Evaluation of Different Turbulent Combustion Models Based on Tabulated Chemistry Using DNS of Heterogeneous Mixtures Under Multi-injection Diesel Engine-Relevant Conditions

Abstract

This paper assesses the accuracy of partially-premixed turbulent combustion models based on the tabulation of chemical kinetics, under multi-injection diesel engine-relevant conditions. For this purpose, 2-D direct numerical simulation (DNS) is carried out. Pockets of gaseous n-heptane are randomly distributed in a turbulent field of a partially burnt n-heptane/air mixture. The burnt gases composition and enthalpy correspond to the partial oxidation of a pilot injection that precedes the main injection, represented here by the fresh fuel pockets. The DNS domain is enclosed in a larger volume, permitting quasi-constant pressure autoignition. Chemical kinetics is modeled by a 29-species skeletal reaction mechanism for n-heptane/air mixture autoignition and flame propagation. A homogeneous isotropic turbulence spectrum is used to initialize the velocity field in the domain. A DNS database is generated varying the progress of the pilot injection combustion $$c_0$$ and the velocity fluctuation level $$u'$$ of the turbulence spectrum. Three different modeling approaches are tested a priori against the DNS data: (1) the tabulated homogeneous reactor, which is a direct exploitation of the chemistry tabulation ignoring any local mixture heterogeneity; (2) the presumed conditional moment model, which includes a separate statistical description for the mixture and the combustion progress; (3) the approximated diffusion flame model, which considers the heterogeneous turbulent reactor as a diffusion flame. Since the same chemical kinetics mechanism is used for the generation of the chemistry tabulation, the study is entirely focused on the evaluation of the different modeling assumptions. Results show that accounting for initial progress variable of the mixture ( $$c_0$$ ) is mandatory for such models. They also indicate the omission of mixture fraction Z and progress variable c heterogeneities and the assumption of the statistical independence of Z and c as the main responsible for model discrepancies under the studied conditions.