In this contribution the authors use a Deep Neural Network based approach for the optimization of an electric motor, taking into account both electromagnetic and mechanical constraints, i.e. approaching the problem from the multiphysics point of view. In the design process of high speed electric motors, the mechanical design of the rotor is of noteworthy importance, and in case of reluctance motors it cannot be separated from the electromagnetic design. The multiphysics model is created by using a commercial FEM software, and a multiobjective optimization procedure is run by using the before mentioned software. This is the selected tool for the generation of the training dataset used to train a Deep Neural Network, that is used to refine the sub-optimal solutions previously obtained. The results show that the use of a two-step optimization lead to a better solution.

Deep Neural Network Based Electro-Mechanical Optimization of Electric Motors

Barmada S.;Tucci M.;Sani L.;
2023-01-01

Abstract

In this contribution the authors use a Deep Neural Network based approach for the optimization of an electric motor, taking into account both electromagnetic and mechanical constraints, i.e. approaching the problem from the multiphysics point of view. In the design process of high speed electric motors, the mechanical design of the rotor is of noteworthy importance, and in case of reluctance motors it cannot be separated from the electromagnetic design. The multiphysics model is created by using a commercial FEM software, and a multiobjective optimization procedure is run by using the before mentioned software. This is the selected tool for the generation of the training dataset used to train a Deep Neural Network, that is used to refine the sub-optimal solutions previously obtained. The results show that the use of a two-step optimization lead to a better solution.
2023
Barmada, S.; Tucci, M.; Sani, L.; Tozzo, C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1165274
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