Topological optimization of dimple distribution for enhanced performance in hydrodynamic porous self-lubricating journal bearings with sealed ends

Abstract

This study numerically investigates the impact of optimal textures location on the performance of hydrodynamic porous self-lubricating journal bearings with sealed ends, subjected to a stationary load. The analysis employs a modified Reynolds equation coupled with Darcy’s law to model fluid flow in both the lubricating film and the porous matrix, considering the hydrodynamic self-lubrication problem. The governing nonlinear PDE systems were solved numerically using the finite difference method, combined with Reynolds boundary conditions and continuity conditions for velocity and pressure at the film-bush interface. A Binary Genetic Algorithm (BGA) is employed to optimize the topological distribution of square dimples in the textured porous layer to enhance bearing performance. The study investigates the influence of key parameters, including applied load, rotational speed, permeability, and texture depth, on bearing characteristics such as minimum film thickness and friction coefficient. Results show good agreement with benchmark data and indicate a positive enhancement in porous bearing performance. In addition, findings demonstrate that increasing the permeability of the porous structure reduces bearing performance (up to 25% in minimum film thickness and 8% in friction coefficient). However, the application of the optimization technique identified an optimal arrangement of textures that compensates for these performance losses, even under severe working conditions. Texturing the outlet region of the contact (beyond 180°) at the cavitation zone causes a micro-step bearing mechanism, generating localized pressure recovery within the textured area, significantly enhancing the minimum film thickness (up to 12%), reducing friction (up to 23%), and minimizing cavitation (up to 24%).