Speaker
Description
In electric machine design efficient methods for the optimization of geometry and associated parameters are essential. Nowadays, it is necessary to handle uncertainty caused by manufacturing or material tolerances. In this work we propose a robust optimization approach to handle uncertainty in the design of a 3-phase, 6-pole Permanent Magnet Synchronous Motor (PMSM). The geometry is constructed in a two-dimensional framework, employing Isogeometric Analysis (IGA) to enable flexible shape optimization. The main contributions of this research are twofold. First, we integrate shape optimization with parameter optimization to enhance the performance of PMSM designs. Second, we use robust optimization which creates a min-max problem to ensure the motor maintains its performance when facing uncertainties. To solve this bilevel problem, we work with the maximal value functions of the lower level maximization problems and apply a version of Danskin's Theorem for the computation of generalized derivatives. Additionally, the adjoint method is employed to efficiently solve the lower level problems with gradient based optimization. The paper concludes by presenting numerical results.
