On the Intelligent Optimization of the Crevice Structure in a Rapid Compression Machine

In this study, the multi-objective intelligent optimization of the crevice structure in a rapid compression machine (RCM) is carried out based on the RCM simulation model modified with the KIVA-3V program. A multi-objective optimization simulation model of the crevice structure based on the large eddy simulation model coupled with the genetic algorithm NSGA-III is established. Six optimization parameters and seven optimization objectives are selected in the optimization process. The results show that the genetic algorithm can quickly find the values of the optimized parameters. The crevice volume ratio shows a trade-off relationship with the dimensionless temperature ratio Tmax/Taver and the tumble ratio. A larger crevice volume can reduce the flow of boundary layer cryogenic gas into the combustion chamber, thus improving the temperature uniformity. In addition, the crevice entrance width and the connecting channel length should be smaller, while the volume of the crevice main chamber should be larger, so as to sufficiently introduce the low-temperature gas of the boundary layer into the crevice and reduce their influence on the temperature field of the combustion chamber. When the crevice volume accounts for 10