Optimization of twin grooved two-lobe textured hydrodynamic journal bearing design by using genetic algorithm

Abstract

Surface texture plays a role in enhancing the performance of hydrodynamic journal bearings by reducing friction coefficients and increasing load-carrying capacity. However, its impact on the dynamic performance of the bearings remains largely unexplored. This study aims to fill this gap by investigating the optimized surface texture of twin-grooved two-lobe hydrodynamic journal bearings using genetic algorithms. Through the optimization of surface texture, significant enhancements in the dynamic performance of the bearings, including improvements in fluid film damping, stiffness, and omega threshold speed, are achieved. Utilizing GA optimization, textured bearings demonstrate a remarkable enhancement in dynamic performance, with an impressive increase of 195.55% in omega threshold speed. These findings provide valuable insights for enhancing bearing designs and stability, thereby contributing to advancements in tribological engineering.