ABSTRACT
The influences of differential diffusion of heat and mass on the Favre-filtered scalar dissipation rate (SDR) transport have been analyzed and modeled using a priori analysis of Direct Numerical Simulations (DNS) data of freely propagating statistically planar turbulent premixed flames with different values of global Lewis number, Le. The DNS data has been explicitly filtered using a Gaussian filter to obtain the unclosed terms of the Favre-filtered SDR transport equation, arising from turbulent transport (T1), density variation due to heat release (T2), strain rate contribution due to the alignment of scalar and velocity gradients (T3), correlation between the gradients of reaction rate and reaction progress variable (T4), molecular dissipation of SDR (−D2), and diffusivity gradients f(D). The statistical behaviors of these terms and their scaling estimates reported in a recent analysis have been utilized here to propose models for these unclosed terms in the context of Large Eddy Simulations (LES) and the performances of these models have been assessed using the values obtained from explicitly filtered DNS data. These newly proposed models are found to satisfactorily predict both the qualitative and quantitative behaviors of these unclosed terms for a range of filter widths Δ for all Le cases considered here.
Nomenclature
c | = | reaction progress variable |
cm | = | thermo-chemical parameter |
CP | = | specific heat at constant pressure |
CV | = | specific heat at constant volume |
CF | = | model parameter |
C3, C4 | = | model parameters |
D | = | progress variable diffusivity |
Dt | = | eddy diffusivity |
Da | = | Damköhler number |
D1 | = | molecular diffusion term |
D2 | = | molecular dissipation term |
fb | = | burning mode probability density function |
= | model parameters | |
f(D) | = | term due to diffusivity gradient |
ksgs | = | sub-grid scale kinetic energy |
= | thermo-chemical parameter | |
Ka | = | Karlovitz number |
Le | = | Lewis number |
l | = | integral length scale |
Ma | = | Mach number |
Mi | = | ith component of resolved flame normal |
Nc | = | scalar dissipation rate |
Ni | = | ith component of flame normal |
p | = | model parameter |
Pr | = | Prandtl number |
Q | = | general quantity |
Ret | = | turbulent Reynolds number |
ReΔ | = | sub-grid Reynolds number |
= | unstrained laminar burning velocity | |
t | = | time |
tc | = | chemical time scale |
tf | = | initial turbulent eddy turnover time |
tsim | = | simulation time |
T | = | instantaneous dimensional temperature |
T+ | = | non-dimensional temperature |
Tad | = | adiabatic flame temperature |
T0 | = | reactant temperature |
T1, T2, T3, T4 | = | terms in the transport equation of Favre-filtered scalar dissipation rate |
ui | = | ith component of nondimensional fluid velocity |
u′ | = | root mean square fluctuation of velocity |
u′Δ | = | sub-grid velocity fluctuation |
= | chemical reaction rate | |
xi | = | ith Cartesian coordinate |
YR | = | reactant mass fraction |
YR0 | = | reactant mass fraction in unburned gas |
YR∝ | = | reactant mass fraction in burned gas |
αT | = | thermal diffusivity |
αT0 | = | thermal diffusivity of the unburned gas |
β | = | Zel’dovich number |
β3, β′3 | = | model parameters |
γ | = | ratio of specific heats (=CP/CV) |
γ1, γ2 | = | model parameter |
δth | = | thermal flame thickness |
δz | = | Zel'dovich flame thickness |
Δ | = | filter width |
Γ | = | model parameter |
μ | = | viscosity |
μ0 | = | viscosity of unburned gas |
ρ | = | density |
ρ0 | = | unburned gas density |
τ | = | heat release parameter |
τij | = | viscous stress tensor |
Φ′ | = | model parameter |
= | LES-filtered value of a general quantity q | |
= | Favre-filtered value of a general quantity q | |
Subscripts | = | |
0 | = | unburned gas value |
∞ | = | burned gas value |
res | = | resolved scale value |
sg | = | sub-grid scale value |
Acronyms | = | |
DNS | = | direct numerical simulation |
LES | = | large eddy simulation |
= | probability density function | |
SDR | = | scalar dissipation rate |
1. Introduction
Lean premixed combustion has been identified as one of the possible ways to reduce pollutant emission from gasoline engines and industrial gas turbines [Citation1]. Lean hydrogen and hydrogen-blended hydrocarbon combustion has the potential to attenuate pollutants and greenhouse gas emissions [Citation2, Citation3]. However, the flames with abundance of fast diffusing species such as hydrogen either in molecular or in atomic form give rise to a significant level of differential diffusion of heat and mass. The differential diffusion of heat and mass can be characterized by a nondimensional number known as the Lewis number Le, which is defined as the ratio of thermal diffusivity αT to mass diffusivity D (i.e., Le = αT/D). In actual premixed combustion it is often not straightforward to assign a single global Lewis number in the presence of several species with different Lewis numbers. Often the Lewis number of the deficient species is considered to be the global Lewis number [Citation4], whereas Law and Kwon [Citation5] proposed a methodology of evaluating the effective Lewis number based on heat release measurements. More recently Dinkelacker et al. [Citation6] proposed an algebraic expression for the effective Lewis number based on mole fractions of major species. A number of previous analyses concentrated on the effects of global Lewis number on different aspects of premixed combustion in isolation [Citation7–Citation28] and the same approach has been adopted here.
Modeling of the differential diffusion arising from non-unity global Lewis number remains pivotal to high-fidelity engineering simulations, which are likely to play important roles in the development of new-generation combustors using either hydrogen or hydrogen-blended fuels. Prediction of the micro-mixing rate of hot products and cold unburned gas plays a key role in the modeling of turbulent reacting flows and a quantity known as the scalar dissipation rate (SDR) characterizes this micro-mixing rate [Citation29, Citation30–Citation32]. Furthermore, the Favre-mean value of SDR of reaction progress variable c in premixed turbulent flames can be related to the mean reaction rate in the context of Reynolds Averaged Navier Stokes (RANS) simulations [Citation23, Citation33–Citation35]. The instantaneous SDR of reaction progress variable is defined as [Citation23, Citation33–Citation38]
The modeling of SDR not only is useful for the closure of filtered reaction rate but also plays a pivotal role in the closure of micro-mixing rate in the context of pdf methodology [Citation30, Citation39–Citation41]. For turbulent premixed flames, the Favre-filtered SDR can be modeled either by using an algebraic expression in terms of the resolved quantities or by solving a modeled transport equation. A few recent analyses [Citation36–Citation41] have concentrated on the algebraic closure of SDR for turbulent premixed flames in the context of LES. Algebraic closures are suitable when an equilibrium is maintained between the generation and destruction rates of
, but this assumption may be rendered invalid under some conditions (e.g., low Damköhler number lean premixed combustion). A number of previous analyses [Citation34, Citation42–Citation51] concentrated on the modeling of SDR transport in turbulent premixed combustion in the context of RANS simulations. Interested readers are referred to Ref [Citation34]. for a detailed review of the existing modeling methodologies for SDR transport in the context of RANS simulations. Recent advancements in high-performance computing have made LES of industrial flows more affordable than in the past, and LES is more successful in capturing unsteady flow features than RANS. However, relatively limited attention has been given to the investigation of SDR transport in the context of LES [Citation52, Citation53]. Recently, models for the unclosed terms of the SDR
transport equation for unity Lewis number flames in the context of LES have been proposed [Citation53], but the differential diffusion effects due to non-unity Le were not addressed. A recent analysis [Citation28] concentrated on the influences of global Le on the statistical behaviors of the unclosed terms of the
transport equation based on an order-of-magnitude approach, which successfully explained the effects of global Le and the filter width Δ dependences of the Favre-filtered SDR
and its transport. It has been found that Le has significant influences on both the qualitative and quantitative behaviors of the unclosed terms of the SDR
transport equation [Citation22, Citation28]; however, the modeling of Le effects on these unclosed terms is yet to be addressed, and the present analysis aims to address this gap in the existing literature. In this respect the main objectives of this paper are:
To propose models for the unclosed terms of the SDR transport equation in such a manner that the performances of these models remain satisfactory for a range of Δ and Le.
To assess the performances of the newly proposed models with respect to explicitly filtered Direct Numerical Simulation (DNS) data.
These objectives are addressed here by conducting a priori analysis using a DNS database of statistically planar turbulent premixed flames with a range of different values of Le (i.e., Le = 0.34–1.2). The details related to mathematical background and numerical implementation are provided in the next section. This is followed by the presentation of the results and subsequent discussion. The main findings are summarized and conclusions are drawn in the final section of this paper.
2. Mathematical background & numerical implementation
Three-dimensional DNS simulations with detailed chemistry are now possible, but they remain extremely expensive and need several millions of CPU hours [Citation54] for conducting extensive parametric variations and carrying out explicit filtering of DNS data using a range of filter widths Δ, as has been carried out in the current study. Thus, the chemical mechanism has been simplified here as a single-step Arrhenius-type irreversible chemical reaction. Under this condition the species field is uniquely represented by a reaction progress variable c, which can be defined by using the mass fraction of a suitable reactant YR as c = (YR0 − YR)/(YR0 − YR∞), where subscripts 0 and ∞ denote the values in the unburned and burned gases, respectively. The transport equation of c can be used to derive a transport equation of , which takes the following form [Citation28, Citation53]:
For the present analysis, a DNS database of freely propagating turbulent premixed flames has been considered. The simulation domain is taken to be a cube of 24.1δth × 24.1δth × 24.1δth, which is discretized using a uniform Cartesian grid of 230 × 230 × 230 points, ensuring about 10 grid points are kept within Min(δL, δth), where δL = 1/(Max |∇c|L) is an alternative flame thickness based on |∇c| and the values of δL/δth for cases A–E (with Le = 0.34, 0.6, 0.8, 1.0, and 1.2) are provided in . The initial values of the normalized root-mean-square (rms) value of turbulent velocity u′/SL, integral length scale to thermal flame thickness ratio l/δth, Damköhler number Da = lSL/u′δth, Karlovitz number Ka = (u′/SL)3/2(l/δth)−1/2, turbulent Reynolds number Ret = ρ0u′l/μ0, and τ = (Tad − T0)/T0 are presented in along with domain and grid sizes, where ρ0 and μ0 are the unburned gas density and viscosity, respectively, and SL is the unstrained laminar burning velocity. The flamelet assumption is likely to be valid for the values of u′/SL and l/δth considered here, and all cases considered here represent the thin reaction zones regime combustion according to the regime diagram by Peters [Citation55].
Table 1. Initial values of simulation parameters and nondimensional numbers relevant to the DNS database considered for this analysis.
The simulations have been carried out using a well-known DNS code called SENGA [Citation56]. For all cases the boundary conditions in the mean flame propagation direction are considered to be partially nonreflecting, whereas boundaries in the transverse directions are considered to be periodic. A 10th order central difference scheme is used for spatial differentiation for the internal grid points and the order of differentiation gradually drops to a one-sided second-order scheme at the non-periodic boundaries. A low-storage third-order Runge-Kutta method is used for explicit time advancement for all the governing equations. In all cases flame–turbulence interaction takes place under decaying turbulence, which necessitates the simulation time tsim ≥ max(tf, tc), where tf = l/u′ is the initial eddy turnover time and is the chemical time scale, with αT0 being the unburned gas thermal diffusivity. The simulations have been carried out for about 3.34tf = 3.34l/u′, which amounts to approximately
for all cases considered here. Several studies [Citation12–Citation15, Citation19, Citation57–Citation61] with either similar or smaller simulation time have contributed significantly to the fundamental understanding and modeling of turbulent premixed combustion in the past. By the time the statistics were extracted, the value of u′/SL in the unburned reactants ahead of the flame had decayed by about 50%, whereas the value of l/δth had increased by about 1.7 times, relative to their initial values. This database has been used in several previous analyses [Citation20–Citation28] and it was shown in Ref [Citation23]. that the volume-integrated burning rate for the Le = 1.0 and 1.2 flames reached quasi-steady state by the time statistics were extracted. However, the Le < 1.0 flames are thermo-diffusively unstable and thus the volume-integrated burning rate increases with time for these cases [Citation23]. The qualitative nature of the statistics was found to remain unchanged and the scaling estimates presented in the next section remain valid since t = 2.0 l/u′ for all cases considered here.
The unclosed terms of the transport equation of have been evaluated by explicitly filtering DNS data using a standard three-dimensional Gaussian filter [Citation28, Citation53, Citation57, Citation58, Citation60]:
and the filtered values of a general quantity Q are given by the following integral:
. In the next section, the results will be presented for Δ ranging from Δ ≈ 0.4δth to Δ ≈ 2.8δth. This range of filter widths is comparable to the range of Δ used in several previous a priori DNS analyses [Citation57, Citation58, Citation60], and addresses a range of different length scales from Δ comparable to δth ≈ 1.75δz (δz = αT0/SL is the Zel’dovich flame thickness) up to 2.8δth ≈ 5.0δz, where Δ is comparable to the integral length scale.
3. Results and discussion
The distributions of on x1 − x2 mid-plane for Δ = 0.8δth, 1.6δth, and 2.8δth at t =1.75 tc for cases A–E are shown in , which shows an increase in the extent of flame wrinkling with decreasing Le. The extent of flame wrinkling can be quantified in terms of the normalized turbulent flame surface area AT/AL, where the flame surface area is evaluated using the volume integration of the form: A = ∫ V|∇c|dV with subscripts “T” and “L” denoting the turbulent and laminar flame values, respectively [Citation28]. The values of AT/AL and the normalized turbulent burning velocity ST/SL (where
) at 1.75tc = δth/SL are listed in , which demonstrates that both AT/AL and ST/SL increase significantly with decreasing Lewis number. The burning rate per unit area in turbulent flames increases (decreases) compared with the corresponding laminar value as a result of negative (positive) Markstein length [Citation7–Citation10] for the Le < 1 (Le > 1) flames. This, in turn, leads to ST/SL > AT / AL (ST / SL < AT / AL) in the Le < 1 (Le > 1) flames (see ). It can be further seen from that the flame brush thickens (i.e., the magnitude of
decreases) and the extent of flame wrinkling decreases with increasing Δ as a result of the smearing of local information due to the convolution operation associated with LES filtering. As the SDR is related to the reaction rate, and the gradient of the reaction progress variable, the effects of Le on burning rate and Δ dependence of
are expected to influence the statistical behavior of SDR
and its transport. The effects of Le and Δ on the statistical behavior of SDR
and its transport have been analyzed elsewhere [Citation28] and the current analysis will only concentrate on the influences of global Lewis number on the modeling of SDR transport.
Table 2. The effects of Lewis number on normalized flame surface area AT/AL and normalized turbulent flame speed ST/SLwhen the statistics were extracted (i.e., t = 1.75 αT0/
).
Figure 1. Distributions of on x1 − x2 mid-plane for Δ = 0.8δth (1st column), 1.6δth (2nd column), 2.8δth (3rd column) for cases A–E (1st–5th row) when the statistics were extracted (i.e., t = 1.75 αT0/
).
![Figure 1. Distributions of on x1 − x2 mid-plane for Δ = 0.8δth (1st column), 1.6δth (2nd column), 2.8δth (3rd column) for cases A–E (1st–5th row) when the statistics were extracted (i.e., t = 1.75 αT0/).](/cms/asset/2817ad97-f0d8-4913-bf7f-2e909c013718/unht_a_1125732_f0001_oc.jpg)
The normalized mean values of T1, T2, T3, T4, (−D2), and f(D) conditional on for cases A–E are shown in for Δ ≈ 0.4δth and Δ ≈ 2.8δth. shows that T2 and (−D2) act as source and sink, respectively, in all cases, which is consistent with previous findings [Citation21, Citation28]. The contribution of T4 is positive for a major portion of the flame brush before becoming negative toward the burned gas side for Δ ≤ δth (e.g., Δ ≈ 0.4δth); however, for Δ > δth (e.g., Δ ≈ 2.8δth) the contribution of T4 remains a leading-order source throughout the flame brush. The term T3 assumes positive values toward the unburned gas side of the flame brush before assuming mostly negative values for the major part of the flame brush in cases D and E, whereas T3 is negative throughout the flame brush in cases A–C for all filter widths. The contribution of f(D) is negative (positive) toward the unburned (burned) gas side of the flame brush for all cases and for all filter widths. The magnitude of T1 is negligible compared with T2, T3, T4, (−D2), and f(D) for all Δ in all cases. It can be seen from that the magnitude of all the terms decrease with increasing Le and Δ, which is consistent with previous findings based on DNS data [Citation22, Citation28]. The observed behaviors of T1, T2, T3, T4, (−D2), and f(D) in response to Le and Δ have recently been explained by Gao et al. [Citation28] using a detailed scaling analysis, and the scaling estimates of the filtered SDR and the unclosed terms of the SDR transport equation are provided in . It is worth noting that m and n in are positive numbers with magnitudes greater than unity, and the functions g(Le), φ(Le), φ1(Le), and Ψ(Le) increase with decreasing global Lewis number Le. It can be seen from the scaling estimates in that the magnitudes of the terms T1, T2, T3, T4, (−D2), and f(D) are expected to increase with decreasing filter width and global Lewis number. Interested readers are referred to [Citation28] for further discussion on the derivation of the scaling estimates of T1, T2, T3, T4, (−D2), and f(D), and only the modeling of these terms will be discussed in this paper in the following subsections.
Figure 2. Variations of T1 ( —— ), T2 (
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![Figure 2. Variations of T1 ( —— ), T2 (Display full size), T3 (Display full size), T4 (Display full size), (−D2) (Display full size), and f(D) (Display full size) conditionally averaged in bins of for Δ ≈ 0.4δth (1st column), 1.6δth (2nd column), and 2.8δth (3rd column) in cases A–E (1st–5th row). All the terms are normalized with respect to .](/cms/asset/72e6925c-a45d-440a-9d3b-fe559790bd42/unht_a_1125732_f0002_oc.jpg)
Table 3. Summary of the scaling estimates of the relevant quantities according to Gao et al. [Citation28].
3.1. Modeling of the turbulent transport term T1
Equation (4i) indicates that the turbulent transport term T1 could be satisfactorily closed if the sub-grid flux of SDR (i.e., ) is properly modeled. The sub-grid flux of SDR
is often modeled using a gradient hypothesis as follows [Citation34]:
Gao et al. [Citation28] demonstrated that the unclosed term T1 can be scaled in the following manner:
It is worth noting that the scaling estimate given by Eq. (5ii) (Eq. (5iii)) is more appropriate for counter-gradient (gradient) transport. Equations (5ii) and (5iii) can be combined to obtain the following scaling estimate, which is valid for both gradient and counter-gradient transport [Citation28]:
Gao et al. [Citation53] have recently extended a RANS model proposed by Chakraborty and Swaminathan [Citation51] for the purpose of modeling for the unity Lewis number flames in the context of LES in the following manner:
The parameterization given by Eq. (6ii) ensures that γ2 assumes an asymptotic value for large values of ReΔ (i.e., ReΔ → ∞ ). In Eq. (6i), the first term on the right-hand side principally accounts for the effects of flame normal acceleration due to heat release, whereas the last term on the right-hand side of Eq. (6i) represents turbulent transport according to conventional gradient hypothesis. Moreover, the first and second terms on the right-hand side of Eq. (6i) are consistent with the scaling estimates given by Eqs. (5iv) and (5iii), respectively.
The predictions of according to Eq. (6i) with Φ′ = 0.7 are compared to the corresponding quantity extracted from DNS data for Δ ≈ 0.4δth, 1.6δth, and 2.8δth in for cases A–E. shows that even though Eq. (6i) predicts
in a reasonable manner in the cases with Le ≈ 1.0 (e.g., cases C–E), this model does not adequately capture the correct qualitative and quantitative behaviors of
for the flames with Le << 1.0 (i.e., cases A and B). The model given by Eqs. (6i) and (6ii) does not explicitly account for non-unity Lewis number effects, so it is not surprising that this model does not adequately capture the behavior of
for Le << 1.0 flames where the nondimensional temperature T+ = (T − T0)/(Tad − T0) is significantly different from the reaction progress variable c, which alters the distribution of heat release and thermal expansion within the flame brush compared with the Le ≈ 1.0 flames. This behavior is mimicked here by introducing Le dependence of the model parameter Φ′ in the following manner:
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![Figure 3. Variations of (Display full size) conditionally averaged in bins of along with the predictions of Eqs. (6i) and (6ii) with Φ′ = 0.7 (Display full size) and Eqs. (6i) and (6ii) with Φ′ according to Eq. (6iii) (Display full size) for Δ ≈ 0.4δth (1st column), 1.6δth (2nd column), and 2.8δth (3rd column) in cases A–E (1st–5th row).](/cms/asset/4550df43-c669-4d11-aa4d-a25639ce900d/unht_a_1125732_f0003_oc.jpg)
The predictions of the model given by Eq. (6i) with Φ′ according to Eq. (6iii) are also shown in , which demonstrates that the model with new parameterization Φ′ = 0.3(1 − Le) + 0.7 predicts satisfactorily for all filter widths in all cases considered here and the agreement between the predictions of Eq. (6i) and DNS data improves with increasing Δ (see ). The predictions of Eq. (6i) with Φ′ according to Eq. (6iii) become equal to the corresponding values obtained for Φ′ = 0.7 for the Le = 1.0 case, and these two predictions cannot be distinguished from each other for case D in . It worth noting that the sub-grid flux of the reaction progress variable (i.e.,
) requires modeling in LES, and the performances of the models for
and the turbulent transport term T1 depend on the modeling of
. The modeling of
is beyond the scope of the current analysis and interested readers are referred to recent investigations by Chakraborty and Cant [Citation63] and Gao et al. [Citation64] for further discussion on the modeling of turbulent scalar fluxes in premixed turbulent flames.
3.2. Modeling of the density variation term T2
For unity Lewis number flames the gas density ρ can be expressed as ρ0/(1 + τc) [Citation33], which leads to an alternative expression for the density variation term T2 as [Citation22, Citation47, Citation48, Citation51, Citation53]: . However, ρ = ρ0/(1 + τT+) ≠ ρ0/(1 + τc) in the non-unity Lewis number flames because the equality between T+ and c no longer holds. Although
does not strictly hold in non-unity Lewis number flames, the gas density can still be scaled as ρ ∼ ρ0/(1 + τc); thus, the density variation term T2 can be scaled for adiabatic flames with low Mach number as follows [Citation28]:
The scaling estimates given by Eqs. (7i) and (7ii) demonstrate that T2 remains of the order of irrespective of Δ. By contrast, the magnitude of (T2)res remains comparable to
for Uref ∼ SL and Δ ≈ δth; however, the magnitude of (T2)res is expected to decrease with increasing Δ. This suggests that the sub-grid component (T2)sg = T2 − (T2)res plays an increasingly important role with increasing Δ, which can be substantiated from , where the variations of the mean values of T2 and (T2)sg = T2 − (T2)res conditional on
are shown for cases A–E for Δ ≈ 0.4δth, 1.6δth, and 2.8δth.
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![Figure 4. Variations of T2 (Display full size) and (T2)sg (Display full size) conditionally averaged in bins of along with the predictions of Eqs. (8) (Display full size) and (9) (Display full size) for Δ ≈ 0.4δth (1st column), 1.6δth (2nd column), and 2.8δth (3rd column) in cases A–E (1st–5th row). All the terms are normalized with respect to .](/cms/asset/6132f2fc-12f8-463b-bf82-de7e2a10b18a/unht_a_1125732_f0004_oc.jpg)
Gao et al. [Citation53] recently proposed the following model T2 for unity Le flames in the following manner:
Here the model given by Eq. (8) has been extended in order to account for the effects of Le in the following manner:
In Eq. (9ii) accounts for the strengthening of heat release effects with decreasing Le as suggested by the scaling estimate given by Eq. (7i). The parameter
is a thermo-chemical parameter, which provides information regarding the SDR-weighted dilatation rate
[Citation34, Citation36, Citation65, Citation66]. The thermo-chemical parameter
accounts for the correlation between
and ρNc within the flame front. It is possible to approximate fb(c) as fb(c) = 1/|∇c|L [Citation65, Citation66], which enables one to evaluate
from laminar flame data. The thermo-chemical parameter
is also affected by Le and it is equal to 0.52, 0.67, 0.71, 0.78, and 0.79, respectively, for the Le = 0.34, 0.6, 0.8, 1.0, and 1.2 flames considered here. The predictions of Eq. (9) are compared to the predictions of Eq. (8) and T2 extracted from DNS data in , which shows that Eq. (9) satisfactorily predicts the quantitative behavior of T2 for a range of different values of Δ for flames with Le ranging from 0.34 to 1.2. Eq. (9) becomes exactly equal to Eq. (8) for the Le = 1.0 case and thus the predictions of Eqs. (8) and (9) cannot be distinguished from each other for case D in .
3.3. Modeling of the scalar turbulence interaction term T3
The variations of the mean values of T3 conditional on are shown in for cases A–E at Δ ≈ 0.4δth, 1.6δth, and 2.8δth. shows that T3 assumes predominantly negative values throughout the flame brush for cases A–C, but this term exhibits weak positive values toward both the unburned gas sides of the flame brush before assuming mostly negative values for the major portion of the flame brush in cases D and E. The term T3 can be expressed as follows [Citation21, Citation28, Citation34, Citation45–Citation48]:
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![Figure 5. Variations of T3 (Display full size) and (T3)res (Display full size) conditionally averaged in bins of along with the predictions of Eqs. (12i) and (12iii) (Display full size) and Eqs. (13i) and (13ii) (Display full size) for Δ ≈ 0.4δth (1st column), Δ ≈ 1.6δth (2nd column), and Δ ≈ 2.8δth (3rd column) in cases A–E (1st–5th row). All the terms are normalized with respect to .](/cms/asset/1d8d4826-8a3a-4293-9c31-9491c34393ef/unht_a_1125732_f0005_oc.jpg)
The effects of ∇c alignment with ea, on T3 can be scaled in the following manner [Citation28]:
The contribution of ∇c alignment with eγ on T3 can be scaled as follows [Citation28]:
The Lewis number Le dependence in Eq. (11i) (with n > 1) accounts for the greater extent of ∇c alignment with ea for the flames with Le << 1.0. Gao et al. [Citation28] proposed the following scaling estimate of the resolved part of T3:
A comparison of Eqs. (11i)–(11iii) reveals that the contribution of (T3)res to T3 is expected to weaken with increasing Δ, and this behavior can indeed be seen from , which shows that the magnitude of (T3)res decreases with increasing Δ.
Gao et al. [Citation53] utilized the scaling estimates given by Eqs. (11i) and (11ii) to propose a model for T3 for Le = 1.0 flames:
Gao et al. [Citation53] proposed the following expressions for the model parameter C3, C4, and :
It is worth noting that the terms and
are consistent with scaling estimates given by Eqs. (11i) and (11ii), respectively. However, a comparison between Eq. (11i) and
reveals that the increased alignment of ∇c with eα for small values of Le as a result of the strengthening of flame normal acceleration is not accounted for by the model given by Eq. (12ii). The effects of flame normal acceleration are expected to weaken with increasing Karlovitz number as the reacting flow field exhibits some attributes of passive scalar mixing for large values of Karlovitz number in the broken reaction zones regime [Citation55]. This behavior is mimicked here by KaΔ dependence of C4in Eq. (12iii).
The predictions of Eq. (12i) with the model parameters given by Eq. (12ii) are compared to T3 extracted from DNS data in , which shows that Eq. (12i) adequately captures the qualitative and quantitative behaviors of T3 for the Le ≈ 1.0 cases considered here (e.g., cases C–E); however, this model has been found to underpredict the magnitude of the negative contribution of T3 in the Le << 1.0 cases (e.g., cases A and B) for Δ > δth. It has already been noted that the increased extent of scalar gradient destruction in the Le << 1.0 flames, due to the preferential alignment of ∇c with eα under strong actions of flame normal acceleration, is not addressed in the model given by Eq. (12i). Thus, this model underpredicts the negative contribution of T3 for the flames with Le << 1.0. Here Eq. (12i) has been modified in the following manner to account for non-unity Lewis number effects:
The involvement of the function Γ(Le) in Eq. (13i) accounts for the strengthening of ∇c alignment with eα under strong actions of flame normal acceleration in flames with small values of Lewis number. The presence of helps Eq. (13i) capture the qualitative behavior of T3 across the flame brush. It can be seen from that the model given by Eq. (13i) provides satisfactory qualitative and quantitative predictions of T3 for all the flames with different values of Le for a range of Δ. It is worth noting that Eq. (13i) approaches Eq. (12i) for Le = 1.0 and thus the predictions of Eqs. (12i) and (13i) cannot be distinguished from each other for case D in .
3.4. Modeling of the combined reaction, dissipation, and diffusivity gradient contribution [T4 − D2 + f(D)]
The variations of the mean values of [T4 − D2 + f(D)] conditional on are shown in for A–E for Δ ≈ 0.4δth, 1.6δth, and 2.8δth. It can be seen from that [T4 − D2 + f(D)] acts as a sink (source) term toward the burned (unburned) gas side of the flame brush for Δ ≈ 0.4δth and Δ ≈ 1.6δth; however, the mean value of [T4 − D2 + f(D)] conditional on
assumes predominantly negative values for Δ ≈ 2.8δth. shows that the order of magnitudes of T4, (−D2), and f(D) remain comparable according to the scaling analysis by Gao et al. [Citation28] and their magnitudes are expected to increase with decreasing Le. Furthermore, the scaling estimates of (T4)res, (−D2)res, and {f(D)}res in suggest that their contributions are expected to weaken with increasing Δ, where (T4)res, (−D2)res, and {f(D)}res are the resolved components of T4, (−D2), and f(D), which are given by
Figure 6. Variations of [T4 + f(D) − D2] (
![](/cms/asset/98acee79-b4e9-459a-9330-5476ad35f820/unht_a_1125732_ilg0008_oc.jpg)
![](/cms/asset/6fb61bb1-3658-435f-8794-7af9e146608f/unht_a_1125732_ilg0006_oc.jpg)
![](/cms/asset/f135e6b3-8111-427e-9151-f576c989fd7a/unht_a_1125732_ilg0007_oc.jpg)
![Figure 6. Variations of [T4 + f(D) − D2] (Display full size) and [(T4)sg − (D2)sg + {f(D)}sg] (Display full size) conditionally averaged in bins of along with the predictions of Eqs. (15i) and (15ii) (Display full size) and Eq. (16) (Display full size) for Δ ≈ 0.4δth (1st column), 1.6δth (2nd column), and 2.8δth (3rd column) in cases A–E (1st–5th row). All the terms are normalized with respect to .](/cms/asset/1aaefe5a-e6b7-422d-a8c9-48fb630ccb98/unht_a_1125732_f0006_oc.jpg)
Thus, the sub-grid components (T4)sg = T4 − (T4)res, (−D2)sg = −D2 + (D2)res, and {f(D)}sg = f(D) − {f(D)}res are expected to play major roles for Δ >> δth. The aforementioned behaviors of the resolved and sub-grid components of T4, (−D2), and f(D) can be confirmed from . It can be seen from that the magnitudes of (T4)sg, (−D2)sg, and {f(D)}sg remain of the order of for Δ >> δth; however, their magnitudes are expected to increase with decreasing Le, which can indeed be substantiated from .
Gao et al. [Citation53] utilized to model [T4 + f(D) − D2] together for unity Lewis number flames by extending an existing RANS model [Citation22, Citation34, Citation44, Citation47, Citation48, Citation51] in the following manner:
The involvement of in Eq. (15i) is required for capturing the qualitative behavior of [T4 − D2 + f(D)] across the flame brush, whereas fTD approaches unity for small values of Δ as the terms get fully resolved (i.e.,
). The transition from positive to negative contribution of [T4 + f(D) − D2] with increasing Δ has been accounted for by
. The predictions of Eq. (15i) are shown in , which shows that this model captures both the qualitative and quantitative behaviors of [T4 + f(D) − D2] for the Le ≈ 1.0 cases considered here (e.g., cases C–E); however, this model underpredicts the magnitude of [T4 + f(D) − D2] significantly for the Le << 1.0 cases (e.g., cases A and B). It is worth noting that the model given by Eq. (15i) does not account for the increased magnitude of {T4 − D2 + f(D)}sg for small values of Le (see ); hence, perhaps it is not surprising that this model under-predicts the magnitude of [T4 + f(D) − D2] for the flames with Le << 1.0 (e.g., cases A and B). The increased magnitude of [T4 + f(D) − D2] for the small values of Le is accounted for by modifying Eq. (15i) in the following manner:
It is worth noting that the combined contribution of the terms D1, T4, f(D), and (−D2) can be expressed in the following manner if the SDR transport equation is derived based on the kinematic form of the progress variable transport equation (i.e., Dc/Dt = Sd|∇c|) [Citation34, Citation47]:
3.5. Implications of model implementation
The newly proposed models for the unclosed terms of the SDR transport equation are summarized in for the future potential users of these models. It is worth noting that the flamelet assumption is invoked while deriving these models, so they are expected to remain valid in the corrugated flamelets and thin reaction zones regimes of turbulent premixed combustion [Citation55]. The scaling estimates in indicate that the terms T2, T3, T4, (−D2), and f(D) remain leading-order contributors to the SDR
transport and the magnitude of T1 remains negligible compared with the terms T2, T3, T4, (−D2), and f(D) irrespective of Damköhler and turbulent Reynolds numbers. This is consistent with the observations made from . However, the turbulent transport term T1 still needs to be modeled and included in the model implementation for LES for numerical stability.
Table 4. Summary of the proposed models for the unclosed terms of the SDR
transport equation (Eq. (3)) in this analysis.
4. Conclusions
The effects of global Lewis number Le on the modeling of the unclosed terms of the transport equation of Favre-filtered SDR have been analyzed based on a priori analysis of a DNS database of freely propagating statistically planar turbulent premixed flames with Le ranging from 0.34 to 1.2. It has been found that Le has profound influence on the statistical behavior of the unclosed terms of
transport arising from turbulent transport T1, density variation due to heat release T2, alignment of scalar and velocity gradients T3, correlation between the gradients of reaction rate and reaction progress variable T4, molecular dissipation (−D2), and diffusivity gradients f(D), and detailed physical explanations have been provided for the observed non-unity Lewis number effects. Recently proposed models for T1, T2, T3, T4, (−D2), and f(D) for unity Lewis number flames have been extended here to account for the effects of Le based on the scaling estimates of these unclosed terms [Citation28]. The newly proposed models have been found to satisfactorily predict the unclosed terms obtained from explicitly filtered DNS data for a range of Δ for different values of Le. However, it is still essential to implement these models into actual LES simulations for the purpose of a posteriori assessment. Moreover, these models need to be further validated based on detailed chemistry-based DNS simulations. Further validation of these models will form the basis of future investigations.
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