Electric machine design automation with Python and ANSYS Maxwell

Conference: IKMT 2019 – Innovative Klein- und Mikroantriebstechnik - 12. ETG/GMM-Fachtagung
09/10/2019 - 09/11/2019 at Würzburg, Deutschland

Proceedings: ETG-Fb 159: IKMT 2019

Pages: 7Language: englishTyp: PDF

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Schwarz, Patrick; Moeckel, Andreas (Technische Universität Ilmenau, Ilmenau, Germany)

Nowadays global competition is characterized by high innovation speed as well as short development and product life cycles. Especially the field of electric machines is marked by rising expectations regarding efficiency, power density and production costs. In order to keep development times as low as possible and realize optimization loops in workable periods, effective design approaches are necessary. Usually the preliminary design is based on an experience-based approach with analytical equations. In further steps the design is often verified by static or transient finite element method (FEM) and optimized in details by parameter variations within an experience-based parameter area. In general, there are several strategies for optimizing machine designs, starting with analytical calculations over finite element method calculations up to self-learning systems. The work on this paper is based on the idea of defining experience-based and product-specific sequences of steps with appropriate weighting and adapted parameter areas. Furthermore, these sequences will be processed automatically. An automated FEM-model generation is essential for this kind of optimization. This paper is focused on the model generation based on the open source programming language Python and the commercial simulation program ANSYS Maxwell.