Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach

• URL: http://arxiv.org/abs/2306.09096v1
• Date: Thu, 15 Jun 2023 12:47:56 GMT
• Title: Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach
• Authors: Vivek Parekh, Dominik Flore, Sebastian Sch\"ops, Peter Theisinger
• Abstract summary: We present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM) Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Magneto-static finite element (FE) simulations make numerical optimization of electrical machines very time-consuming and computationally intensive during the design stage. In this paper, we present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM). Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM by predicting intermediate FE measures. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators (KPIs), e.g., torque, shaft power, and material costs. We perform multi-objective optimization with both classical FE and a hybrid approach using a nature-inspired evolutionary algorithm. We show quantitatively that the hybrid approach maintains the quality of Pareto results better or close to conventional FE simulation-based optimization while being computationally very cheap.

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