Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm

This paper aims to optimize the intake characteristics of a side ported Wankel rotary engine by combining machine learning (ML) with genetic algorithm (GA). The computational samples are generated using Sobol sequences, in which the variables are the timing of port full opening, port start closing, and port full closing (PFC). A two-layer structured ML prediction model is establishedwith the intake phases and geometric parameters as input variables. The results show that the coefficients of determination of the prediction models built by Gaussian process regression are greater than 0.99. The response surface presents that the PFC timing determines the intake loss and volumetric efficiency compared to others. The volume efficiency and intake loss are fitted as a quadratic function in the Pareto front. In all the typical cases, the deviation between prediction and calculation is less than 1%. In the typical case C, the intake loss is reduced by 19.39%, and the volumetric efficiency is only reduced by 0.01%. It is promising to integrate ML with GA for further improvements of engine performance.