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A New Adaptive Mesh Refinement Method in FEA Based on Magnetic Field Conservation at Elements Interfaces and Non-Conforming Mesh Refinement Technique

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/66970

Title: A New Adaptive Mesh Refinement Method in FEA Based on Magnetic Field Conservation at Elements Interfaces and Non-Conforming Mesh Refinement Technique
Authors: Noguchi, So Browse this author →KAKEN DB
Naoe, Takuto Browse this author
Igarashi, Hajime Browse this author →KAKEN DB
Matsutomo, Shinya Browse this author
Cingoski, Vlatko Browse this author
Ahagon, Akira Browse this author
Kameari, Akihisa Browse this author
Keywords: Adaptive meshing
element surface integral term
error estimation
finite-element analysis (FEA)
Issue Date: Jun-2017
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE transactions on magnetics
Volume: 53
Issue: 6
Start Page: 7201904
Publisher DOI: 10.1109/TMAG.2017.2655049
Abstract: Mesh quality strongly affects the solution accuracy in electromagnetic finite-element analysis. Hence, the realization of adequate mesh generation becomes a very important task. Several adaptive meshing methods for automatic adjustments of the mesh density in accordance with the shape and complexity of the analyzed problem have been proposed. However, the most of them are not enough robust, some are quite laborious and could not be universally used for adaptive meshing of complex analysis models. In this paper, a new adaptive mesh refinement method based on magnetic field conservation at the border between finite elements is proposed. The proposed error estimation method provides easy mesh refinements, and generates smaller element within regions with large curvature of the magnetic flux lines. The proposed adaptive mesh refinement method based on non-conforming edge finite elements, which could avoid generation of flat or ill-shaped elements, was applied to a simple magnetostatic permanent magnet model. To confirm the validity and accuracy, the obtained results were compared with those obtained by means of the Zienkiewicz-Zhu (ZZ) error estimator. The results show that the computational error using the proposed method was reduced down to 1% compared with that of the ZZ method, which yields error of 8.6%, for the same model.
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article (author version)
URI: http://hdl.handle.net/2115/66970
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 野口 聡

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