Employing detailed chemistry into modern engine simulation technologies has potential to enhance the robustness and predictive power of such tools. Specifically this means significant advancements in the ability to compute the onset of ignition, low and high temperature heat release, local extinction, knocking, exhaust gas emissions formation etc. resulting in a set of tools which can be employed to carry out virtual engineering studies and add additional insight into common IC engine development activities such as computing IMEP, identifying safe/feasible operating ranges, minimizing exhaust gas emissions and optimizing operating strategy. However the adoption of detailed chemistry comes at a greater computational cost, this paper investigates the means to retain computational robustness and ease of use whist reducing computational timescales.
This paper focuses upon a PDF (Probability Density Function) based model based on the Stochastic Reactor Model (SRM), which has gained increasing attention from academics and industry for its capabilities to account for in-cylinder processes such as chemical kinetics, fuel injection, turbulent mixing, heat transfer etc. whilst retaining in-cylinder stratification of mixture composition (i.e. fuel equivalence ratio) and temperature. Among the techniques considered here are: a standard KIVA 3V simulation, down-sampling from 3D CFD composition-space to stochastic particles using sequential coupling of KIVA 3V and SRM, the use of detailed chemical kinetics within SRM, chemical mechanism reduction, down-sampling of a chemical mixture space within the SRM, and parallelization of chemistry solution within SRM. The experimental engine setup studied is that used by Cao