This paper presents the development of a design optimization module for achieving the best performance of hydrostatic bearings. The design optimization module consists of two components: a bearing performance analysis module and an optimization module that utilizes optimization algorithms. Widely recognized global search methods, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were employed as the optimization algorithms. The design optimization problem was defined for hydrostatic bearings. Optimization design processes were carried out to improve load capacity, stiffness, and flow rate. Subsequent experimental validation was conducted through the fabrication of a practical experimental setup. The design optimization model demonstrated superior performance compared to the initial model while satisfying design conditions and constraints. This confirms the practical applicability of the design optimization module developed in this study.

Magnetic bearings are being actively adopted by the turbo-chiller industry because of their higher efficiency during partial load, quieter operation, and smaller footprint than that which machines with ball bearings provide. Since magnetic bearings are open-loop unstable, feedback control is necessary. In the industry, traditional PID-based control is preferred to model-based control, because of its simplicity. When traditional control algorithms are used, significant resources are required to obtain and tune control parameters, which is an impediment to the widespread use of magnetic bearing technology in the industry. In this paper, we propose a mixed optimization method by combining genetic algorithm and sequential quadratic programming. To obtain the initial guess to be used for the mixed optimization, a phase-margin maximization algorithm is also proposed, based on the rigid-body model of the system. Mixed optimization results in suitable control parameters in less than 2.8% of the time it takes a genetic algorithm only to find similar solutions. The proposed optimization also ensures the robustness of the control parameters. The output sensitivity measured from a prototype compressor with magnetic bearings confirms the validity of the control parameters.