Correlating structural complexity and acoustic noise performance of electric motors

Abstract

Purpose

Acoustic noise is a crucial performance index in the design of electrical machines. Due to the challenges associated with modelling a complete motor, the stator is often used to estimate the sound power in the prototyping stage. While this approach greatly reduces lengthy simulations, the actual sound power of the motor may not be known. But, from the acoustic noise standpoint, not much is known about the correlation between the stator and complete motor. This paper, therefore, aims to use the sound pressure levels of the stator and the full motor to investigate the existence of correlations in the interior permanent magnet synchronous motor.

Design/methodology/approach

A multiphysics simulation framework is proposed to evaluate the sound pressure levels of multiple motor geometries in a given design space. Then, a statistical analysis is performed on the calculated sound pressure levels of each geometry over a selected speed range to compare the correlation strength between the stator and the full model.

Findings

It was established that the stator and the complete motor model are moderately correlated. As such, a reliance on the stator sound power for design and optimization routines could yield inaccurate results.

Originality/value

The main contribution involves the use of statistical tools to study the relationship between sound pressure levels associated with the stator geometry and the complete electric motor by increasing the motor sample size to capture subtle acoustic correlation trends in the design space of the interior permanent magnet synchronous motor.

Citation

Ibrahim, I., Mohammadi, M.H., Ghorbanian, V. and Lowther, D. (2022), "Correlating structural complexity and acoustic noise performance of electric motors", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 41 No. 3, pp. 925-937. https://doi.org/10.1108/COMPEL-03-2021-0094