Modeling of Reactivity Controlled Compression Ignition Combustion using a Stochastic Reactor Model coupled with Tabulated Chemistry

Advanced combustion concepts such as reactivity controlled compression ignition have been proven to be capable of fundamentally improve conventional Diesel combustion by mitigating or avoiding the soot NOx trade-off while delivering comparable or better thermal efficiency. To further facilitate the development of the RCCI technology, a robust and possibly computationally efficient simulation framework is needed. While many successful studies have been published using 3D-CFD coupled with detailed combustion chemistry solvers, the level of maturity of 0D/1D based software solution offerings is relatively limited. The close interaction between physical and chemical processes challenges the development of predictive numerical tools, particularly when spatial information are not available. The present work discusses a novel stochastic reactor model based modeling framework capable of predicting combustion and emission formation in heavy-duty engines running in RCCI combustion mode. The combination of physical turbulence models, detailed emission formation sub-models and an established chemical kinetic tabulation process enables the model to be computationally in-expensive, compared to 3D-CFD. A detailed chemical kinetic scheme with 690 species and 8282 reactions was used to pre-tabulate combustion and emissions of a PRF mixture and n-heptane, used to model the gasoline and diesel fuels. The model is trained and validated based on 25 operating conditions of a single-cylinder research engine featuring different loads, speeds, EGR and gasoline fuel fractions. The model was found to be capable of reproducing the combustion phasing as well as the emissions trends measured on the test bench. The proposed modeling approach represent a promising basis towards establishing a comprehensive modeling framework capable of simulating transient operation as well as fuel property sweeps with acceptable accuracy.