Optimal design of an annular thrust air bearing using parametric computational fluid dynamics model and genetic algorithms

Abstract

The performance of air bearing is highly influenced by the geometrical parameters of its restrictor. This study aims to maximize the load-carrying capacity and stiffness of air bearing, and minimize its volume flow rate by optimizing the geometrical parameters of restrictor. To facilitate the calculation of air bearing performance, a parametric computational fluid dynamics model is developed. Then, it is combined with multiobjective optimization genetic algorithm to search the Pareto optimal solutions. Furthermore, as a case study, the optimal design of an annular thrust air bearing is implemented. The stiffness of air bearing is improved 38.5%, the load-carrying capacity is improved 33.9%, and the volume flow rate is declined 19.6%, which are finally validated by experiments. It proves the reliability of proposed parametric computational fluid dynamics model and genetic optimization algorithm.