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This paper reports on a fast predictive combustion tool employing detailed chemistry. The model is a stochastic reactor based, discretised probability density function model, without spatial resolution. Employing detailed chemistry has the potential of predicting emissions, but generally results in very high CPU costs. Here it is shown that CPU times of a couple of minutes per cycle can be reached when applying detailed chemistry, and CPU times below 10 seconds per cycle can be reached when using reduced chemistry while still catching in-cylinder in-homogeneities. This makes the tool usable for efficient engine performance mapping and optimisation. To meet CPU time requirements, automatically load balancing parallelisation was included in the model. This allowed for an almost linear CPU speed-up with number of cores available.

The subject of this work is 3D numerical simulations of combustion and soot emissions for a passenger car diesel engine. The CFD code STAR-CD version 3.26 [1] is used to resolve the flowfield. Soot is modeled using a detailed kinetic soot model described by Mauss [2]. The model includes a detailed description of the formation of polyaromatic hydrocarbons. The coupling between the turbulent flowfield and the soot model is achieved through a flamelet library approach, with transport of the moments of the soot particle size distribution function as outlined by Wenzel et al. [3]. In this work we extended this approach by considering acetylene feedback between the soot model and the combustion model. The model was further improved by using new gas-phase kinetics and new fitting procedures for the flamelet soot library.