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arXiv · 2201.08877

Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines

Vivek Parekh·Dominik Flore·Sebastian Schöps
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Abstract

Conventional magneto-static finite element analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, design optimization is commonly performed independently. This paper presents a novel method for predicting Key Performance Indicators (KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high dimensional integrated design parameters in a lower dimensional latent space using a variational autoencoder. After training, via a latent space, the decoder and multi-layer neural network will function as meta-models for sampling new designs and predicting associated KPIs, respectively. This enables parameter-based concurrent multi-topology optimization.

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Vivek Parekh, Dominik Flore, Sebastian Schöps. 2022-01-21. Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines. https://doi.org/10.1109/tmag.2022.3163972

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