Optimum Design and Analysis of Axial Hybrid Magnetic Bearings Using Multi-Objective Genetic Algorithms

Abstract

Design optimization of axial hybrid magnetic thrust bearings (with bias magnets) was carried out using multi-objective evolutionary algorithms (MOEAs) and compared with the case of electromagnetic bearings (without bias magnets). Mathematical models of objective functions and associated constraints are presented and discussed. The different aspects of implemented MOEA are discussed. It is observed that the size of the bearing with bias magnets is considerably reduced as compared to the case of those without bias magnets, with the objective function as the minimization of weight for the same operating conditions. Similarly, current densities aret reduced drastically with biased magnets when the objective function is chosen as the minimization of the power loss. For illustration of various performances of the bearing, a typical design has been chosen from the final optimized population by an “a posteriori” approach. Sensitivities for both the objective functions with respect to the outer radius, the inner radius, and the height of coil are observed to be approximately in the ratio 2.5:1.6:1. Analysis of final optimized population has been carried out and is compared with the case without bias magnets and some salient points are observed in the case of using bias magnets.