2005 Fiscal Year Final Research Report Summary
Study on machine learning for generation of finite element models of electrical machines
| Project/Area Number |
15560233
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
電力工学・電気機器工学
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| Research Institution | Hokkaido University |
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Principal Investigator |
IGARASHI Hajime Hokkaido Univ., Graduate School of Information Sc.and Tech., Professor, 大学院・情報科学研究科, 教授 (90212737)
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| Co-Investigator(Kenkyū-buntansha) |
NOGUCHI So Hokkaido Univ., Graduate School of Information Sc.and Tech., Associate Professor, 大学院・情報科学研究科, 助教授 (30314735)
WATANABE Kota Hokkaido Univ., Graduate School of Information Sc.and Tech., Research Associate, 大学院・情報科学研究科, 助手 (20322828)
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| Project Period (FY) |
2003 – 2005
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| Keywords | Mesh Generation / Finite Element Method / Genetic Algorithm / Machine Learning |
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| Research Abstract |
In this research, we have developed a new method to realize automatic generation of finite element meshes for electric machines. In this method, an initial course mesh is prepared, and element properties P are computed where P is a linear combination of element features such as magnitude of magnetic induction at the element, distance from the nearest corner of magnetic material, distance from the nearest current source and so on. The element with largest P is subdivided into two finer elements. And this process is repeated until the number of elements equals a prescribed number. The error in the finite element analysis on the obtained mesh is then computed. This error strongly depends on the mesh quality, which is dependent on the weights in the linear combination of the element features. We optimize the weights using the genetic algorithm. When the weights are optimized, this method can be applied for other similar finite element models of electrical machines.
It is shown that the resu
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ltant finite element mesh using the above method, called the simple method, often have flat elements which are inadequate for finite element analysis. To resolve this difficulty, we introduce mesh control techniques so that the number of subdivision of element edges is determined from their length and a criterion to choose elements to be subdivided. The finite element meshes obtained using this method, called the mesh control method, are shown to be better than those obtained using the simple method.
Although the mesh quality is improved by the mesh control method, it becomes worse when the initial course mesh includes flat elements. It is difficult to overcome this problem as long as the mesh is generated from the initial mesh. For this reason, we introduce another method where the density of nodes is determined using the above mentioned machine learning, and elements are generated using the Delaunay triangulation on the basis of the nodes obtained above. This method is shown to improve the mesh quality. Less
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