Combined Parameter and Shape Optimization of Electric Machines with Isogeometric Analysis

TL;DR

This paper introduces a combined parameter and shape optimization method for electric machines using Isogeometric Analysis, enabling efficient design improvements and significant reductions in magnet mass and torque ripple.

Contribution

It presents a novel framework integrating parameter and shape optimization with IGA, employing an analytical adjoint method for efficient gradient computation.

Findings

Reduced permanent magnet mass in motor design.

Almost eliminated torque ripple through combined optimization.

Significantly decreased optimization time.

Abstract

In structural optimization, both parameters and shape are relevant for the model performance. Yet, conventional optimization techniques usually consider either parameters or the shape separately. This work addresses this problem by proposing a simple yet powerful approach to combine parameter and shape optimization in a framework using Isogeometric Analysis (IGA). The optimization employs sensitivity analysis by determining the gradients of an objective function with respect to parameters and control points that represent the geometry. The gradients with respect to the control points are calculated in an analytical way using the adjoint method, which enables straightforward shape optimization by altering of these control points. Given that a change in a single geometry parameter corresponds to modifications in multiple control points, the chain rule is employed to obtain the gradient with respect to the parameters in an efficient semi-analytical way. The presented method is exemplarily applied to nonlinear 2D magnetostatic simulations featuring a permanent magnet synchronous motor and compared to designs, which were optimized using parameter and shape optimization separately. It is numerically shown that the permanent magnet mass can be reduced and the torque ripple can be eliminated almost completely by simultaneously adjusting rotor parameters and shape. The approach allows for novel designs to be created with the potential to reduce the optimization time substantially.