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The incorporation of detailed chemistry models in internal combustion engine simulations is becoming mandatory as local, globally lean, low-temperature combustion strategies are setting the path towards a more efficient and environmentally sustainable use of energy resources in transportation. In this paper, we assessed the computational efficiency of a recently developed sparse analytical Jacobian chemistry solver, namely ‘SpeedCHEM’, that features both direct and Krylov-subspace solution methods for maximum efficiency for both small and large mechanism sizes. The code was coupled with a high-dimensional clustering algorithm for grouping homogeneous reactors into clusters with similar states and reactivities, to speed-up the chemical kinetics solution in multi-dimensional combustion simulations.

In recent years society's demand and interest in clean and efficient internal combustion engines has grown significantly. Several ideas have been proposed and tested to meet this demand. In particular, dual-fuel Reactivity Controlled Compression Ignition (RCCI) combustion has demonstrated high thermal efficiency, and low engine-out NOx, and soot emissions. Unlike homogeneous charge compression ignition (HCCI) combustion, which solely relies on the chemical kinetics of the fuel for ignition control, RCCI combustion has proven to provide superior combustion controllability while retaining the known benefits of low emissions and high thermal efficiency of HCCI combustion. However, in order for RCCI combustion to be adopted as a high efficiency and low engine-out emission solution, it is important to achieve high-power operation that is comparable to conventional diesel combustion (CDC).