Optimization Of Injection Parameters For Diesel PCCI Combustion

In the premixed charge compression ignition (PCCI) combustion strategy, injecting parameters were the key factors to improve premixed proportion and combustion efficiency. Genetic Algorithm was used to optimize the injection parameters including start of injection (SOI), injection pressure and spray angle, to decrease NOx and Soot emissions.In this paper, KIVA-3V was used as a tool to stimulate diesel PCCI combustion. A non-dominated sorting genetic algorithm (NSGA2) was paralleled based on the Master-Slave parallel algorithm. Master-processor took charge of the distribution of fitness calculation which was done in the slave-processor, after the calculation the result was returned and other genetic operations were done in the master-processor.Based on the Master-Slave parallel algorithm, NSGA2 was coupled with computational fluid dynamics (CFD) code KIVA-3V into the code KIVA3V-NSGA2-MS to conduct the multi-objective optimization with goals of low emissions and improved fuel economy. The effect of each parameter on combustion and emissions was investigated based on the temperature and equivalence ratio distributions, as well as spray targeting in the cylinder.The study reveals that the use of the KIVA3V-NSGA2-MS enables an efficient search of global optimal solutions with conflicting objectives for the combustion optimization. Premixed combustion dominates PCCI combustion process when SOI advances before 27°CA BTDC under the condition of 79 degrees spray included angle and 149.7 MPa injection pressure. In the PCCI strategy, injection pressure does not have an obvious effect on combustion and emissions. SOI should be associated with spray targeting to explain the emission trends.