Construction of digital twin model of engine in-cylinder combustion based on data-driven

Deng Hu, Hechun Wang, Chuanlei Yang, Binbin Wang, Baoyin Duan, Yinyan Wang and Hucai Li

Energy, 2024, vol. 293, issue C

Abstract: Optimizing the combustion process by predicting combustion parameters during prolonged engine operation is crucial for engine maintenance. This study presents a zero-dimensional (0-D) prediction model that integrates the advantages of model-driven and data-driven approaches. Initially, the snake optimization algorithm (SO) is employed to address the challenges related to low parameter fitting accuracy and multiple solutions in calculating Wiebe parameters. Subsequently, a convolutional neural network-bidirectional long short-term memory neural network (CNN–Bi-LSTM) is devised to establish a nonlinear correlation between operating parameters and Wiebe parameters. The structural parameters of CNN–Bi-LSTM are then optimized using the SO algorithm (SO–CNN–Bi-LSTM). Ultimately, a 0-D prediction combustion model is formulated by amalgamating the Wiebe function with the neural network, enabling real-time prediction of combustion results and generalization analysis of prediction performance under non-calibrated conditions. The findings demonstrate that the combustion model exhibits heightened accuracy, thereby establishing a robust technical foundation for the development of a digital twin in the engine combustion process.

Keywords: Digital twin; Diesel engine; Wiebe function; 0-D model; Deep learning; Data-driven (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003141

DOI: 10.1016/j.energy.2024.130543

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