In the present work, an optimum design and analyses of active magnetic thrust bearings (AMTB) and hybrid magnetic thrust bearings (HMTB) have been carried out. The active magnetic bearing contains only electromagnets, whereas, the hybrid contains both the electro-magnets and permanent magnets. Initially, the optimization has been carried out using single-objective genetic algorithms (SOGA). Two objectives, namely the power-loss and overall weight of the bearing, are considered one at a time. Different constraints considered are the maximum current density flow in the coil, the maximum flux density flow in the stator iron, the maximum power-loss allowed, and the maximum space occupied by the bearing. Two objectives considered are found to be conflicting. This led to the attempt of optimization by using multi-objective genetic algorithms (MOGA) by considering two objectives simultaneously namely, the power-loss and overall weight of the bearing. The effect of load on the Pareto frontier has been studied, and the load is found out to be an objective in addition to the weight and the power-loss. A complex system of double-acting hybrid magnetic thrust bearings (DAHMTB) with a centralized controller as an integrated system has been optimized by using MOGA. Five objectives are considered with three for the actuator and two for the controller. Additional constraints considered are stability conditions of the controller. Though power amplifiers can be designed with respect to designed controller requirements, sometimes it is not possible to have the required power amplifiers as a standard one, and one has to design the controller by taking the constraints of the power amplifier available at hand. Though centralization of controller requires less number of power amplifiers, but needs some complex winding scheme and control strategies. To go for a simpler winding and control strategies one may have to go for a decentralized actuator, controller and power amplifier in double acting magnetic bearing systems. Hence, the design optimization methodology is extended to the DAHMTB with decentralized controller systems by taking consideration of constraints of the power amplifier, namely the maximum power rating, and the voltage of the power amplifier. The overall exercise of the optimization gives rise to a novel methodology of analysis of Pareto optimal systems called Pareto optimal design analysis by which one can predict the behavior of different designs in the Pareto front with respect to each other. It has also lead to a general integrated design optimization methodology by which one can optimize magnetic bearing systems with the actuator, controller, and amplifier as an integrated system by using the SOGA or the MOGA. The behavior of different parameters with respect to tradeoffs has been explored in detail.

Design optimization of axial hybrid magnetic thrust bearings (with bias magnets) is carried out using multi-objective evolutionary algorithms (MOEAs) and compared with the case of electro magnetic bearings (without bias magnets). Mathematical models of objective functions and associated constraints have been presented and discussed. The different aspects of MOEA implemented have been discussed. It is observed that the size of the bearing with bias magnets is considerably reduced as compared to the case of without bias magnets, with the objective function as the minimization of weight for the same operating conditions. Similarly, current densities get reduced drastically with biased magnets when the objective function is chosen as the minimisation of the powerloss. 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 of without bias magnets and some salient points are observed in the case of using bias magnets.

An optimum design of thrust magnetic bearings has been carried out using multi-objective genetic algorithms (MOGAs). The power-loss and the weight have been selected as the minimization type objective functions for the optimum design. The maximum space available, the maximum current density that can be supplied in the coil, the maximum magnetic flux density that is allowed in the stator-iron (i.e., the magnetic flux density at the saturation), and the load required to be supported have been chosen as constraints. The inner and outer radii of the coil, and the height of the coil have been proposed as design variables. Apart from the comparison of performance parameters in the form of figures and tables, designs are also compared through line diagrams. Post-processing has been done on the final optimized population by studying the variation of different parameters with respect to objective functions. The saturation of magnetic flux density and the saturation of coil current density are observed to be the salient points, where the major changes occur in the behavior of different design parameters. A criterion for the choice of one of the best design based on the minimum normalized distance near the utopia point is used. A sensitivity analysis has been done on a chosen design by giving small perturbations on the design variables. It is observed that the effect of the outer radius of the coil on the objective functions is nearly double as compared to other two design variables.