Robust Shape Optimization of Electric Devices Based on Deterministic Optimization Methods and Finite Element Analysis With Affine Decomposition and Design Elements

TL;DR

This paper presents a deterministic gradient-based optimization framework for electric device design that leverages finite element analysis with affine decomposition and design elements, incorporating robustness against uncertainties, demonstrated on a machine example.

Contribution

It introduces a robust shape optimization method combining affine decomposition and design elements to avoid remeshing, with a focus on uncertainty handling in electric device design.

Findings

Deterministic optimization outperforms stochastic methods like particle swarm in accuracy.

Affine decomposition simplifies geometry updates without remeshing.

Robust optimization improves device performance under parameter uncertainties.

Abstract

In this paper, gradient-based optimization methods are combined with finite-element modeling for improving electric devices. Geometric design parameters are considered by affine decomposition of the geometry or by the design element approach, both of which avoid remeshing. Furthermore, it is shown how to robustify the optimization procedure, i.e., how to deal with uncertainties on the design parameters. The overall procedure is illustrated by an academic example and by the example of a permanent-magnet synchronous machine. The examples show the advantages of deterministic optimization compared to standard and popular stochastic optimization procedures such as, e.g., particle swarm optimization.