JOURNAL ARTICLE

Motor Magnetic Field Analysis Using the Alpha Finite Element Method (αFEM).

  • Published In: International Journal of Computational Methods, 2026, v. 23, n. 5. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Peng, M. D.; Zhang, G. Y.; He, Z. C.; Huang, Y. Y.; Gao, H. 3 of 3

Abstract

This paper delves into the application of the alpha finite element method (α FEM) in the simulation of electromagnetic fields within electric machines, with a particular focus on permanent magnet synchronous motors. Electromagnetic simulation analysis based on the finite element method (FEM) has become a critical tool for the design and optimization of electric machines. Traditional FEM, which employ linear triangular elements, often encounter challenges regarding computational accuracy and efficiency in complex electromagnetic scenarios. α FEM, a variant within the smooth finite element method (S-FEM) family, enhances the capabilities of conventional FEM by scaling physical coordinates or strain gradients in the Jacobian matrix with an adjustable factor " α." This modification yields more precise numerical solutions, rendering α FEM particularly effective in dynamic problems such as vibration, acoustics, and plate analysis. Widely acknowledged for its stability and convergence properties, α FEM is utilized in this study to analyze computational performance in simulating the electromagnetic fields of motor. Numerical examples presented confirm that α FEM achieves higher computational accuracy with the same mesh model compared to conventional FEM. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Computational Methods. 2026/06, Vol. 23, Issue 5, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2026
  • ISSN:02198762
  • DOI:10.1142/S0219876224410068
  • Accession Number:193629761
  • Copyright Statement:Copyright of International Journal of Computational Methods is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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