北京航空航天大学学报2026，Vol.52Issue(8)：2720-2728,9.DOI:10.13700/j.bh.1001-5965.2026.0011
基于PINN的液压泵油膜厚度求解方法
A PINN-based method for solving oil film thickness in hydraulic pumps
摘要
Abstract
The oil film thickness in hydraulic pumps significantly impacts their lubrication performance and operational reliability.Traditional numerical methods often face challenges such as low computational efficiency and strong dependence on boundary conditions when handling complex lubrication models.In order to study the oil film thickness and lubrication of the cylinder block/valve plate pair in axial piston pumps,this research suggests a physics-informed neural network(PINN)-based approach for modeling and solving hydraulic pump flow fields.This approach embeds the Reynolds equation,energy equation,and mixed lubrication conditions into the neural network's loss function,achieving a fusion of physical principles and data-driven solutions.Results demonstrate that under typical operating conditions,the PINN method effectively solves oil film thickness distributions without requiring extensive training data.With an average relative error of less than 10%,it shows high agreement with numerical approach findings while greatly increasing computing efficiency.关键词
物理信息神经网络/液压泵/转子-配流盘副/雷诺方程/油膜厚度Key words
physics-informed neural network/hydraulic pump/cylinder block/valve plate pair/Reynolds equation/oil film thickness分类
机械制造引用本文复制引用
马仲海,于福林,尹方龙,展召彬,史俊强..基于PINN的液压泵油膜厚度求解方法[J].北京航空航天大学学报,2026,52(8):2720-2728,9.基金项目
国家自然科学基金(52475044) （52475044）
国家重点实验室开发基金(KFJJ2024-01-01) National Natural Science Foundation of China(52475044) （KFJJ2024-01-01）
State Key Laboratory Development Fund(KFJJ2024-01-01) （KFJJ2024-01-01）
