Design Optimization of an Axial Flux Magnetic Gear by Using Reluctance Network Modeling and Genetic Algorithm

The use of a suitable modeling technique for the optimized design magnetic gear is essential to simulate its electromagnetic behavior and predict satisfactory performance. This paper presents optimization an axial flux (AFMG) using two-dimensional (2D) equivalent circuit model (MEC) Multi-objective Genetic Algorithm (MOGA). proposed MEC configured as meshed reluctance network (RN) with permanent magnet magnetomotive force sources. non-linearity in ferromagnetic materials accounted by MEC. based on networks considered be good compromise between accuracy computational effort. new will allow faster analysis AFMG. A multi-objective carried out achieve optimal volume-focused AFMG future practical applications. performance then verified establishing density comparisons finite element simulations. study shows that combination MEC-RN GA optimization, can achieved compared (FEA), but only fraction time.