Implementation of a novel dual-layer machine learning structure for predicting the intake characteristics of a side-ported Wankel rotary engine

This paper aims to predict the intake characteristics of a side-ported Wankel rotary engine implemented by a novel dual-layer machine learning (ML) structure. The Sobol sequence coupled with a parametric phase control model is performed to generate samples using a three-dimensional dynamic simulation model. Two ML structures are compared to determine the input features, in which the input features of structure A are the port phases, while structure B also contains the geometric features. Compared with Structure A, the regression and generalization abilities of Structure B with different ML methods are generally favorable. Based on structure B, a novel dual-layer structure prediction model is developed using the Gaussian process regression method in which geometric features are used as intermediate variables. The results show that for the prediction of intake loss and volumetric efficiency, the coefficients of determination are greater than 0.99, and the prediction error is less than 1%. In addition, the port full closing timing determines the intake characteristics compared with other port phases. This paper provides novel solutions for predicting intake characteristics to guide practical optimization.