Comparative study on single-objective optimization and multi-objective optimization of convex textured friction couple performance

Abstract To improve the friction property of the friction pair of the hydrostatic thrust bearing, the surface with convex textures is designed at the bottom of the oil cavity, and the effect of convex textures on the interstitial oil film performance is analyzed. The interstitial oil film in contact with convex textures is simulated and studied by the computational fluid dynamics method. The study shows that the oil cavity with the convex structures can significantly improve the friction characteristics of the gap oil film in the hydrostatic thrust bearing. In order to further study the effect of texture parameters (texture radius, texture spacing , texture quantity and texture area ratio ) on its performance, the method based on the full factor test is used for single objective optimization, and the methods based on the BBD response surface and the NSGA-II algorithm are used for multiple target collaborative optimization. The full factor test with the main effect shows that the best combination with the bearing capacity is 0.5 mm, 7 mm, 7 and 1.6%, and the best combination with the rigidity is 0.3 mm, 5 mm, 5 and 1.13%. The influence of textures on the maximum temperature of oil film is basically unchanged and the bearing capacity is in inverse proportion to the friction factor. When considering the interaction between texture parameters, the BBD response surface method is used for multi-objective optimization. The study shows that the texture radius is 0.7 mm, the texture spacing is 7 mm, the quantity of textures is 6.2, and the area ratio is 3.14%, the optimal response values can be obtained, which the bearing capacity is 8519.31 N and the rigidity is 1.31868 E + 12 N m−1. The multi-objective optimization analysis of the NSGA-II algorithm based on the BBD response surface model shows that the algorithm can obtain a Pareto solution set. Each set of solutions obtained by the NSGA-II algorithm is the optimal solution. Both the BBD response surface method and the NSGA-II algorithm can be used to solve multi-objective problems.