Technical Report 31, c4e-Preprint Series, Cambridge

Authors: Haiyun Su, Alexander Vikhansky, Sebastian Mosbach, Amit Bhave, Markus Kraft*, and Fabian Mauss

Reference: Technical Report 31, c4e-Preprint Series, Cambridge, 2005

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We present a new probability density function (PDF) based computational model to simulate a direct injection (DI) homogeneous charge compression ignition (HCCI) engine. This stochastic reactor model (SRM) accounts for the engine breathing process in addition to the closed-volume HCCI engine operation. A weighted-particle Monte Carlo method is used to solve the resulting PDF transport equation. While simulating the gas exchange, it is necessary to add a large number of stochastic particles to the ensemble due to the intake air and EGR streams as well as fuel injection, resulting in an increased computa- tional expense. Therefore, in this work we apply a down-sampling technique in order to reduce the number of stochastic particles, while conserving the statistical properties of the ensemble. In this method some of the most im- portant statistical moments (e.g., concentration of the main chemical species and enthalpy) are conserved exactly, while other moments are conserved in a statistical sense. Detailed analysis demonstrates that the statistical error associated with the down-sampling algorithm is more sensitive to the number of particles than to the number of conserved species for the given operating conditions. For a full-cycle simulation this down-sampling procedure was observed to reduce the computational time by a factor of eight as compared to the simulation without this strategy, whilst still maintaining the error within an acceptable limit. Furthermore, the new model is applied to qualitatively estimate the influence of engine parameters such as the relative air-fuel ratio and injection timing on HCCI combustion and emissions.

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