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Friction 2023, 11(7): 1253-1264 https://doi.org/10.1007/s40544-022-0658-x
Research Article | Open Access | Issue | Published: 02 September 2022

Application of physics-informed neural network in the analysis of hydrodynamic lubrication

Show Author's Information Yang ZHAO1( ),  Liang GUO2,  Patrick Pat Lam WONG3
School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
Keywords:
hydrodynamic lubrication, physics-informed neural network, slider bearing
Cite this article:
ZHAO Y, GUO L, WONG PPL. Application of physics-informed neural network in the analysis of hydrodynamic lubrication. Friction, 2023, 11(7): 1253-1264. https://doi.org/10.1007/s40544-022-0658-x
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Abstract Full text About this article

The last decade has witnessed a surge of interest in artificial neural network in many different areas of scientific research. Despite the rapid expansion in the application of neural networks, few efforts have been carried out to introduce such a powerful tool into lubrication studies. Thus, this work aims to apply the physics-informed neural network (PINN) to the hydrodynamic lubrication analysis. The 2D Reynolds equation is solved. The PINN is a meshless method and does not require big data for network training compared with classical methods. Our results are consistent with those obtained by experiments and the finite element method. Hence, we envision that the PINN method will have great application potential in lubrication and bearing research.


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About this article

Application of physics-informed neural network in the analysis of hydrodynamic lubrication

Show Author's information Yang ZHAO1( ), Liang GUO2, Patrick Pat Lam WONG3
School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China

Abstract

The last decade has witnessed a surge of interest in artificial neural network in many different areas of scientific research. Despite the rapid expansion in the application of neural networks, few efforts have been carried out to introduce such a powerful tool into lubrication studies. Thus, this work aims to apply the physics-informed neural network (PINN) to the hydrodynamic lubrication analysis. The 2D Reynolds equation is solved. The PINN is a meshless method and does not require big data for network training compared with classical methods. Our results are consistent with those obtained by experiments and the finite element method. Hence, we envision that the PINN method will have great application potential in lubrication and bearing research.

Keywords: hydrodynamic lubrication, physics-informed neural network, slider bearing

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Publication history
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Publication history

Received: 20 December 2021
Revised: 28 January 2022
Accepted: 23 May 2022
Published: 02 September 2022
Issue date: July 2023

Copyright

© The author(s) 2022.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51805310), the Scientific Research Startup Fund for Shenzhen High-caliber Personnel of SZPT (No. 6022310045k). Special thanks go to Prof. A. Almqvist for his valuable comments on the work.

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