A Review of Subdomain Models for Design of Electric Machines: Opportunities and Challenges

The global transition toward electrification has accelerated the need for high-performance, sustainable electric machine designs. Emerging manufacturing techniques, particularly additive manufacturing, have enabled the development of complex and unconventional machine topologies. Designing novel machine topologies often relies on data-driven methods and topology optimization, which can be computationally intensive. Semi-analytic modeling offers an effective middle ground by balancing computational efficiency with modeling accuracy—positioned between fully analytical formulations and resource-intensive numerical simulations. While its advantages are recognized, the current literature lacks a unified overview of semi-analytic approaches applied across coupled multiphysics domains, including electromagnetic, thermal, and structural analyses. This paper addresses that gap by presenting a comprehensive review of recent semi-analytic modeling techniques relevant to electric machine design. The goal is to establish a foundational reference for researchers aiming to incorporate these models into advanced topology optimization frameworks.

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Publication Details

Journal
Energies
Published
2025-12-31
DOI
https://doi.org/10.3390/en19010222
Primary Topic
Topology Optimization in Engineering
Type
article
Field-Weighted Citation Impact
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article

A Review of Subdomain Models for Design of Electric Machines: Opportunities and Challenges

Shanelle N. Foster, Orwell Madovi
Energies
Topology Optimization in Engineering
article

A Review of Subdomain Models for Design of Electric Machines: Opportunities and Challenges

Shanelle N. Foster, Orwell Madovi
article en

Abstract

The global transition toward electrification has accelerated the need for high-performance, sustainable electric machine designs. Emerging manufacturing techniques, particularly additive manufacturing, have enabled the development of complex and unconventional machine topologies. Designing novel machine topologies often relies on data-driven methods and topology optimization, which can be computationally intensive. Semi-analytic modeling offers an effective middle ground by balancing computational efficiency with modeling accuracy—positioned between fully analytical formulations and resource-intensive numerical simulations. While its advantages are recognized, the current literature lacks a unified overview of semi-analytic approaches applied across coupled multiphysics domains, including electromagnetic, thermal, and structural analyses. This paper addresses that gap by presenting a comprehensive review of recent semi-analytic modeling techniques relevant to electric machine design. The goal is to establish a foundational reference for researchers aiming to incorporate these models into advanced topology optimization frameworks.

EnergiesVol. 19(1)
Michigan State University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 35%
Topology Optimization in Engineering
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