Investigation and Optimization of Textured Water-lubricated Journal Bearings Using Multi-objective Optimization

Abstract

Water lubricated bearings can be used to reduce contamination due to lubricant leakage in heavy machinery such as power positioning systems of offshore platforms and ship propulsion systems. The lubrication model of a textured two-dimensional parallel friction pair and a textured water-lubricated journal bearing are developed to investigate the lubrication performance. The governing equation is solved, and the fluid cavitation is analyzed using the Zwart-Gerber-Belamri (ZGB) model. A multi-objective optimization method combining the response surface and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the textured journal bearings. The results indicate that a small texture width will inhibit the occurrence of liquid film cavitation. With the rise in the texture width, the cavitation effect gradually rises and stabilizes. As the texture depth deepens, the micro dynamic pressure effect is enhanced and liquid film pressure rises. Through the tests, it is found that the optimized texture parameters can be implemented to effectively diminish the friction and wear volume, also the optimized textured bearing hydrodynamic pressure effect is enhanced at the same speed.