Radial magnetic bearing (RMB) works on magnetic levitation principle. Use of magnetic forces for supporting a shaft has been topic of interest of researchers. Shaft supported using magnetic force eliminates the use of lubricant and these bearing are free from rubbing action between the surfaces. Application of turbine rotor 60 mm diameter is taken into consideration and RMB of eight poles with NSNS pole configuration has been designed. Bearing is designed such that it is capable of generating electromagnetic force to levitate rotor with constant air gap 0.5 mm in dynamic condition. However these bearing encounter various energy losses. Loss optimization of eight poles RMB is carried out using variables like air gap, turbine speed, and laminating layer thickness over the constraints. Optimum results of bearing design variables obtained using Genetic Algorithm (GA) ensures minimum total loss in bearing. These optimized variables are used as initial iteration in reduced gradient based method. Results obtained in GA are validated using Reduced Gradient method.
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