Abstract
This study presents a computationally cost-effective modeling approach for a switched reluctance machine (SRM) towards predicting vibration and acoustic noise. In the proposed approach, the SRM is modeled using Finite Element (FE) software for capturing magnetic snapshots from static simulations. Using an advanced field reconstruction method (FRM), these snapshots are used to develop basis functions to estimate magnetic fields under any arbitrary stator excitation and at any desired rotor position. This method includes magnetic properties of the machine and can estimate flux density at once instead of partially predicting it. The vibration model is built in FE software while the acoustic noise is predicted using the analytical method. The proposed study can significantly reduce the computational time for vibration and noise analysis with decent accuracy. Dynamic simulation by finite-element analysis (FEA) software and experimental verification have been carried out to verify the effectiveness of the proposed hybrid model.
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