Diesel engine performance mapping using a parametrized mixing time model

Abstract

A numerical platform is presented for diesel engine performance mapping. The platform employs a zero-dimensional stochastic reactor model for the simulation of engine in-cylinder processes. n-Heptane is used as diesel surrogate for the modeling of fuel oxidation and emission formation. The overall simulation process is carried out in an automated manner using a genetic algorithm. The probability density function formulation of the stochastic reactor model enables an insight into the locality of turbulence–chemistry interactions that characterize the combustion process in diesel engines. The interactions are accounted for by the modeling of representative mixing time. The mixing time is parametrized with known engine operating parameters such as load, speed and fuel injection strategy. The detailed chemistry consideration and mixing time parametrization enable the extrapolation of engine performance parameters beyond the operating points used for model training. The results show that the model responds correctly to the changes of engine control parameters such as fuel injection timing and exhaust gas recirculation rate. It is demonstrated that the method developed can be applied to the prediction of engine load–speed maps for exhaust NO x , indicated mean effective pressure and fuel consumption. The maps can be derived from the limited experimental data available for model calibration. Significant speedup of the simulations process can be achieved using tabulated chemistry. Overall, the method presented can be considered as a bridge between the experimental works and the development of mean value engine models for engine control applications.