A new method to solve the Reynolds equation including mass-conserving cavitation by physics informed neural networks(PINNs)with both soft and hard constraints  

作　　者：Yinhu XI Jinhui DENG Yiling LI 

机构地区：[1]School of Mechanical Engineering,Anhui University of Science and Technology,Huainan 232001,China [2]Ericsson AB,Datalinjen 3,Linköping 58330,Sweden

出　　处：《Friction》2024年第6期1165-1175,共11页摩擦（英文版）

基　　金：the funding from Anhui University of Science and Technology(No.2022yjrc15);the Key Project of National Natural Science Foundation of China(Nos.U21A20125 and U21A20122);the Key Research and Development Projects of Anhui Province(No.2022a05020043);the National Natural Science Foundation of China(Nos.51805410 and 51804007).

摘　　要：In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries.

关 键 词：Reynolds equation mass-conserving cavitation physics informed neural networks hard constraints trivial solution 

分 类 号：TB332[一般工业技术—材料科学与工程]