A solution for finite journal bearings by using physics-informed neural networks with both soft and hard constrains
Industrial Lubrication and Tribology
ISSN: 0036-8792
Article publication date: 12 May 2023
Issue publication date: 27 June 2023
Abstract
Purpose
The purpose of this study is to solve the Reynolds equation for finite journal bearings by using the physics-informed neural networks (PINNs) method. As a meshless method, it is unnecessary to use big data to train the neural networks, but to satisfy the Reynolds equation and the corresponding boundary conditions by using the known physics information.
Design/methodology/approach
Here, the boundary conditions are enforced through the loss function firstly, i.e. the soft constrain method. After this, an equation was constructed to build a surrogate model for satisfying the corresponding boundary conditions naturally, i.e. the hard constrain method.
Findings
For the soft one, in brief, the pressure results agree well with existing results, apart from the ones on the boundaries. While for the hard one, it can be noted that the discrepancies on the boundaries are reduced significantly.
Originality/value
The PINNs method is used to solve the Reynolds equation for finite journal bearings, and the error values on the boundaries for the results of the soft constrain method are improved by using the hard constrain method. Therefore, the hard constraint maybe also a good option when the pressure results on the boundaries are emphasized.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0045/
Keywords
Acknowledgements
The authors would like to thank the funding from the Anhui University of Science and Technology (No. 2022yjrc15), from the Key research and development projects of Anhui Province (No. 2022a05020043) and from the National Natural Science Foundation of China (No. 51805410, 51804007).
Citation
Xi, Y., Deng, J. and Li, Y. (2023), "A solution for finite journal bearings by using physics-informed neural networks with both soft and hard constrains", Industrial Lubrication and Tribology, Vol. 75 No. 5, pp. 560-567. https://doi.org/10.1108/ILT-02-2023-0045
Publisher
:Emerald Publishing Limited
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