Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm

Abstract

Hydrogen is a promising way to achieve high efficiency and low emissions for Wankel rotary engines. In this paper, the intake and exhaust phases and excess air ratios (λ) were optimized using machine learning (ML) and genetic algorithm (GA). Firstly, a one-dimensional model was built and verified under various λ. Secondly, the variables were determined using sensitivity analysis method, and the sample for training models was generated using the Latin hypercube sampling. Finally, a prediction model for performance and emissions was built using ML and combined with GA for multi-objective optimization. The results show that the timing of intake port full closing (IPFC) and exhaust port start opening (EPSO) exhibits the most significant influence on performance and emissions, while the other phases are less influential. Both indicated mean effective pressure (IMEP) and indicated specific nitrogen oxides (ISNOx) increase as the IPFC timing is advanced, while indicated specific fuel consumption (ISFC) decreases as EPSO timing is delayed. Compared with the original engine, the optimized IMEP is improved by 0.18%, ISFC is reduced by 2.39%, and ISNOx is reduced by up to 65.43%. It is an efficient way to use ML combined with GA to improve performance and reduce emissions simultaneously.