SURROGATE-MODEL-BASED OPTIMIZATION DESIGN OF WOUND-FIELD FLUX SWITCHING MACHINE FOR INDUSTRIAL APPLICATIONS

Chiweta Abunike; Udochukwu Akuru; Ogbonnaya Okoro; Chukwuemeka Awah; Imon Nkan; Uchechi Innocent; Ekom Okpo; Peter Udenze; Muncho Mbunwe

doi:10.4314/njt.v44i2.11

Authors

C. E. Abunike Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria
U. B. Akuru Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa
O. I. Okoro Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria
C. C. Awah Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria
I. E. Nkan Department of Electrical/Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria
U. O. Innocent Department of Electrical/Electronic Engineering, Alex-Ekwueme Federal University, Ndufu-Alike, Nigeria
E. E. Okpo Department of Electrical/Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria
P. I. Udenze Department of Electrical/Electronic Engineering, University of Agriculture, Makurdi, Nigeria
M. J. Mbunwe Department of Electrical Engineering, University of Nigeria, Nsukka, Nigeria

DOI:

https://doi.org/10.4314/njt.v44i2.11

Keywords:

Finite Element Analysis, Response Surface Methodology, Surrogate Model, Torque Optimization, Torque Ripple Minimization, Wound-Field Flux Switching Machine

Abstract

The Wound-Field Flux Switching Machine (WFFSM) is a promising solution for high-performance industrial applications, offering high torque density, brushless operation, and controllable excitation flux. However, optimizing its design is challenging due to nonlinear flux interactions and multiple competing objectives. This study presents a surrogate-model-based optimization framework using response surface methodology (RSM) and multi-objective genetic algorithm (MOGA) to enhance the electromagnetic performance of a 1.5 kW WFFSM. Finite element analysis (FEA)-driven sampling and RSM surrogate modeling enable efficient exploration of the design space. The optimized WFFSM achieves a 77.7% reduction in torque ripple compared to the initial model, along with significant improvements in torque output and enhanced back-EMF characteristics. The proposed approach contributes a novel, systematic optimization strategy specifically tailored for industrial WFFSM applications, ensuring improved efficiency, reliability, and adaptability in next-generation electric machines.

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