Large-Eddy Simulation of Turbulent Dispersion Effects in Direct Injection Diesel and Gasoline Sprays

In most large-eddy simulation (LES) applications to two-phase engine flows, the liquid-air interactions need to be accounted for as source terms in the respective governing equations. Accurate calculation of these source terms requires the relative velocity “seen” by liquid droplets as they move across the flow, which generally needs to be estimated using a turbulent dispersion model. Turbulent dispersion modeling in LES is very scarce in the literature. In most studies on engine spray flows, sub-grid scale (SGS) models for the turbulent dispersion still follow the same stochastic approach originally proposed for Reynolds-averaged Navier-Stokes (RANS). In this study, an SGS dispersion model is formulated in which the instantaneous gas velocity is decomposed into a deterministic part and a stochastic part. The deterministic part is reconstructed using the approximate deconvolution method (ADM), in which the large-scale flow can be readily calculated. The stochastic part, which represents the impact of the SGS flow field, is assumed to be locally homogeneous and isotropic and, therefore, governed by a Langevin-type equation. The model is applied to the spray G and spray H conditions defined by the engine combustion network (ECN) group. Simulation results are compared with the available experimental data for spray characteristics such as penetration rates, mixture fraction profile, and droplet velocity and Sauter mean diameter (SMD) distributions. Simulations with no dispersion and the commonly used RANS-type stochastic model are also performed for comparison purposes. Results show that the turbulent dispersion has a considerable impact on quantitative spray characteristics such as projected liquid volume (PLV) fraction, droplet SMD and velocity, and fuel vapor mixture fractions. On the other hand, the macroscopic spray characteristics such as liquid- and vapor-phase penetrations are not significantly affected by the dispersion modeling. The proposed SGS model also improves the prediction of spray and ignition characteristics at the spray conditions studied in this work.