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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.

In this work are presented experimental and simulated data from a one-cylinder direct injected Diesel engine fuelled with Diesel, two different biodiesel blends and pure biodiesel at one engine operating point. The modeling approach focuses on testing and rating biodiesel surrogate fuel blends by means of combustion and emission behavior. Detailed kinetic mechanisms are adopted to evaluate the fuel-blends performances under both reactor and diesel engine conditions. In the first part of the paper, the experimental engine setup is presented. Thereafter the choice of the surrogate fuel blends, consisting of n-decane, α-methyl-naphtalene and methyl-decanoate, are verified by the help of experiments from the literature. The direct injection stochastic reactor model (DI-SRM) is employed to simulate combustion and engine exhaust emissions (NOx, HC, CO and CO2), which are compared to the experimental data.

An investigation on reducing the set of modeling parameters for engine cycle simulation is presented. The investigation considers a detailed kinetic model for combustion and emissions predictions coupled to a complete cycle simulation tool applied to a modern Diesel engine. The analysis is based on a previously developed method that combines a 1-D gas dynamics model with a stochastic reactor model for direct injection engines (SRM-DI). Initially, the global and instantaneous performance parameters of a Diesel engine were simulated at different operating conditions. The model was validated and the simulated results were compared to experimental data to assess the quality of the model. Afterwards, the influence of the chosen modeling parameters on engine performance, such as in-cylinder pressure, emissions and global performances, were analyzed. The mixing time proved to be the most important modeling parameter for the stochastic reactor model.