Multi-objective free-form shape optimization of a synchronous reluctance machine
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866917370373603328 |
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| author | Gangl, Peter Köthe, Stefan Mellak, Christiane Cesarano, Alessio Mütze, Annette |
| author_facet | Gangl, Peter Köthe, Stefan Mellak, Christiane Cesarano, Alessio Mütze, Annette |
| contents | This paper deals with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque, by means of gradient-based free-form shape optimization. The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. We validate our results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm. Finally, we show an extension of the free-form shape optimization algorithm to the case of multiple objective functions and illustrate a way to obtain an approximate Pareto front. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2010_10117 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Multi-objective free-form shape optimization of a synchronous reluctance machine Gangl, Peter Köthe, Stefan Mellak, Christiane Cesarano, Alessio Mütze, Annette Computational Engineering, Finance, and Science Optimization and Control This paper deals with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque, by means of gradient-based free-form shape optimization. The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. We validate our results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm. Finally, we show an extension of the free-form shape optimization algorithm to the case of multiple objective functions and illustrate a way to obtain an approximate Pareto front. |
| title | Multi-objective free-form shape optimization of a synchronous reluctance machine |
| topic | Computational Engineering, Finance, and Science Optimization and Control |
| url | https://arxiv.org/abs/2010.10117 |