抄録
An accurate in-cylinder combustion model for SI-engines has been constructed by combining a stochastic reactor model with predictive turbulent flame propagation. The computational time for full cycle calculations is kept low through load balanced parallelization and particle clustering. This paper presents a two-zone zero dimensional transported probability density function model, utilizing a stochastic reactor for solution of the ordinary differential equations in each zone. A turbulent flame propagation model is applied, and laminar flame speed is achieved from a flame speed library built with detailed chemistry. The flame front is tracked with a CPU efficient polygon method. Knock and emissions are calculated using on-line detailed chemistry. The model has previously shown very good predictability of combustion as well as knock. In this work, the model calculation time has been decreased through load balanced distribution of the on-line chemistry calculations on a large number of CPU's. In addition, an algorithm for chemical state based particle clustering in each zone has been developed. Particles are clumped into groups based on user provided dispersion thresholds for any number of cluster tracking parameters. As a consequence, not all particles need to be treated during the chemistry step; only one cluster representative particle needs to be passed to the chemistry solver. The paper presents the clustering algorithm, and its verification. It is shown that a very limited number of clustering parameters can be used, and that the dispersion thresholds can be set high. On average, the clustering yields a factor 3-4 in speedup of chemistry calculations and CPU times of about a minute per cycle are achieved. Since chemistry and in-homogeneities are taken into account this yields a model suitable for e.g. fuel analysis and performance studies.
