Convection Heat Transfer Coefficient Regression Models Construction for Fast High-Speed Motor Thermal Analysis

Abstract

This article proposes a computationally efficient thermal study method that substitutes computational fluid dynamics (CFDs) with a finite-element analysis (FEA) involving minimal loss of accuracy and omission of the increased computational load. The suggested technique contributes toward the realization of multiphysics optimization of high-speed electrical machines that effectively includes critical thermal evacuation phenomena. The strategy is regression-based and succeeds in constructing convection coefficient regression models that typically involve many variables and small training datasets. The proposed scheme is verified in high-loss density and high-speed outer rotor surface-mounted permanent magnet (SMPM) motor geometries that an optimization algorithm typically encounters during execution. The reference target specifications correspond to 20 kW output power, 15k r/min rotating speed, and 2 kHz electrical frequency.