Deep learning based Meta-modeling for Multi-objective Technology Optimization of Electrical Machines

• URL: http://arxiv.org/abs/2306.09087v3
• Date: Mon, 24 Jul 2023 07:55:19 GMT
• Title: Deep learning based Meta-modeling for Multi-objective Technology Optimization of Electrical Machines
• Authors: Vivek Parekh, Dominik Flore, Sebastian Sch\"ops
• Abstract summary: We present the application of a variational auto-encoder to optimize two different machine technologies simultaneously. After training, we employ a deep neural network and a decoder as meta-models to predict global key performance indicators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Optimization of rotating electrical machines is both time- and computationally expensive. Because of the different parametrization, design optimization is commonly executed separately for each machine technology. In this paper, we present the application of a variational auto-encoder (VAE) to optimize two different machine technologies simultaneously, namely an asynchronous machine and a permanent magnet synchronous machine. After training, we employ a deep neural network and a decoder as meta-models to predict global key performance indicators (KPIs) and generate associated new designs, respectively, through unified latent space in the optimization loop. Numerical results demonstrate concurrent parametric multi-objective technology optimization in the high-dimensional design space. The VAE-based approach is quantitatively compared to a classical deep learning-based direct approach for KPIs prediction.

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