JOURNAL ARTICLE

The Weighted Least-Squares Collocation Method for Elastic Wave Obstacle Scattering Problems.

  • Published In: International Journal of Computational Methods, 2024, v. 21, n. 10. P. 1 1 of 3

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

  • Authored By: Zhang, Jing; Li, Siqing; Yue, Junhong; Wang, Yu 3 of 3

Abstract

Scattering problems have wide applications in the medical and military fields. In this paper, the weighted least-squares (WLS) collocation method based on radial basis functions (RBFs) is developed to solve elastic wave scattering problems, which are governed by the Navier equation and the Helmholtz equations with coupled boundary conditions. The perfectly matched layer (PML) technique is used to truncate the unbounded domain into a bounded domain. The WLS method is constructed by setting the collocation points denser than the trial centers and imposing different weights on different types of boundary conditions. The WLS method can overcome the matrix singularity problem encountered in the Kansa method, and the convergence rate of WLS is m − 2 for Sobolev kernel with kernel smoothness m. Furthermore, compared with the finite element method (FEM) and the Kansa method, WLS can provide higher accuracy and more stable solutions for relatively large angular frequencies. The numerical example with a circular obstacle is used to verify the effectiveness and convergence behavior of the WLS. Besides, the proposed scheme can easily handle irregular obstacles and obtain stable results with high accuracy, which is validated through experiments with ellipse and kite-shaped obstacles. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Computational Methods. 2024/12, Vol. 21, Issue 10, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:02198762
  • DOI:10.1142/S0219876223410098
  • Accession Number:181415776
  • 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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