Large eddy simulation of a lifted ethylene flame using a dynamic nonequilibrium model for subfilter scalar variance and dissipation rate

Abstract

Accurate prediction of nonpremixed turbulent combustion using large eddy simulation(LES) requires detailed modeling of the mixing between fuel and oxidizer at scales finer than the LES filter resolution. In conserved scalar combustion models, the small scale mixing process is quantified by two parameters, the subfilter scalar variance and the subfilter scalar dissipation rate. The most commonly used models for these quantities assume a local equilibrium exists between production and dissipation of variance. Such an assumption has limited validity in realistic, technically relevant flow configurations. However, nonequilibrium models for variance and dissipation rate typically contain a model coefficient whose optimal value is unknown a priori for a given simulation. Furthermore, conventional dynamic procedures are not useful for estimating the value of this coefficient. In this work, an alternative dynamic procedure based on the transport equation for subfilter scalar variance is presented, along with a robust conditional averaging approach for evaluation of themodel coefficient. This dynamic nonequilibrium modeling approach is used for simulation of a turbulent lifted ethylene flame, previously studied using DNS by Yoo et al. (Proc. Comb. Inst., 2011). The predictions of the new model are compared to those of a static nonequilibrium modeling approach using an assumed model coefficient, as well as those of the equilibrium modeling approach. The equilibrium models are found to systematically underpredict both subfilter scalar variance and dissipation rate. Use of the dynamic procedure is shown to increase the accuracy of the nonequilibrium modeling approach. However, numerical errors that arise as a consequence of grid-based implicit filtering appear to degrade the accuracy of all three modeling options. Thus, while these results confirm the usefulness of the new dynamic model, they also show that the quality of subfilter model predictions depends on several factors extrinsic to the formulation of the subfilter model itself.

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ORCID for E.S. Richardson: orcid.org/0000-0002-7631-0377

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