An optimization research on groove textures of a journal bearing using particle swarm optimization algorithm

This study aims to optimize the distributions of groove textures in a journal bearing to reduce its friction coefficient. Firstly, A lubrication model of a groove textured journal bearing is established, and the finite difference and overrelaxation iterative methods are used to numerically solve the model. Then, the friction coefficient is adopted as the fitness function and the groove lengths are optimized by particle swarm optimization (PSO) algorithm to evolve the optimal distributions. Furthermore, the effects of eccentricity ratios and rotary speeds on optimal distributions of groove textures are also discussed. The numerical results show the optimal distributions of groove textures are like trapeziums under different eccentricity ratios and rotary speeds, and the trapeziums become slenderer with increasing of eccentricity ratios. It is also found that the reductions of friction coefficients by optimal groove textures are more significant under lower eccentricity ratios. Briefly, this study may provide guidance on surface texture design to improve the tribological performance of journal bearings.