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Construction of digital twin model of engine in-cylinder combustion based on data-driven.
- Source :
-
Energy . Apr2024, Vol. 293, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
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. • The SO algorithm for calculating Wiebe parameters was applied. • A parameter identification model based on SO–CNN–Bi-LSTM was proposed. • A 0-D prediction model was constructed by combining Wiebe function with neural network. • Based on the prediction model, the combustion process was simplified and reconstructed. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03605442
- Volume :
- 293
- Database :
- Academic Search Index
- Journal :
- Energy
- Publication Type :
- Academic Journal
- Accession number :
- 175848242
- Full Text :
- https://doi.org/10.1016/j.energy.2024.130543