Friction 2023, 11(7): 1253-1264 https://doi.org/10.1007/s40544-022-0658-x

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Open Access | Issue | Published: 02 September 2022

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

Show Author's Information Hide Author's Information Yang ZHAO^¹(

), Liang GUO^², Patrick Pat Lam WONG^³

1 School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China

2 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

3 Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China

Keywords:

physics-informed neural network, hydrodynamic lubrication, slider bearing

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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

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.

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

Show Author's information Hide Author's Information Yang ZHAO^¹(

), Liang GUO^², Patrick Pat Lam WONG^³

1 School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China

2 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

3 Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China

Abstract

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

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Acknowledgements

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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

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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