y., Wakamatsu, T., and Tsujimoto, M. (2007) An experimental study on the smoke behavior and ensuring the safety of staircases in a station building during a fire-air current control in an underground station by a passive safety system. Fire Sci. Technol., 26, 9–42. Neophytou, M.K.A. and Britter, R.E. (2005) A simple model for the movement of fire smoke in a confined tunnel. Pure Appl. Geophys., 162 (10), 1941–1954. Hu, L.H., Huo, R., Lia, L.Z., Wang, H.B., and Chow, W.K. (2005) Full-scale burning tests on studying smoke temperature and velocity along a corridor. Tunn. Undergr. Sp. Tech., 20 (3), 223–229. Wang, Z., Jia, F., and Galea, E.R. (2007) Predicting toxic gas concentrations resulting from enclosure fires using local equivalence ratio concept linked to fire field models. Fire Mater., 31 (1), 27–51. Lattimer, B.Y., Vandsburger, U., and Roby, R.J. (1997) Transport of high concentrations of carbon monoxide to locations remote the burning compartment, National Institute of Standards and Technology, Gaithersburg, pp. 1–315. Wieczorek, C.J. (2003) Carbon monoxide generation and transport from compartment fires. Ph.D. Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. Fang, T.Y., Yang, L.Z., and Huang, R. (2003) The relationship between smoke concentration some distance down the

13

14

15

16

17

18

19

20

hallway and the opening of the room of fire origin. J. Fire Sci., 21 (4), 305–318. Kim, S.-Y., Kim, E.-S., Kim, H., Ku, D.-C., and Lee, C.-H.(17–20 March 2004) An analysis of compartment fire and induced smoke movement in adjacent corridor. 6th Asia-Oceania Symposium on Fire Science and Technology, Daegu, Korea, pp. 512–518. Hu, L.H., Li, Y.Z., Huo, R., Yi, L., and Chow, W.K. (2006) Full-scale experimental studies on mechanical smoke exhaust efficiency in an underground corridor. Build. Environ., 41 (12), 1622–1630. Fang, T.Y., Yang, L.Z., Huang, R. et al. (2003) The relationship between smoke concentration some distance down the hallway and the opening of the room of fire origin. J. Fire Sci., 21 (4), 305–318. Au, S.K., Wang, Z.H., and Lo, S.M. (2007) Compartment fire risk analysis by advanced Monte Carlo simulation. Eng. Struct., 29 (9), 2381–2390. Lin, Y.S. (2004) Life risk analysis in residential building fires. J. Fire Sci., 22 (6), 491–504. Wade, C. and Whiting, P. (1996) Fire risk assessment using the building fire safety engineering method. J. Fire Protect. Eng., 8 (4), 157–167. Wu, L.-X., Yang, L.-Z., and Li, X. (2008–2009) The spatial distribution rule and risk analysis of CO in the nonfiresource-field in the fire scenario. J. Appl. Fire Sci., 16 (3), 185–204. Huo, R. and Yuan, H.Y. (2003) Performance-based Fire Protection Analyse and Design, Anhui Science & Technology Publishing House, HeFei, p. 409.

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