Surrogate-Based Acoustic Noise Prediction of Electric Motors

Abstract

Design and optimization problems typically require running thousands of motor simulations, which could take several hours if not days. Finding alternative means of reducing the solution time has recently gained research interest. Surrogate models can emulate the outputs of computer simulations with less computational effort. This article proposes the use of surrogate models to predict the acoustic noise, applied to an interior permanent-magnet synchronous motor (IPMSM). The simulation procedure involves using finite element analysis (FEA) to evaluate the acoustic performance across a design space of stator and rotor geometric variations. Then, four different classes of surrogate models are used to learn a portion of the design space before attempting to generalize and make predictions in a much larger space with relatively less computational burden. It is demonstrated that the trained models can be considered as appropriate replacements of the time-consuming FEA for future design and optimization problems of the same motor case study.