title: Deep Learning based Design and Optimization of Electrical Machines
Creators: name: Parekh, Vivek
Publisher: name: UNSPECIFIED
Year: 2024

description: Developing engineering products requires significant natural resources, human effort, and time in the industry. It can be expensive if physical phenomena are not predicted correctly during manufacturing. Virtual prototyping enables the analysis of physical processes under real-world conditions before actual product manufacturing. Various simulation softwares have been developed in recent decades that allow for considering different working conditions, complex design criteria, and constraints during design simulation. However, these simulation tools require significant computational power to solve complex mathematical models, which limits the capacity of numerical analysis for exploring a large design space for optimal designs. Data-driven deep learning (DL) methods have evolved in recent years. They can notably reduce expensive computational effort by finding a low-cost meta-model to predict physical output quantities in the design process.

In this thesis, different data-driven DL approaches for accelerating the design optimization procedure of electrical machines are investigated. All the proposed approaches are focused on enabling the exploration of a high-dimensional design space to generate optimal machine designs while saving computational resources. 

First, various permanent magnet synchronous machine (PMSM) geometry representations are analyzed. Image-based models are studied for different pixel resolutions. A quantitative comparison is made between the image-based and scalar parameter-based meta-models for approximating cross-domain key performance indicators (KPIs) of PMSMs. Numerical results showed that the scalar parameter-based meta-model has high prediction accuracy while being computationally cheap. On the other hand, image-based models are more flexible in scenarios, e.g., cross-rotor topologies and reparameterization. All trained meta-models evaluate new designs in much less time than conventional finite element simulations. 

Second, a hybrid data- and physics-driven approach is proposed to improve the scalar representation’s prediction accuracy and flexibility for quantifying the performance of PMSMs. The electromagnetic behavior is characterized using a data-driven DL approach, and subsequent KPIs are evaluated using a physics-based post-processing tool. The hybrid approach is compared to a data-driven approach. Finally, multi-objective optimization is performed using a hybrid approach in industrial settings, and quantitative analysis is conducted. 

A method is introduced to predict KPIs by mapping a high-dimensional complex scalar design space to a lower-dimensional latent space for differently parameterized machine technologies and topologies using a deep generative model. This approach enables concurrent parametric optimization of different machine types and rotor topologies with a single meta-model training. The proposed method is demonstrated for two scenarios: first, for the concurrent optimization of heterogeneously parameterized rotor topologies, and second, for heterogeneously parameterized machine technologies. 

All proposed methods can be applied to any industrial product design workflow where the physical phenomena can be described as a system of linear or nonlinear functions.