Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines
This addresses the problem of inefficient design optimization for electrical machine engineers by providing a faster, concurrent approach, though it appears incremental as it builds on existing variational autoencoder techniques.
The paper tackles the time-consuming and computationally expensive nature of conventional magneto-static finite element analysis in electrical machine design by introducing a variational autoencoder-based method to predict Key Performance Indicators (KPIs) for multiple machine topologies simultaneously, enabling parameter-based concurrent multi-topology optimization.
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.