Application of physics-informed neural network in the analysis of hydrodynamic lubrication
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. Thu...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-09-01
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| Series: | Friction |
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| Online Access: | https://doi.org/10.1007/s40544-022-0658-x |
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| _version_ | 1850230301027991552 |
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| author | Yang Zhao Liang Guo Patrick Pat Lam Wong |
| author_facet | Yang Zhao Liang Guo Patrick Pat Lam Wong |
| author_sort | Yang Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-213403a7522946418b5219adb9e3644e |
| institution | OA Journals |
| issn | 2223-7690 2223-7704 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Friction |
| spelling | doaj-art-213403a7522946418b5219adb9e3644e2025-08-20T02:03:55ZengTsinghua University PressFriction2223-76902223-77042022-09-011171253126410.1007/s40544-022-0658-xApplication of physics-informed neural network in the analysis of hydrodynamic lubricationYang Zhao0Liang Guo1Patrick Pat Lam Wong2School of Automotive and Transportation Engineering, Shenzhen PolytechnicSchool of Mechatronic Engineering and Automation, Shanghai UniversityDepartment of Mechanical Engineering, City University of Hong KongAbstract 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.https://doi.org/10.1007/s40544-022-0658-xphysics-informed neural networkhydrodynamic lubricationslider bearing |
| spellingShingle | Yang Zhao Liang Guo Patrick Pat Lam Wong Application of physics-informed neural network in the analysis of hydrodynamic lubrication Friction physics-informed neural network hydrodynamic lubrication slider bearing |
| title | Application of physics-informed neural network in the analysis of hydrodynamic lubrication |
| title_full | Application of physics-informed neural network in the analysis of hydrodynamic lubrication |
| title_fullStr | Application of physics-informed neural network in the analysis of hydrodynamic lubrication |
| title_full_unstemmed | Application of physics-informed neural network in the analysis of hydrodynamic lubrication |
| title_short | Application of physics-informed neural network in the analysis of hydrodynamic lubrication |
| title_sort | application of physics informed neural network in the analysis of hydrodynamic lubrication |
| topic | physics-informed neural network hydrodynamic lubrication slider bearing |
| url | https://doi.org/10.1007/s40544-022-0658-x |
| work_keys_str_mv | AT yangzhao applicationofphysicsinformedneuralnetworkintheanalysisofhydrodynamiclubrication AT liangguo applicationofphysicsinformedneuralnetworkintheanalysisofhydrodynamiclubrication AT patrickpatlamwong applicationofphysicsinformedneuralnetworkintheanalysisofhydrodynamiclubrication |