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Physics-informed neural network approach to randomly rough surface contact mechanics

文献类型：期刊论文

作者Zhou, Yunong3; Song HX(宋恒旭)1,2
刊名TRIBOLOGY LETTERS
出版日期2025-09-01
卷号73期号:3页码:9
关键词Contact mechanics Rough surface PINN Stress distribution Persson theory
ISSN号1023-8883
DOI10.1007/s11249-025-02022-y
通讯作者Song, Hengxu(songhengxu@imech.ac.cn)
英文摘要In this study, we employed the Green's function molecular dynamics (GFMD) to simulate the non-adhesive contact between an elastic half-space and a rough counter face in (1+1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1+1)$$\end{document} dimensions, obtaining the contact stress distribution under varying length scales and Hurst exponents. Subsequently, based on the dataset generated by GFMD and adopting the diffusion equation form from Persson's theory, we obtained the stress distribution as well as the relative contact area using Physics-informed neural network (PINN). The results demonstrate that in full contact case, the diffusion equation coefficient aligns almost perfectly with Persson's theoretical prediction. In cases of partial contact, assuming the diffusion coefficient follows a power-law function of the length scale, the stress distribution predicted by PINN exhibits an error of less than 0.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.5\%$$\end{document} compared to GFMD. Furthermore, we verified that PINN can predict contact stress distribution and relative contact area at larger scales based on small-scale data, with predictions closely matching GFMD results.
分类号二类
WOS关键词RUBBER-FRICTION
资助项目National Natural Science Foundation of China[12402116] ; National Natural Science Foundation of China[BK20220555] ; Natural Science Foundation of Jiangsu Province[XDB0620101] ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向Engineering
语种英语
WOS记录号WOS:001513709000001
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Strategic Priority Research Program of the Chinese Academy of Sciences
其他责任者宋恒旭
源URL[http://dspace.imech.ac.cn/handle/311007/101862]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, LNM, Inst Mech, Beijing 100190, Peoples R China;
3.Yangzhou Univ, Dept Civil Engn, Yangzhou 225127, Jiangsu, Peoples R China;
推荐引用方式
GB/T 7714		 Zhou, Yunong,Song HX. Physics-informed neural network approach to randomly rough surface contact mechanics[J]. TRIBOLOGY LETTERS,2025,73(3):9.
APA Zhou, Yunong,&宋恒旭.(2025).Physics-informed neural network approach to randomly rough surface contact mechanics.TRIBOLOGY LETTERS,73(3),9.
MLA Zhou, Yunong,et al."Physics-informed neural network approach to randomly rough surface contact mechanics".TRIBOLOGY LETTERS 73.3(2025):9.

入库方式： OAI收割

来源：力学研究所

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