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This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge.

Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance.

The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2ºC compared to a smooth thrust bearing.

The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural network, and the improved multi-objective salp swarm algorithm was used to optimize the process parameters.

The purpose of this study is to evaluate three types of textures designed to enhance the tribological performance of static and dynamic pressure thrust bearings.

To explore the effects of different types of textures on tribological performance, the Reynolds equation is modified using lubrication theory and computational fluid dynamics methods while considering the influence of cavitation and turbulence on the physical field. In addition, the tribological performance is optimized through an improved selection algorithm based on Pareto envelope (PESA).

The results indicate that textured thrust bearings exhibit superior tribological performance compared to untextured ones. The circular texture outperforms other textures in terms of load-bearing and friction performance, with improvements of approximately 28.8% and 18.9%, respectively. In addition, the triangular texture exhibits the most significant temperature improvement, with a reduction of approximately 1.93%.

The study proposes three types of textures and evaluates the friction performance of thrust bearings by modifying the Reynolds equation. In addition, the optimal texture design is determined using an improved selection algorithm based on PESA.