A PINN-based method for solving oil film thickness in hydraulic pumps

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.