Modeling of Reactivity Controlled Compression Ignition Combustion Using a Stochastic Reactor Model Coupled with Detailed Chemistry 2021-24-0014
Advanced combustion concepts such as reactivity controlled compression ignition (RCCI) have been proven to be capable of fundamentally improve the 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 maturity level of the 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 is not available. The present work discusses a novel stochastic reactor model (SRM) based modeling framework capable of predicting the combustion process and the emission formation in a heavy-duty engine running under RCCI combustion mode. The combination of physical turbulence models, detailed emission formation sub-models and state-of-the-art chemical kinetic mechanisms enables the model to be computationally inexpensive compared to the 3D-CFD approaches. A chemical kinetic mechanism composed of 248 species and 1428 reactions was used to describe the oxidation of gasoline and diesel using a primary reference fuel (PRF) mixture and n-heptane, respectively. The model is compared to operating conditions from 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 emission trends measured on the test bench, at some extent. The proposed modeling approach represents a promising basis towards establishing a comprehensive modeling framework capable of simulating transient operation as well as fuel property sweeps with acceptable accuracy.
Tim Franken, Andrea Matrisciano, Rafael Sari, Álvaro Fogué Robles, Javier Monsalve-Serrano, Dario Lopez Pintor, Michal Pasternak, Antonio Garcia, Fabian Mauss
Brandenburg University of Technology, LOGE AB - Chalmers University, Universitat Politecnica de Valencia, Sandia National Laboratories, LOGE Polska Sp. z o.o., Brandenburg Univ of Technology
15th International Conference on Engines & Vehicles