Abstract
The statistical behaviour of turbulent scalar flux and modelling of its transport have been analysed for both major reactants and products in the context of Reynolds Averaged Navier Stokes simulations using a detailed chemistry Direct Numerical Simulation (DNS) database of freelypropagating H_{2} −air flames (with an equivalence ratio of 0.7) spanning the corrugated flamelets, thin reaction zones and broken reaction zones regimes of premixed turbulent combustion. The turbulent scalar flux in the cases representing the corrugated flamelets and thin reaction zones regimes of combustion exhibit predominantly countergradient transport, whilst a gradient transport has been observed for the broken reaction zones regime flame considered here. It has been found that the qualitative behaviour of the various terms of the turbulent scalar flux transport equation for the major species such as H_{2}, O_{2} and H_{2}O in the cases representing the corrugated flamelets and thin reaction zones regimes of combustion are mostly similar, whilst the behaviour is markedly different for the case representing the broken reaction zone regime. However, the terms for the scalar flux transport equation for H_{2} and O_{2} show same signs whereas the corresponding terms for H_{2}O show signs opposite to those for H_{2} and O_{2}. The performances of the wellestablished existing models for the unclosed terms of the turbulent scalar flux transport equation have been found to be similar for H_{2}, O_{2} and H_{2}O Some of the existing models for turbulent flux, pressure gradient, molecular diffusion and reaction contributions have been found to yield reasonable performance for the cases representing the corrugated flamelets and thin reaction zones regimes but the existing closures for these terms have been found to be mostly inadequate for the broken reaction zones regime flames.
Introduction
One of the major challenges of turbulent scalar transport modelling in the context of Reynolds Averaged Navier Stokes (RANS) simulations involves the closure of turbulent scalar flux components. The most widely used closure of turbulent scalar flux assumes a gradient hypothesis. According to the gradient hypothesis, the turbulent scalar flux components \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for compressible turbulent flows are modelled as [1]:
where ρ is the fluid density, u_{i} is the i^{th} component of fluid velocity, Y_{α} is the scalar in question (here Y_{α} is taken to be the mass fraction of species α), μ_{t} is the eddy viscosity, \(Sc_{t}=\mu _{t}/\overline {\rho } D_{t}\) is the turbulent Schmidt number with D_{t} being the eddy diffusivity and \(\tilde {q}=\overline {\rho q}/\overline {\rho } \) and \(q^{\prime \prime }=q\tilde {q}\) are the Favre mean and fluctuations of a general variable q with the overline referring to the Reynolds averaging operation. The closure of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) using the gradient hypothesis has wellknown limitations [2,3,4,5,6,7,8]. For example, \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) and \((\partial \tilde {Y}_{\alpha } /\partial x_{i})\) are not collinearly aligned in turbulent channel flows. The difficulty in the modelling of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) is further exacerbated by the fact that the turbulent scalar flux shows a countergradient behaviour in turbulent premixed flames when the flame normal acceleration dominates over turbulent fluctuation [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. Due to the limitation of algebraic closures of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\), it is often necessary to solve a modelled transport equation of turbulent scalar flux components \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) in the context of secondmoment closure and probability density function based modelling methodologies [31,32,33].
Several analyses concentrated on the closures of turbulent fluxes of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) [34] and pressure gradient contributions [35,36,37] to the turbulent scalar flux transport for passive scalar mixing in nonreacting flows. However, the physics of turbulent scalar flux transport is considerably different in turbulent premixed flames due to the heat release arising from chemical reaction and selfinduced pressure gradients. The modelling of the unclosed terms of the transport equation of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for turbulent premixed flames was discussed by Bray et al. [9] for the flamelet regime of combustion, and the modelled transport equation of turbulent scalar flux was used by Lindstedt and Vaos [31] and Tian and Lindstedt [32] in RANS simulations of laboratory scale burners. The models for the unclosed terms have been assessed based on apriori Direct Numerical Simulation (DNS) analysis by Nishiki et al. [20] for flames representing the corrugated flamelets regime [38]. Chakraborty and Cant [23,24,25] demonstrated that the characteristic Lewis number has a significant influence on the statistical behaviours of the unclosed terms of the turbulent scalar flux transport equation based on apriori DNS analyses. Furthermore, the analysis of Chakraborty and Cant [24] proposed modifications to the existing model expressions for the unclosed terms of the turbulent scalar flux transport equation for nonunity Lewis number and also for the thin reaction zones [37] combustion regime. In a subsequent analysis [27] the same authors addressed the effects of turbulent Reynolds number on the closures of the various terms of the turbulent scalar flux transport equation. Recently, Lai et al. [39] also analysed the nearwall behaviours of the unclosed terms of the turbulent scalar flux transport equation based on DNS data of headon quenching of statistically planar turbulent premixed flames with different turbulence intensities and characteristic Lewis numbers. Based on this analysis Lai et al. [39] proposed nearwall modifications to the models of the unclosed terms of the turbulent scalar flux transport equation. In this regard, it is worthwhile to mention that the usage of turbulent scalar flux transport equation is rare in LES, whereas the turbulent scalar flux transport equation is used in the probability density function (PDF) method coupled with secondmoment closure [31, 32]. Thus, an analysis of the turbulent scalar flux transport in the context of Large Eddy Simulations (LES) will be of limited relevance. As a result, this analysis focuses primarily on RANS modelling.
To date, all the analyses [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30, 39] on the turbulent scalar flux transport in premixed combustion modelling have been carried out in the context of simple chemistry and transport. With the advancement of highperformance computing, it is now possible to incorporate detailed chemical mechanisms and solve transport equations for several species in engineering simulations [40, 41]. It is not possible to translate the results for the turbulent scalar flux for one major species to any other major species by a linear transformation. The spatial distributions of each major species are different and thus the correlations between velocity and scalar fluctuations are different for every major species. For this reason, it is necessary to analyse the turbulent scalar fluxes for different major species in the context of multispecies RANS simulations. Moreover, it is necessary to ascertain if the same models for the unclosed terms in the transport equation of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) work equally well for all the major species α in different regimes of combustion. The present analysis addresses this gap in the existing literature by the analysis of turbulent scalar fluxes \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) of H_{2},O_{2} and H_{2}O, and the wellestablished submodels of the transport equation of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) in the context of RANS using a threedimensional Direct Numerical Simulations (DNS) database of H_{2}air flames with an equivalence ratio of 0.7 (which ensures that the flames remain globally thermodiffusively neutral with respect to flame speed response to flame stretch [42]). The simulation parameters for this DNS database have been chosen in such a manner that the cases considered here represent typical combustion situations within the corrugated flamelets, thin reaction zones and broken reaction zones regimes of premixed turbulent combustion. The turbulent scalar fluxes \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) of H_{2},O_{2} and H_{2}O, and the transport equation of these scalar fluxes have been analysed using the aforementioned DNS database. Moreover, the modelling of the unclosed terms of the turbulent scalar flux transport equations will be discussed for the mass fractions of H_{2},O_{2} and H_{2}O.^{Footnote 1} In this respect, the main objectives of this paper are:

(a)
To analyse the statistical behaviours of turbulent scalar fluxes \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for H_{2},O_{2} and H_{2}O mass fractions (i.e. α = H_{2},O_{2} and H_{2}O) and the unclosed terms of the transport equation of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) in different regimes of combustion.

(b)
To provide physical explanations for the differences in the behaviour of turbulent scalar fluxes \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for different major species in different combustion regimes.

(c)
To assess the performances of the models for the unclosed terms of the transport equation of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for α = H_{2},O_{2} and H_{2}O in different combustion regimes.
The modelling attempts for the turbulent scalar flux transport in the context of turbulent reacting flows are relatively scarce [9, 20, 24, 27, 39] and all the existing analyses on turbulent premixed flames have been carried out for the flames in the flamelets regime using simple chemical mechanism [9, 20]. Until now no model has been proposed for the lowDamköhler number conditions in the broken reaction zones regime. In this paper, the turbulent scalar flux transport has been addressed in the context of detailed chemical mechanism and nonunity Lewis number for the very first time. It is not to be expected that this analsyis will provide solutions to the modelling challenges that are open in the literature for decades and therefore the current paper should be treated as a first step towards achieving the goal of having a unified model of turbulent scalar flux transport for all regimes of premixed combustion for a multispecies system.
The rest of the paper will be organised as follows. The mathematical background and numerical implementation pertaining to the current analysis are provided in the next two sections. Following that, results are presented and discussed. The main findings are summarised and conclusions are drawn in the final section of this paper.
Mathematical Background
The transport equation of the Favreaveraged mass fraction \(\tilde {Y}_{\alpha } =\overline {\rho Y_{\alpha } }/\overline {\rho } \) of species α is given by:
where \(\dot {\omega _{Y}}\) and D_{α} are the chemical reaction rate of species α and diffusivity of species α, respectively. The last term on the righthand side of Eq. 2 indicates turbulent transport of species α and one requires a model for the turbulent scalar flux \(\overline {\rho u_{j}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\). Using the transport equations of Y_{α} and u_{i}, it is possible to derive a transport equation for \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\), which takes the following form [9, 20, 23,24,25, 39]:
where τ_{ij} = μ[(∂u_{i}/∂x_{j}) + (∂u_{j}/∂x_{i})] − (2μ/3)δ_{ij}(∂u_{k}/∂x_{k}) is the viscous stress tensor, P is the pressure and μ is the dynamic viscosity. The term T_{1} is associated with turbulent transport of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\), whereas T_{2} and T_{3} represent generation of turbulent scalar flux \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) by mean species and velocity gradients respectively. The terms T_{4} and T_{5} denote the contributions of mean and fluctuating pressure gradients respectively. The combined contributions of T_{6} and T_{7} is referred to as the molecular diffusion term. The term T_{8} is the chemical reaction rate contribution to the turbulent scalar flux \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) transport. The statistical behaviours of T_{1} − T_{8} and their modelling will be discussed for turbulent scalar flux transports of H_{2},O_{2} and H_{2}O in Section 4 of this paper.
Numerical Implementation
In order to analyse the statistical behaviours of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\), and T_{1} − T_{8} a threedimensional detailed chemistry (9 species and 19 chemical reactions) DNS database of statistically planar H_{2}air flames with an equivalence ratio of 0.7 (i.e. ϕ = 0.7) was used [43]. The simulations have been conducted using a wellknown DNS code [43], which employs highorder finite differences (8^{th} order at the internal grid points and gradually reducing to a onesided 4^{th} order scheme at the nonperiodic boundary) and a high order RungeKutta (4^{th} order) scheme for spatial discretisation and explicit time advancement respectively. The reacting scalar field has been initialised using the steady laminar H_{2}air flame solution corresponding to ϕ = 0.7. The thermophysical properties are taken to be temperature dependent and are expressed according to CHEMKIN polynomials. The unburned gas temperature T_{0} is taken to be 300K, which leads to an unstrained laminar burning velocity S_{L} = 135.6cm/s and heat release parameter τ = (T_{ad} − T_{0})/T_{0} = 5.71 (where T_{ad} is the adiabatic flame temperature) under atmospheric pressure. The numerical implementation for this database has been discussed elsewhere [43] in detail and thus a brief description is provided here. Turbulent inflow and outflow boundaries are taken in the direction of mean flame propagation, and transverse boundaries are considered to be periodic. The inflow and outflow boundaries are specified using an improved Navier Stokes characteristic boundary conditions (NSCBC) technique [44]. The inflow turbulent velocity fluctuations are specified by scanning a plane through a frozen field of turbulent homogeneous isotropic incompressible velocity generated using a pseudospectral method [45] following the PassotPouquet spectrum [46]. The inflow values of normalised rootmeansquare turbulent velocity fluctuation u^{′}/S_{L}, turbulent length scale to flame thickness ratio l_{T}/δ_{th}, flame thickness to the Kolmogorov length scale ratio δ_{th}/η Damköhler number Da = l_{T}S_{L}/u^{′}δ_{th}, Karlovitz number Ka = (ρ_{0}S_{L}δ_{th}/μ_{0})^{0.5}(u^{′}/S_{L})^{1.5}(l_{T}/δ_{th})^{− 0.5} and turbulent Reynolds number Re_{t} = ρ_{0}u^{′}l_{T}/μ_{0} for all cases are presented in Table 1 where ρ_{0} and μ_{0} are the unburned gas density and viscosity, respectively, δ_{th} = (T_{ad} − T_{0})/max∇T_{L} is the thermal flame thickness (with ‘L’ denoting unstrained laminar flame quantities). Cases AC are representative of the corrugated flamelets (Ka < 1), thin reaction zones (1 < Ka < 100) and broken reaction zones (Ka > 100) regimes [38] of premixed turbulent combustion respectively. The domain size is 20mm × 10mm × 10mm (8mm × 2mm × 2mm) in cases A and B (case C) and the domain has been discretised by a uniform mesh of dimension 512 × 256 × 256 (1280 × 320 × 320). The mean inlet velocity has been adjusted to match the turbulent flame speed and the temporal evolution of the flame area has been monitored until a quasisteady state is reached. The statistical stationary state has been achived at a time corresponding to 1.0l_{T}/u^{′},6.8l_{T}/u^{′}6.7l_{T}/u^{′} for cases AC respectively and this simulation time remains comparable to several previous analyses [47,48,49]. For statistically planar flames, the directions normal to the mean direction of flame propagation (i.e. x_{1}direction) are statistically homogeneous. All the Reynolds/Favre averaging operations have been conducted by ensemble averaging the variables in the directions normal to the mean direction of flame propagation.
Results & Discussion
Flameturbulence interaction
The isosurfaces of nondimensional temperature c_{T} = (T − T_{0})/(T_{ad} − T_{0}) for cases AC are shown in Fig. 1, which indicates that the flame morphologies in these cases are significantly different from each other and interested readers are referred to Refs. [43, 50] for further discussion in this regard. The flame wrinkling can be quantified based on the normalised flame surface area A/A_{0} where the flame surface area A is evaluated as: \(A={\int }_{V} {\vert \mathrm {\nabla } c\vert dV} \) and A_{0} is the initial value of flame surface area based on the onedimensional steady state laminar flame solution. The quasisteady state values of A/A_{0} are 3.25, 5.0 and 3.25 for cases A, B and C respectively. The inlet turbulence intensity u^{′}/S_{L} increases from case A to B, which leads to a greater extent of flame wrinkling in case B than in case A. However, l_{T}/δ_{th} values in cases A and C are considerably different and thus, the extent of flame wrinkling in cases A and C remain comparable despite large differences in u^{′}/S_{L} values. It should be noted that Ka has not been modified here in isolation and thus the differences in behaviours of T_{1} − T_{8} originate not only due to the changes in the variation of Ka but also due to the changes in Da.
Statistical behaviour of turbulent scalar flux
In the case of statistically planar flames, \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) is the only nonzero component of turbulent scalar flux. The variations of \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\times 1/\rho _{0}S_{L}\vert Y_{\alpha u}Y_{\alpha b}\vert \) (where subscripts u and b refer to the values in unburned and burned gases respectively and the multiplier is used for normalisation) with \(\tilde {c}_{T}\) are shown in Fig. 2. The signs of \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for α = H_{2} and O_{2} are the same but are different to \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for α = H_{2}O. Moreover, it can be seen from Fig. 2 that the qualitative behaviour of \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for α = H_{2},O_{2} and H_{2}O does not change in cases A and B but the behaviour in case C is completely different from that in cases A and B. In order to understand this observation the variation of \(\mathrm {{\Lambda } = }~\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}/(\partial \tilde {Y}_{\alpha } /\partial x_{1})\times 1/\rho _{0}S_{L}\delta _{th}\) with \(\tilde {c}_{T}\) is shown for all cases in Fig. 3 for α = H_{2},O_{2} and H_{2}O. A positive (negative) value of \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}/(\partial \tilde {Y}_{\alpha } /\partial x_{1})\) is indicative of a gradient (countergradient) transport. Furthermore, for a gradient transport \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}/(\partial \tilde {Y}_{\alpha } /\partial x_{1})\) provides the densityweighted eddy diffusivity \(\overline {\rho } D_{t}\), whereas an unphysical negative \(\overline {\rho } D_{t}\) is obtained for a countergradient transport. Figure 3 indicates that a predominantly countergradient behaviour has been observed for turbulent scalar fluxes of H_{2}, O_{2} and H_{2}O for cases A and B, whereas a gradient type transport is obtained for these species in case C.
It is important to note that the results in Fig. 3 do not necessarily mean that countergradient (gradient) transport will always be obtained for the flames within the corrugated flamelets and thin reaction zones regimes (broken reaction zones regime). According to Veynante et al. [12], a gradient transport is obtained for N_{B} < 1, whereas a countergradient transport is favoured for N_{B} > 1, where N_{B} = τS_{L}/2αu^{′} is the Bray number with α being an efficiency function. The value of α is about 0.5 according to Veynante et al. [12] and thus τS_{L}/u^{′} can be taken to provide a measure of the Bray number N_{B}. The Bray number criterion proposed by Veynante et al. [12] is valid in an order of magnitude sense. The velocity jump due to flame normal acceleration arising from chemical heat release is taken to scale with τS_{L}, whereas the velocity jump due to turbulence scales with u^{′}. A countergradient transport is obtained when τS_{L} dominates over u^{′}, whereas a gradient transport is obtained when u^{′} dominates over τS_{L}. In cases A and B, τS_{L}/u^{′} remains greater than unity (= 8.16 for case A and 1.14 for case B) whereas it is smaller than unity (= 0.41) for case C and accordingly a countergradient behaviour has been observed for cases A and B whilst a gradient transport is obtained for case C.
Statistical behaviour of the terms of the turbulent scalar flux transport equation
The variations of T_{1} − T_{8} with \(\tilde {c}_{T}\) in cases AC are shown in Fig. 4 for α = H_{2},O_{2} and H_{2}O so that the relative magnitudes of these terms for different species can be compared for flames belonging to the different combustion regimes. The modelling of the terms of the turbulent scalar flux transport equation will be discussed in the subsequent subsections. The signs of these terms for H_{2}O are different from those of H_{2} and O_{2} for all cases because H_{2}O is a product species, whereas H_{2} and O_{2} are the reactants. It is worth noting that a positive (negative) contribution of T_{1} − T_{8} acts to produce a countergradient (gradient) transport of turbulent scalar flux \(\overline {\rho u_{1}^{\prime \prime }Y_{H_{2}O}^{\prime \prime }}\). By contrast, a negative (positive) value of T_{1} − T_{8} promotes a countergradient (gradient) transport of turbulent scalar fluxes of H_{2} and O_{2} (i.e. \(\overline {\rho u_{1}^{\prime \prime }Y_{H_{2}}^{\prime \prime }}\) and \(\overline {\rho u_{1}^{\prime \prime }Y_{O_{2}}^{\prime \prime }})\). The mean and fluctuating pressure gradient terms T_{4} and T_{5} play leading order roles in cases A and B. For case C, T_{4} and T_{5} remain comparable to the magnitudes of the contributions of T_{1}, T_{2}, T_{6} and T_{8}. The flame normal acceleration sets up a negative mean pressure gradient (i.e. \({\partial \overline {P}} / {\partial x_{1}}<0\) when the mean direction of flame propagation is in the negative x_{1}direction), which gives rise to negative (positive) values of T_{4} for reactants (products) because of negative (positive) values of \(\overline {Y_{\alpha }^{\prime \prime }}\). A negative (positive) fluctuation of \(Y_{\alpha }^{\prime \prime }\) for reactants (products) tends to induce a more negative pressure gradient and thus \(T_{5}=\overline {Y_{\alpha } ^{\prime \prime }(\partial P^{\prime }/\partial x_{1})} \) assumes mostly positive (negative) values for α = H_{2} and O_{2} (α = H_{2}O). The terms due to mean species and velocity gradients T_{2} and T_{3} exhibit positive (negative) values across the flame brush for H_{2} and O_{2}(H_{2}O) in cases A and B. In case C, T_{2} assumes positive (negative) values across the flame brush for H_{2} and O_{2}(H_{2}O), whereas the magnitude of T_{3} remains negligible in comparison to the contributions of T_{1}, T_{2}, T_{4}, T_{5}, T_{6} and T_{8} but this term assumes negative (positive) values within the flame brush for H_{2} and O_{2}(H_{2}O). For statistically planar flames with the mean direction of flame propagation aligned with x_{1}direction, the term \(T_{2}=\overline {\rho u_{1}^{\prime \prime }u_{1}^{\prime \prime }} (\partial \tilde {Y_{\alpha } }/\partial x_{1})\) assumes negative values for product species (e.g. H_{2}O) because \(\partial \tilde {Y_{\alpha } }/\partial x_{1}\) and \(\overline {\rho u_{1}^{\prime \prime }u_{1}^{\prime \prime }} \) assume positive values. By contrast, positive values of T_{2} are obtained for reactant species (e.g. H_{2} and O_{2}) in statistically planar flames because of predominantly negative values of \(\partial \tilde {Y_{\alpha } }/\partial x_{1}\). For statistically planar flames T_{3} can be expressed as: \(T_{3}=\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }} (\partial \tilde {u_{1}}/\partial x_{1})\) and thus the sign of T_{3} depends on \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }} \) because \((\partial \tilde {u_{1}}/\partial x_{1})\) is expected to be positive due to flame normal acceleration as a result of heat release. In cases A and B, \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }} \) assumes negative (positive) values within the flame brush for reactants (products), which leads to positive (negative) values of T_{3} for α = H_{2} and O_{2} (α = H_{2}O). By contrast, \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }} \) assumes positive (negative) values within the flame brush for reactants (products) in case C, which leads to negative (positive) values of T_{3} for α = H_{2} and O_{2} (α = H_{2}O).
The molecular diffusion terms T_{6} and T_{7} play marginal roles in cases A and B but T_{6} plays a leading order role in case C, especially for the turbulent scalar flux transport of H_{2}. It can be seen from Fig. 4 that the combined action of T_{6} and T_{7} acts to reduce the extent of countergradient transport in cases A and B, whereas it acts to reduce the extent of gradient transport in case C, and this behaviour remains unchanged for all species considered here. The combined contribution of T_{6} and T_{7} can be split into a molecular diffusion contribution (\(\propto \mathrm {\nabla \cdot } \overline {({{\rho D\nabla (u}_{1}^{\prime \prime }Y}_{\alpha }^{\prime \prime }))}\) and a dissipation contribution (\(\propto \overline {2\rho D\nabla Y_{\alpha }^{\prime \prime }\cdot \nabla u_{1}^{\prime \prime }})\). For high values of Re_{t} the molecular dissipation contribution dominates over the diffusion contribution (i.e. \(\vert \mathrm {\nabla \cdot } \overline {({{\rho D\nabla (u}_{1}^{\prime \prime }Y}_{\alpha }^{\prime \prime }))})\)<< \( \vert \overline {2\rho D\nabla Y_{\alpha }^{\prime \prime }\cdot \nabla u_{1}^{\prime \prime }}\vert )\). For a countergradient transport, the quantity \(\overline {2\rho D\nabla Y_{\alpha }^{\prime \prime }\cdot \nabla u_{1}^{\prime \prime }}\) assumes positive (negative) values for products (reactants) and thus the contribution of (T_{6} + T_{7}) acts to reduce the extent of countergradient transport in cases A and B. By contrast, \(\overline {2\rho D\nabla Y_{\alpha }^{\prime \prime }\cdot \nabla u_{1}^{\prime \prime }}\) assumes negative (positive) values for products (reactants) for a gradient transport, and thus it acts to reduce the extent of gradient transport in case C.
The reaction rate contribution T_{8} assumes positive (negative) values towards the unburned gas side of the flame brush, before becoming negative (positive) on the burned gas side of the flame brush for H_{2} and O_{2} (H_{2}O) for all cases, but for cases A and B this term remains small in comparison to the magnitudes of T_{4} and T_{5}, whereas its magnitude is comparable to T_{1}, T_{2}, T_{4}, T_{5} and T_{6} in case C. An increase (decrease) in reaction rate magnitude on the unburned (burned) gas side tends to induce a negative \(u_{1}^{\prime \prime }\) due to the decay of turbulence under the enhanced viscous action, and this leads to positive (negative) values towards the unburned gas side of the flame brush and negative (positive) values of T_{8} on the burned gas side of the flame brush for reactants such as α = H_{2} and O_{2} (for products such as α = H_{2}O) for all cases. However, this action is relatively stronger in case C than in cases A and B because flamegenerated turbulence effects are stronger in these cases. The flamegenerated turbulence acts to locally enhance the turbulence level in cases A and B, which counters the decay of turbulence with increased chemical activity and thus the relative magnitude of T_{8} is smaller than the leading order contributions of T_{4} and T_{5} in these cases in comparison to case C.
It is worth noting that the observed behaviours from Fig. 4 are consistent with the scaling analyses presented in Chakraborty and Cant [27] and Lai et al. [39], which indicate that T_{2} and T_{4} are expected to play dominant roles in the turbulent scalar flux transport and T_{3} is likely to be of marginal importance for high Reynolds number flames with small values of Da (i.e. Da < 1).
The terms T_{2} and T_{3} are closed terms in the context of secondmoment closure because \(\overline {\rho u_{i}^{\prime \prime }u_{j}^{\prime \prime }}\) and \(\overline {\rho u_{j}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) are already modelled in this framework. The terms T_{1}, T_{4}, T_{5}, T_{6}, T_{7} and T_{8} are unclosed and need closures in order to solve Eq. 2. This is often achieved by modelling \(\overline {\rho {u_{j}^{\prime \prime }u}_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\), (T_{4} + T_{5}), (T_{6} + T_{7}) and T_{8}, which will be discussed next in this paper.
Modelling of turbulent flux of scalar flux \(\overline {\rho {u_{j}^{\prime \prime }u}_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\)
The quantity \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) is the only nonzero component of \(\overline {\rho {u_{j}^{\prime \prime }u}_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for a statistically planar flame. The turbulent flux of \(\overline {\rho u_{i}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) is usually modelled for nonreacting flows in the following manner utilising the gradient hypothesis [34]:
This model will henceforth be referred to as the TDH (triplecorrelation DalyHarlow) model in this paper. Equation 4 is based on a gradient hypothesis and thus is incapable of addressing countergradient behaviour. Moreover, Eq. 4 does not account for flame normal acceleration effects arising from chemical heat release which are responsible for countergradient transport.
Subject to the assumption of a bimodal probability density function of c (i.e. P(c) with impulses at c= 0 and c= 1) accordiong to the BrayMossLibby (BML) analysis, the Favreaverage velocity component takes the following form: \(\tilde {u}_{j}\mathrm {=}\overline {(u_{j})}_{P}\tilde {c}\mathrm {+(1}\tilde {c}\mathrm {)}\overline {(u_{j})}_{R}\mathrm {+}O\mathrm {(1/}Da\mathrm {)}\), which upon using in \(\overline {\rho u^{\prime \prime }_{i}u_{j}^{\prime \prime }c^{\prime \prime }} \mathrm {=}{\int }_{\mathrm {\infty } }^{\mathrm {\infty } } {\int }_{\mathrm {\infty } }^{\mathrm {\infty } } {\int }_{0}^{1}\)\({\rho \mathrm (u_{i}\mathrm {}\tilde {u}_{i}\mathrm )(u_{j}\mathrm {}\tilde {u}_{j}\mathrm {)(}c\mathrm {}\tilde {c}\mathrm {)}P\mathrm {(}u_{i}\mathrm {;}u_{j};c\mathrm {)}du_{i}du\!_{j}dc} \) provides [27, 39]:
The first term on the righthand side represents the reacting contribution to \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }c^{\prime \prime }} \), whereas the combined action of second and third terms represent the effects of turbulence on \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }c^{\prime \prime }} \). The last term O(1/Da) originates from the interior of the flame, and this contribution becomes negligible for Da≫ 1. Chakraborty and Cant [24, 27] proposed an alternative model of \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) by incorporating the reacting contribution according to the BrayMossLibby (BML) analysis and the turbulent contribution in Eq. 5i is accounted for by the TDH model. The model by Chakraborty and Cant [24, 27] is given by:
Equation 5ii will henceforth be referred to as the CC model in this paper. The second term on the right hand side of Eq. 5ii is capable of predicting countergradient transport and includes the effects of flame normal acceleration due to chemical heat release (i.e.. \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}(Y_{\alpha u} Y_{\alpha b})/[\overline {\rho } (Y_{\alpha u}\tilde {Y}_{\alpha } )(\tilde {Y}_{\alpha } Y_{\alpha b})]+a_{3}\sqrt {\overline {\rho u_{1}^{\prime \prime }u_{1}^{\prime \prime }}/\overline {\rho } } )\) in Eq. 5ii accounts for the velocity jump across the flame brush with the first term representing the effects of heat release, whereas the second term represents the effects of turbulent velocity fluctuations).
The predictions of the TDH and CC models are compared to \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) extracted from DNS data in Fig. 5 for α = H_{2},O_{2} and H_{2}O. In cases A and B, \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) assumes negative (positive) values towards the unburned (burned) gas side of the flame brush for α = H_{2} and O_{2} but just the opposite behaviour is obtained for α = H_{2}O. In case C, \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) assumes predominantly positive values for α = H_{2} and O_{2}, whereas predominantly negative values of \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) are obtained for α = H_{2}O.
It can be seen from Fig. 5 that both TDH and CC models do not adequately predict \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) in case C for α = H_{2},O_{2} and H_{2}O. The TDH model predicts an incorrect sign of \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for cases A and B for α = H_{2},O_{2} and H_{2}O, which implies that \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) exhibits countergradient behaviour in these cases. In cases A and B, the CC model has been found to accurately predict the quantitative behaviour of \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{H_{2}}^{\prime \prime }}\) and \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{O_{2}}^{\prime \prime }}\) on the unburned gas side of the flame brush, but does not predict the correct trend on the burned gas side of the flame brush and its prediction becomes comparable to the TDH model. This implies that the magnitude of the second term on the right hand side of Eq. 5i–5iii becomes small in comparison to the first term towards the burned gas side of the flame brush for α = H_{2} and O_{2} in cases A and B. The CC model captures the correct qualitative behaviour of \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{H_{2}O}^{\prime \prime }}\) for case A and the quantitative agreement also remains reasonable. However, in case B the CC model predicts the qualitative and quantitative behaviours of \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{H_{2}O}^{\prime \prime }}\) towards the leading edge and the middle of the flame brush and its prediction becomes qualitatively and quantitatively similar to the TDH model towards the burned gas side. The predictions of the CC model can be improved further by modifying the model parameters C_{Cs} and a_{3} and the optimum values of these parameters for cases AC are listed in Table 2. These optimum values have been parameterised for α = H_{2},O_{2} and H_{2}O in the following manner:
where \(Ka_{L}=\tilde {\varepsilon }^{0.5}\delta _{th}^{0.5}S_{L}^{1.5}\) is the local Karlovitz number. The predictions of the CC model with the model parameters given by Eq. 5iii are henceforth referred to as the CCM model in this paper. It can be seen from Fig. 5 that the predictions of CCM model show better agreement with DNS data than the TDH and CC models and this improvement is especially evident in case C where the CCM still does not capture the correct behaviour for \(\tilde {c}_{T}<0.5\).
It is worth noting that the TDH model was originally proposed for nonreacting flows using the gradient hypothesis, and thus does not include the effects of flame normal acceleration due to chemical heat release and therefore is not equipped to predict the countergradient behaviour. The effects of heat release remain significant even for case C in spite of large values of Ka and thus, it is perhaps not surprising that this model does not perform satisfactorily for all species in all cases considered here. This behaviour has been found to be consistent with previous analyses based on simple chemistry DNS results [24, 27, 39]. This implies that the choices of species and chemical mechanism do not have significant influence on the agreement of the TDH model with \(\overline {\rho {u_{1}^{\prime \prime }u}_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) extracted from DNS data.
The functional form of the second term on the right hand side of Eq. 5ii is derived based on a presumed bimodal distribution of Y_{α} with peaks at Y_{αu} and Y_{αb}, which is strictly valid for Da > 1 and Ka < 1. Thus, the CC model works relatively satisfactorily for case A (where Da > 1 and Ka < 1) but the probability density function of Y_{α} does not remain bimodal for Ka > 1 and thus its performance worsens progressively with increasing Ka (i.e. from case B to case C). Some recent analyses [51, 52] focussed on modelling of the PDF of c where non bimodal distribution is realised.
Modelling of the pressure gradient terms ( T _{4} + T _{5} )
The variations of the normalised values of (T_{4} + T_{5}) with \(\tilde {c}_{T}\) in cases AC are shown in Fig. 6 for α = H_{2},O_{2} and H_{2}O. It can be seen from Fig. 6 that the net contribution of (T_{4} + T_{5}) assumes mostly negative values for the major reactants such as α = H_{2} and O_{2} in all cases, whereas (T_{4} + T_{5}) exhibits positive values for α = H_{2}O. The flame normal acceleration sets up a negative mean pressure gradient \(\partial \overline {P}/\partial x_{1}\) in the statistically planar flames. According to BML analysis, one obtains: \(\overline {Y_{\alpha }^{\prime \prime }}=\overline {\rho } (Y_{\alpha u}\tilde {Y}_{\alpha } )(\tilde {Y}_{\alpha } Y_{\alpha b})\tau /\rho _{0}(Y_{\alpha u}Y_{\alpha b})\) [9] and even though this expression is not strictly valid for Da < 1 it can still be used in a qualitative sense. As Y_{αu} > Y_{αb} (Y_{αu} < Y_{αb}) for reactants (products) and \((Y_{\alpha u}\tilde {Y}_{\alpha } )(\tilde {Y}_{\alpha } Y_{\alpha b})\) is positive semidefinite (i.e. \((Y_{\alpha u}\tilde {Y}_{\alpha } )(\tilde {Y}_{\alpha } Y_{\alpha b})\ge 0)\), the quantity \(\overline {Y_{\alpha }^{\prime \prime }}\) assumes negative (positive) values for reactants (products). Chakraborty and Cant [24] proposed \(\overline {Y_{\alpha }^{\prime \prime }}=\overline {\rho } \tilde { Y_{\alpha }^{\prime \prime 2}}\tau /\rho _{0}\left (Y_{\alpha u}Y_{\alpha b} \right )\) for both Da > 1 and Da < 1 combustion, and this expression remains valid for all cases considered here for α = H_{2},O_{2} and H_{2}O. Thus, the negative (positive) values of \(\overline {Y_{\alpha }^{\prime \prime }}\) for the major reactant (product) species lead to negative (positive) values of \(T_{4}=\overline Y_{\alpha }^{\prime \prime } (\partial \overline P /\partial x_{1})\) (see Fig. 4). A comparison between Figs. 4 and 6 reveals that although the fluctuating pressure gradient term T_{5} locally assumes values with a different sign to that of T_{4}, the net contribution of (T_{4} + T_{5}) follows the sign of the mean pressure gradient term T_{4}.
The mean and fluctuating pressure gradient terms are often modelled together because of their similar origin [2]. Several models are available in the existing literature for (T_{4} + T_{5}) (see Refs. [23, 27, 39]). Some of the models for (T_{4} + T_{5}), which were originally proposed for nonreacting flows, take the following form [2]:
where C_{1c},C_{2c},C_{3c} and C_{4c} are the model parameters. Launder [35] suggested that C_{1c} = 3.0,C_{2c} = 0,C_{3c} = 0 and C_{4c} = 0.4, and this model will henceforth be referred to as the PL model. Craft [36] adopted a similar model (referred to as the PC model) with C_{1c} = 3.0,C_{2c} = 0.5,C_{3c} = 0 and C_{4c} = 0. An alternative model (PD model) was suggested by Durbin [37] where C_{1c} = 2.5,C_{2c} = 0,C_{3c} = 0 and C_{4c} = 0.45. Jones [53] and Bradley et al. [54] modelled (T_{4} + T_{5}) in the following manner:
where C_{ϕ1} = 3.0 andC_{ϕ2} = 0.5 are taken for the model by Jones [53] (PJ model) , whereas C_{ϕ1} = 3.0 and C_{ϕ2} = 0 are considered for the model by Bradley et al. [54] (PB model). Lindstedt and Vaos [31] proposed another alternative model (PLV model) as:
where C_{As} = 1/3 and G_{il} is the generalised Langevin coefficient which is a function of Reynolds stress \(\overline {\rho u_{i}^{\prime \prime }u_{j}^{\prime \prime }} \) and the mean velocity gradient \({\partial \tilde {u_{i}}} / {\partial x_{j}}\) [31]. It is worth noting that \(\overline Y_{\alpha }^{\prime \prime } \) was evaluated using the BML relation: \(\overline Y_{\alpha }^{\prime \prime } =\overline {\rho } (Y_{\alpha u}\tilde {Y}_{\alpha } )(\tilde {Y}_{\alpha }Y_{\alpha b})\tau /\rho _{0}(Y_{\alpha u}Y_{\alpha b})\) in Refs. [31, 54, 55] but for the current apriori analysis \(\overline Y_{\alpha }^{\prime \prime } \) is extracted from DNS data. In all cases considered here, \(\overline {Y_{\alpha }^{\prime \prime }}\) can be modelled as: \(\overline {Y_{\alpha } ^{\prime \prime }}=\overline {\rho } \tilde { Y_{\alpha }^{\prime \prime 2}}\tau /\rho _{0}(Y_{\alpha u}Y_{\alpha b})\) (not shown here).
Nishiki et al. [20] also proposed a model (PN model) based on apriori simple chemistry DNS analysis for flames in the corrugated flamelets regime in the following manner:
where C_{D} = 0.8,C_{E1} = 0.38 and C_{E2} = 0.66 are the model constants. The first term on the right hand side of the PN model originates from the BML relation for T_{4} [9].
The predictions of all the aforementioned models are compared to (T_{4} + T_{5}) extracted from DNS data in Fig. 6 for α = H_{2},O_{2} and H_{2}O. Figure 6 shows that the PL, PC and PD models fail to capture both qualitative and quantitative behaviours of (T_{4} + T_{5}) in cases A and B irrespective of the choice of α. By contrast, these models predict the qualitative behaviour of (T_{4} + T_{5}) satisfactorily in case C. These models overpredict the magnitude of (T_{4} + T_{5}) towards the unburned gas side of the flame brush, whereas quantitative agreement with DNS data remains reasonable towards the burned gas side of the flame brush. The PL, PC and PD models were originally proposed for incompressible nonreacting flows [2, 34,35,36] where the contribution of \(T_{4}=\overline Y_{\alpha }^{\prime \prime } \partial \overline P /\partial x_{i}\) is identically zero and thus its contribution was ignored. It can be seen from Fig. 4 that the contribution of T_{4} remains significant for the cases considered here irrespective of the choice of α. A comparison of cases AC in Fig. 4 shows that the contribution of T_{4} in comparison to T_{5} diminishes from case A to case C, and thus the PL, PC and PD models are found to be more successful in capturing the statistical behaviour of (T_{4} + T_{5}) in case C. The case C belongs to the broken reaction zones regime and thus it shows some attributes of nonreacting flows, which is also reflected in the more satisfactory performance of the PL, PC and PD models than in cases A and B.
The PJ, PB and PLV models exhibit very similar behaviour. All three models capture the qualitative behaviour of (T_{4} + T_{5}) in case A for α = H_{2},O_{2} and H_{2}O, but these models underpredict (overpredict) the magnitude of (T_{4} + T_{5}) towards the unburned (burned) gas side of the flame brush. In case B, these models fail to predict the behaviour of (T_{4} + T_{5}) on the unburned gas side of the flame brush, but capture both quantitative and the qualitative behaviours on the burned gas side of the flame brush. The PJ and PB models perform satisfactorily in case C for α = H_{2},O_{2} and H_{2}O but the PLV model does not capture the qualitative and quantitative behaviours of (T_{4} + T_{5}) in this case for all the major species considered here.
The PN model captures the qualitative behaviour of (T_{4} + T_{5}) better than the other alternative models in case A but overpredicts the magnitude. In case B, the PN model captures the qualitative behaviour, but underpredicts the magnitude on the unburned gas side and overpredicts the magnitude on the burned gas side of the flame brush. However, in case C, the PN model fails to predict the qualitative behaviour and the magnitude of (T_{4} + T_{5}) on the unburned gas side, whereas it accurately captures the behaviour on the burned gas side of the flame brush but overpredicts the magnitude of (T_{4} + T_{5}). The PN model was originally proposed for strict flamelet combustion (i.e. Da > 1 and Ka < 1) and these assumptions are rendered invalid for the broken reaction zones regime in case C and thus this model does not perform satisfactorily in this case for all species considered here.
It is worth noting that the PL, PC, PD models, which do not account for the leading order contribution of \(T_{4}=\overline Y_{\alpha }^{\prime \prime } \partial \overline P /\partial x_{i}\), are not successful in capturing (T_{4} + T_{5}) extracted from DNS data. However, the PJ, PB and PN models, which include \(T_{4}=\overline Y_{\alpha }^{\prime \prime } \partial \overline P /\partial x_{i}\) are more successful in capturing the behaviour of (T_{4} + T_{5}) extracted from DNS data than the PL, PC, PD models. The model parameter and the model expression for the PLV model have been calibrated for the flamelet regime of combustion and thus it is perhaps unsurprising that this model does not perform satisfactorily in case C representing the broken reaction zones regime. The first term on the right hand side of the PN model (9) assumes a bimodal distribution of c, which is not realised in cases B and C and thus the prediction of the PN model exhibits some inaccuracies especially in these cases. It is possible that the methodologies, which parameterise the pdf of c when the presumed bimodal pdf with impulses at c = 0 and c = 1.0 is not realised, can be more successful in deriving improved models for (T_{4} + T_{5}) for nonflamelet regime of combustion.
Modelling of the molecular diffusion terms ( T _{6} + T _{7} )
The variations of normalised (T_{6} + T_{7}) with \(\tilde {c}_{T}\) in cases AC are shown in Fig. 7 for α = H_{2},O_{2} and H_{2}O. It can be seen from a comparison between Figs. 2 and 7 that (T_{6} + T_{7}) assumes a sign which is opposite to \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) for all species α in all cases. These terms tend to oppose the dominant behaviour of the turbulent scalar flux \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\). In cases A and B, (T_{6} + T_{7}) assumes positive (negative) values for α = H_{2} and O_{2} (α = H_{2}O), whereas \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) exhibits negative (positive) values throughout the flame brush [9, 12, 24, 27, 39]. By contrast, in case C, (T_{6} + T_{7}) assumes negative (positive) values for α = H_{2} and O_{2} (α = H_{2}O), whereas \(\overline {\rho u_{1}^{\prime \prime }Y_{\alpha }^{\prime \prime }}\) exhibits a positive (negative) value throughout the flame brush.
According to Bray et al. [9] (T_{6} + T_{7}) is modelled as:
where \(\overline {\dot {\omega _{Y_{\alpha } }}}\) is the chemical reaction rate of α, and K_{1} = 0.85 is the model constant. The model given by Eq. 10 will henceforth be referred to as the DBML model. An alternative model (i.e. DN model) was proposed by Nishiki et al. [20] in the following manner:
where C_{F} = 0.4 is a model constant. It is worthwhile to note that both Eqs. 10 and 11 are directly proportional to the mean reaction rate \(\overline {\dot {\omega _{Y_{\alpha } }}}\) and thus these models predict nonzero values of (T_{6} + T_{7}) within the flame brush.
The predictions of the DBML, DN and DC models are compared to (T_{6} + T_{7}) extracted from DNS data in Fig. 7. It can be seen from Fig. 7 that both DBML and DN models satisfactorily capture the qualitative behaviours of (T_{6} + T_{7}) for α = H_{2},O_{2} and H_{2}O. However, both DBML and DN models overpredict the magnitudes of (T_{6} + T_{7}) in cases A and B but the extent of overprediction is relatively smaller in the case of the DN model.
The DBML analysis was originally proposed for the strict flamelet regime (i.e. Da > 1 and Ka < 1) but even then this model significantly overpredicts (T_{6} + T_{7}) in case A where the conditions in terms of Da and Ka for which the model was proposed are satisfied (using K_{1} = 0.2 instead of 0.85 provides satisfactory quantitative agreement with DNS data). The same can be said for the DN model where C_{F} = 0.2 instead of 0.4 yields good quantitative agreement with DNS data. Thus, the application of these models for case C (where Da < 1 and > 1) is beyond the scope of their validity. In spite of the above limitation, the DBML model predicts the correct sign of (T_{6} + T_{7}) for α = H_{2},O_{2} and H_{2}O in case C and the quantitative agreement with DNS data also remains reasonable. The choice of characteristic dissipation timescale \(K_{1}\overline {\dot {\omega _{Y_{\alpha }}}}\mathrm { (}Y_{\mathrm {\alpha u}}\mathrm {}Y_{\mathrm {\alpha b}}\mathrm {)/}[\overline {\rho } \mathrm {(}Y_{\mathrm {\alpha u}}\mathrm {}\tilde {Y}_{\mathrm {\alpha }}\mathrm {)(}\tilde {Y}_{\mathrm {\alpha }} \mathrm {}Y_{\mathrm {\alpha b}}\mathrm {)]}\) in the DBML model may not be appropriate for lowDamköhler number (i.e. Da < 1) combustion in case C and this might be one of the reasons behind the overprediction of the DBML model in this case. The DN model is strictly valid only for countergradient transport and it predicts wrong sign for gradient transport [24, 27, 39]. Thus, the DN model fails to predict the correct qualitative behaviour of (T_{6} + T_{7}) for α = H_{2},O_{2} and H_{2}O in case C where a gradient transport is obtained (see Fig. 4).
The optimum values of K_{1} for different cases for α = H_{2},O_{2} and H_{2}O are listed in Table 3. As the DN model cannot predict the correct sign in the case of gradient transport, the optimum values C_{F} for cases AC have not been reported in Table 3. It can be seen that the optimum K_{1} values for cases AC are different. Moreover, optimum values of K_{1} for α = H_{2} are different from the corresponding optimum values for α = O_{2} and H_{2}O Furthermore, optimum values of K_{1} are similar for α = O_{2} and H_{2}O. The Lewis number of H_{2} is significantly smaller than unity (i.e. \(Le_{H_{2}}\ll 1)\), whereas the Lewis numbers for α = O_{2} and H_{2}O (i.e. \(Le_{O_{2}}\) and \(Le_{H_{2}O})\) are close to unity. Thus, the variations of the optimum values of K_{1} in Table 3 suggests that it is likely to be dependent on Karlovitz and Lewis numbers. Based on this, the optimum value of K_{1} has been parameterised as:
According to this parameterisation, K_{1} reaches an asymptotic for large values of Ka_{L}. The predictions of the DBML model with K_{1} according to Eq. 12 (i.e. DBMLM model) are shown in Fig. 7, which shows that Eq. 12 sigfnificantly improves the model performance and the DBMLM model satisfactorily captures both qualitative and quantitative behaviours of (T_{6} + T_{7}). It is worth noting that Eq. 12 provides a possible parameterisation of K_{1} and an alternative parameterisation exhibiting similar behaviour is also possible.
Modelling of the reaction rate term T _{8}
The reaction rate contribution to the turbulent scalar flux transport was modelled by Bray et al. [9] for strict flamelet combustion (i.e. Da > 1 and Ka < 1) in the following manner (i.e. RB model):
where C_{R} = 1.0 and ϕ_{m} = 0.5 are the model constants. The variations of normalised T_{8} with \(\tilde {c}_{T}\) for cases AC are shown in Fig. 8 for α = H_{2},O_{2} and H_{2}O along with the predictions of the RB model. Figure 8 shows that in cases A and B, the term T_{8} assumes negative values towards the unburned gas side and positive values on the burned gas side of the flame brush for α = H_{2} and O_{2}, whereas just the opposite behaviour is observed for α = H_{2}O. By contrast, in case C, T_{8} assumes positive (negative) values towards the unburned gas side and negative (positive) values on the burned gas side of the flame brush for α = H_{2} and O_{2} (α = H_{2}O). This implies that the correlation between the fluctuations of velocity and reaction rate is fundamentally different in case C than in cases A and B. In the case of countergradient transport, a positive fluctuation of velocity induced by an increase in reaction rate magnitude tends to produce negative (positive) values of T_{8} for reactant (product) species such as H_{2} and O_{2} (H_{2}O) and this is predominantly responsible for the observed behaviours of T_{8} towards the unburned gas side and middle of the flame brush in cases A and B. However, the reaction rate magnitude tends to decrease towards the burned gas side whereas temperature continues to rise which acts to increase the positive fluctuations of velocity due to thermal expansion in the case of predominant countergradient transport. This leads to negative (positive) values of T_{8} for reactant (product) species such as H_{2} and O_{2} (H_{2}O) and this is predominantly responsible for the observed behaviours of T_{8} towards the burned gas side of the flame brush in cases A and B. Due to a predominantly gradient transport in case C, an increase in reaction rate magnitude tends to induce negative fluctuations of velocity, which leads to positive (negative) values of T_{8} for reactant (product) species such as H_{2} and O_{2} (H_{2}O) towards the unburned gas side and middle of the flame brush. In case C, the reaction rate magnitude tends to decrease towards the burned gas side whereas the rising temperature acts to decrease velocity due to augmented viscous damping, which is reflected in the negative (positive) values on the burned gas side of the flame brush for α = H_{2} and O_{2} (α = H_{2}O).
It can be seen from Fig. 8 that the RB model captures the correct qualitative behaviour of T_{8} for α = H_{2},O_{2} and H_{2}O in cases A and B. Although the quantitative agreement of the RB model with DNS data is not perfect, it is reasonable for these cases and this level of agreement has been found to be consistent with previous findings based on simple chemistry DNS data [24, 27, 39]. The qualitative and quantitative agreement between the RB model and DNS data is comparatively less satisfactory in case C in comparison to cases A and B. It is worth noting that the RB model was originally proposed for Da > 1 and Ka < 1, and thus the performance of this model progressively worsens with increasing (decreasing) Ka (Da).
It has been found that the performance of the RB model is not significantly dependent on ϕ_{m} but on C_{R}. The optimum values of C_{R} for different cases for α = H_{2},O_{2} and H_{2}O are listed in Table 4, which shows that C_{R} decreases from case A to case C and its value is different for different species. Moreover, optimum values of C_{R} for α = H_{2} are greater than the corresponding optimum values for α = O_{2} and H_{2}O. The optimum values of C_{R} for α = O_{2} and H_{2}O are similar in magnitude. Thus, it can be inferred that the optimum values of C_{R} is dependent on Karlovitz and Lewis numbers, which can be parameterised as:
The predictions of the RB model with ϕ_{m} = 0.5 and C_{R} according to Eq. 14 (i.e. RBM model) are also shown in Fig. 8, which shows that Eq. 14 significantly improves the model performance and the RBM model satisfactorily captures both qualitative and quantitative behaviours of T_{8}. It is worth noting that Eq. 14 provides a possible parameterisation of C_{R} and an alternative parameterisation exhibiting similar behaviour is also possible.
Final Remarks on the Turbulent Scalar Flux Transport Equation
A summary of the models considered for this analysis is presented in Table 5. Based on the foregoing discussion, the optimal combinations of the closure models for the unclosed terms of the turbulent scalar flux transport equation for different combustion regimes for different species (e.g. Eqs. 5iii, 12 and 14 are dependent on Le_{α}) are summarised in Table 6 for the convenience of readers and future users of the turbulent scalar flux transport equation. Table 6 provides an idea about the appropriate models for major species in different combustion regimes. It can be appreciated from the information provided in Table 6 that there is a huge scope for improvement in the modelling of the turbulent scalar flux transport in the broken reaction zones regime and this aspect needs further investigation. Further, it becomes clear that for most unclosed terms there is not a single existing model that performs satisfactorily in all regimes of combustion and for all species (see Tables 24). The suggested empirical relations accounting for some of these effects for CC, DBML and RB models will require further investigation.
Conclusions
The statistical behaviours of the turbulent scalar flux and the terms of its transport equation for major species have been analysed in the context of RANS using a detailed chemistry DNS database of freelypropagating H_{2} −air flames with an equivalence ratio of 0.7 spanning the corrugated flamelets, thin reaction zones and broken reaction zones regimes of combustion. The turbulent scalar flux statistics and its transport have been analysed in detail for the major reactants and products (i.e. H_{2},O_{2} and H_{2}O). A countergradient transport has been observed for the cases considered here representing the corrugated flamelets and thin reaction zones regimes of combustion, whereas a gradient transport is observed for the case representing the broken reaction zones regime. Accordingly, the qualitative behaviours of the terms of the turbulent scalar flux transport equation remain similar for the flames representing the corrugated flamelets and thin reaction zones regimes but these behaviours are different to that observed for the broken reaction zones regime flame considered here. It has been found that the performances of the existing closures for turbulent transport, pressure gradient, molecular diffusion and reaction rate terms show some dependence on the choice of the major species. The models for the unclosed terms of the turbulent scalar flux transport equation which perform satisfactorily in the corrugated flamelets and thin reaction zones regimes of premixed combustion have been identified based on apriori DNS analysis. However, there is no existing modelling methodology which was originally developed for the broken reaction zones combustion and thus the existing closures for the unclosed terms of the turbulent scalar flux transport equation, which were originally proposed for the strict flamelet combustion, have been found to be mostly inadequate for the broken reaction zones regime. Detailed explanations have been provided for the observed performances of submodels for the unclosed terms of the turbulent scalar flux transport equation for different regimes of premixed turbulent combustion. The present analysis indicates that there is ample scope for improvement in the modelling of the unclosed terms in the turbulent scalar flux transport equation and this improvement is especially needed for the broken reaction zone regime combustion.
Notes
 1.
The turbulent scalar fluxes of sensible enthalpy and temperature (i.e. \(\overline {\rho u_{i}^{\prime \prime }h^{\prime \prime }}\) and \(\overline {\rho u_{i}^{\prime \prime }T^{\prime \prime }})\) exhibit similar qualitative behaviour as that of \(\overline {\rho u_{i}^{\prime \prime }Y_{H_{2}O}^{\prime \prime }}\) and thus are not explicitly discussed for the purpose of conciseness.
References
 1.
Launder, B.E.: Heat and mass transport by turbulence. Top. Appl. Phys. 12, 231–287 (1976)
 2.
Durbin, P.A., PetterssonReif, B.A.: Statistical Theory and Modelling for Turbulent Flows. Willey (2001)
 3.
Kim, J., Moin, P.: Transport of passive scalars in a turbulent channel flow. Turb. Shear Flows 6, 85–95 (1989)
 4.
Abe, K., Suga, K.: Towards the development of a Reynoldsaveraged of algebraic scalar flux model. Int. J. Heat Fluid Flow 22, 19–29 (2001)
 5.
Guo, Y., Xu, C., Cui, G., Zhang, Z.: Large eddy simulation of scalar turbulence using a new subgrid Eddy diffusivity model. Int. J. Heat Fluid Flow 28, 268–274 (2007)
 6.
Murman, S.M.: A scalar anisotropy model for turbulent eddy viscosity. Int. J. Heat Fluid Flow 42, 115–130 (2013)
 7.
Rossi, R., Philips, D.A., Iaccarino, G.: A numerical study of scalar dispersion downstream of a wallmounted using direct simulations and algebraic closures. Int. J. Heat Fluid Flow 31, 805–819 (2010)
 8.
van Hooff, T., Blocken, B., Glousseau, P., van Heijst, G.J.F.: Countergradient diffusion in a slotventilated enclosure assessed by LES and RANS. Comput. Fluids 96, 63–75 (2014)
 9.
Bray, K.N.C., Libby, P.A., Moss, J.B.: Unified modelling approach for premixed turbulent combustion – Part I: General formulation. Combust. Flame 61, 87–102 (1985)
 10.
Cheng, R.K., Shepherd, I.G.: Influence of burner geometry on premixed turbulent flame propagation. Combust. Flame 85, 7–26 (1991)
 11.
Rutland, C.J., Cant, R.S.: Turbulent transport in premixed flames. In: Proc. of 1994 Summer Program, Centre for Turbulence Research Stanford University/NASA Ames (1994)
 12.
Veynante, D., Trouvé, A., Bray, K.N.C., Mantel, T.: Gradient and countergradient turbulent scalar transport in turbulent premixed flames. J. Fluid Mech. 332, 263–293 (1997)
 13.
Veynante, D., Poinsot, T.: Effects of pressure gradient in turbulent premixed flames. J. Fluid Mech. 353, 83–114 (1997)
 14.
Boger, M.: SubGrid Scale Modeling for Large Eddy Simulation of Turbulent Premixed Combustion. PhD dissertation, E’ cole Centrale Paris (2000)
 15.
Swaminathan, N., Bilger, R.W., Cuenot, B.: Relationship between turbulent scalar flux and conditional dilatation in premixed flames with complex chemistry. Combust. Flame 126, 1764–1779 (2001)
 16.
Rymer, G.: Analysis and Modelling of the Mean Reaction Rate and Transport Terms in Turbulent Premixed Combustion. PhD dissertation, E’ cole Centrale Paris (2001)
 17.
Kalt, P.A.M., Chen, Y.C., Bilger, R.W.: Experimental investigation of turbulent scalar flux in premixed stagnationtype flames. Combust. Flame 129, 401–415 (2002)
 18.
Tullis, S.W., Cant, R.S.: Scalar transport modelling in large Eddy simulation of turbulent premixed flames. Proc. Combust. Inst. 29, 2097–2104 (2003)
 19.
Huai, Y., Sadiki, A., Pfadler, S., Loffler, M., Beyrau, F., Leipertz, A., Dinkelacker, F.: Experimental assessment of scalar flux models for large eddy simulations of reacting flows. Turb. Heat Mass Transfer 5, 263–266 (2006)
 20.
Nishiki, S., Hasegawa, T., Borghi, R., Himeno, R.: Modelling of turbulent scalar flux in turbulent premixed flames based on DNS database. Combust. Theory Modell. 10, 39–55 (2006)
 21.
Richard, S., Colin, O., Vermorel, O., Angelberger, C., Benkenida, A., Veynante, D.: Large eddy simulation of combustion in spark ignition engine. Proc. Combust. Inst. 31, 3059–3066 (2007)
 22.
Pfadler, S., Kerl, J., Beyrau, F., Leipertz, A., Sadiki, A., Scheuerlein, J., Dinkelacker, F.: Direct evaluation of the subgridscale scalar flux in turbulent premixed flames with conditioned dualplane stereo PIV. Proc. Combust. Inst. 32, 1723–1730 (2009)
 23.
Chakraborty, N., Cant, R.S.: Effects of Lewis number on scalar transport in turbulent premixed flames. Phys. Fluids 21, 035110.1 (2009)
 24.
Chakraborty, N., Cant, R.S.: Effects of Lewis number on turbulent scalar transport and its modelling in turbulent premixed flames. Combust. Flame 156, 1427–1444 (2009)
 25.
Chakraborty, N., Cant, R.S.: Physical insight and modelling for Lewis number effects on turbulent heat and mass transport in turbulent premixed flames. Num. Heat Trans. 55, 762–779 (2009)
 26.
Lecocq, G., Richard, S., Colin, O., Vervisch, L.: Gradient and countergradient modelling in premixed flames: Theoretical study and application to the LES of a Lean premixed turbulent swirlburner. Combust. Sci. Technol. 182, 465–479 (2010)
 27.
Chakraborty, N., Cant, R.S.: Effects of turbulent Reynolds number on the modelling of turbulent scalar flux in premixed flames. Numer. Heat Trans. A 67(12), 1187–1207 (2015)
 28.
Gao, Y., Chakraborty, N., Klein, M.: Assessment of subgrid scalar flux modelling in premixed flames for large Eddy simulations: Apriori direct numerical simulation. Eur. J. Mech. FluidsB 52, 97–108 (2015)
 29.
Gao, Y., Chakraborty, N., Klein, M.: Assessment of the performances of subgrid scalar flux models for premixed flames with different global Lewis numbers: A direct numerical simulation analysis. Int. J. Heat Fluid Flow 52, 28–39 (2015)
 30.
Klein, M., Chakraborty, N., Gao, Y.: Scale similarity based models and their application to subgrid scale scalar flux modelling in the context of turbulent premixed flames. Int. J. Heat Fluid Flow 57, 91–108 (2016)
 31.
Lindstedt, R.P., Vaos, E.M.: Modelling of premixed turbulent flames with second moment methods. Combust. Flame 116, 461–485 (1999)
 32.
Tian, L., Lindstedt, R.P.: The impact of dilatation, scrambling, and pressure transport in turbulent premixed flames. Combust. Theor. Modell. 21, 1114–1147 (2017)
 33.
Lindstedt, R.P.: Transported Probability Density Function Methods for Turbulent Premixed Flames with Second Moment Methods in Turbulent Premixed Flames, pp. 102–130. Cambridge University Press (2011)
 34.
Daly, B.J., Harlow, F.H.: Transport equations of turbulence. Phys. Fluids 13, 2634–2649 (1970)
 35.
Launder, B.L.: Secondmoment closure: presentand future. Int. J. Heat Fluid Flow 10, 282–300 (1989)
 36.
Craft, T., Graham, L., Launder, B.: Impinging jet studies for turbulence model assessment –II. An examination of the performance of four turbulence models. Int. J. Heat Mass Transfer 36, 2687–2697 (1993)
 37.
Durbin, P.A.: A Reynolds stress model for nearwall turbulence. J. Fluid Mech. 249, 465–493 (1993)
 38.
Peters, N.: Turbulent Combustion. 1st edn. Cambridge University Press, Cambridge (2000)
 39.
Lai, J., Alwazzan, D., Chakraborty, N.: Turbulent scalar flux transport in headon quenching of turbulent premixed flames: A direct numerical simulations approach to assess models for Reynolds averaged Navier Stokes simulations. J. Turbul. 18, 1033–1066 (2017)
 40.
Li, S.C., Kong, Y.H.: Diesel combustion modelling using les turbulence model with detailed chemistry. Combust. Theor. Model 12, 208–219 (2008)
 41.
Vermorel, O., Richard, S., Colin, O., Angelberger, C., Benkenida, A., Veynante, D.: Towards the understanding of cyclic variability in a spark ignited engine using multicycle LES. Combusti. Flame 156, 1525–1541 (2009)
 42.
Im, H.G., Chen, J.H.: Preferential diffusion effects on the burning rate of interacting turbulent premixed hydrogenair flames. Combust. Flame 126, 246–258 (2002)
 43.
Arias, P.G., Chaudhuri, S., Uranakara, H.A., Im, H.G.: Direct numerical simulations of statistically stationary turbulent premixed flame. Combust. Sci. Technol. 188, 1182–1198 (2016)
 44.
Yoo, C.S., Wang, Y., Trouve, A., Im, H.G.: Characteristic boundary conditions for direct simulations of turbulent counterflow flames. Combust. Theor. Modell. 9, 617–646 (2005)
 45.
Rogallo, R.S.: Numerical Experiments in Homogeneous Turbulence. NASA TM81315. NASA Ames Research Center, California (1981)
 46.
Passot, T., Pouquet, A.: Compressible Turbulence with a perfect gas law: A numerical. Approach. J. Fluid Mech. 181, 441–466 (1987)
 47.
Han, K.Y.: Roles of displacement speed on evolution of flame surface density for different turbulent intensities and Lewis numbers in turbulent premixed combustion. Combust. Flame 152, 194–205 (2008)
 48.
Reddy, H., Abraham, J.: Twodimensional direct numerical simulation evaluation of the flamesurface density model for flames developing from an ignition kernel in lean methane/air mixtures under engine conditions. Phys. Fluids 24, 105–108 (2012)
 49.
Dopazo, C., Cifuentes, L., Martin, J., Jimenez, C.: Strain rates normal to approaching isoscalar surfaces in a turbulent premixed flame. Combust. Flame 162, 1729–1736 (2015)
 50.
Wacks, D.H., Chakraborty, N., Klein, M., Arias, P.G., Im, H.G.: Flow topologies in different regimes of premixed turbulent combustion: A direct numerical simulation analysis. Phys. Rev. Fluids 1, 083401 (2016)
 51.
Salehi, M.M., Bushe, W.K., Shahabazian, N., Groth, C.P.T.: Modified laminar flamelet presumed probability density function for LES of premixed turbulent combustion. Proc. Combust. Inst. 34, 1203–1211 (2013)
 52.
Mura, A., Robin, V., Kha, K.Q.N., Champion, M.: A layered description of a premixed flame stabilized in stagnating turbulence. Combust. Sci. Technol. 188, 1592–1602 (2016)
 53.
Jones, W.P.: Turbulence modelling and numerical solution methods for variable density and combusting flows. In: Libby, P.A., Williams, F.A. (eds.) Turbulent Reacting Flows, pp 309–374. Academic Press, London (1994)
 54.
Bradley, D., Gaskell, P.H., Gu, X.J.: Application of a Reynolds stress, stretched flamelet, mathematical model to computations to turbulent burning velocities and comparison with experiments. Combust. Flame 96, 221–248 (1994)
 55.
Domingo, P., Bray, K.N.C.: Laminar flamelet expressions for pressure fluctuation terms in second moment models of premixed turbulent combustion. Combust. Flame 121, 555–574 (2000)
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The financial and computational support of EPSRC and ARCHER are gratefully acknowledged.
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This study was funded by EPSRC (EP/K025163/1).
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Papapostolou, V., Chakraborty, N., Klein, M. et al. Statistics of Scalar Flux Transport of Major Species in Different Premixed Turbulent Combustion Regimes for H_{2}air Flames. Flow Turbulence Combust 102, 931–955 (2019). https://doi.org/10.1007/s1049401899890
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Keywords
 Turbulent scalar flux
 Reynolds averaged Navier Stokes simulations
 Direct numerical simulations
 Premixed combustion
 Countergradient transport