Identification, prediction and classification of hydrogen-fueled Wankel rotary engine knock by data-driven based on combustion parameters

Hao Meng, Qiang Zhan, Changwei Ji, Jinxin Yang and Shuofeng Wang

Energy, 2024, vol. 308, issue C

Abstract: Hydrogen-fueled Wankel rotary engine has excellent power density and low backfire possibility, however, as well as the severe knock. The knock is closely related to the in-cylinder combustion process, therefore, the present work is conducted to identify, predict and classify the knock by data-driven based on combustion parameters in the hydrogen-fueled Wankel rotary engine. The results show that: Knock type can be identified well according to knock intensity and crank angle of peak knock pressure through the Gaussian Mixture Model. There are 141 strong knock cycles and 809 weak knock cycles in test data. Compared with Support Vector Machines and Back-propagation Neural Networks, Multiple Linear Regression has a better global performance in knock-level prediction based on combustion parameters. The maximum pressure rising rate and CA50 have more significant impacts on knock, the partial of regression coefficients of which is about 0.42 and −0.58, respectively. In particular, due to different formation mechanisms, the prediction models of two types of knock are recommended to be established separately. In addition, the Support Vector Machine can be applied to conduct knock classification. Among kernel functions in Support Vector Machine, the linear kernel function can achieve optimal mean test accuracy, about 88.66 %.

Keywords: Hydrogen-fueled Wankel rotary engine; Knock; Gaussian Mixture Model; Multiple Linear Regression; Support vector machine; Back-propagation Neural Network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:308:y:2024:i:c:s0360544224028032

DOI: 10.1016/j.energy.2024.133029

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