TY - JOUR

T1 - Novel reduced matrix equation constructing method accelerates iterative solution of characteristic basis function method

AU - Wang, Zhonggen

AU - Chen, Qiang

AU - Nie, Wen Yan

AU - Lin, Han

N1 - Funding Information:
This work was supported by the Natural Science Foundation of Anhui Province [Grant Number 1808085MF166, 1808085QF197]; the Postdoctoral Science Foundation of Anhui Province [grant number 2017B214]; the Overseas Visiting and Training Program for Outstanding Young Talents of Anhui Province [Grant Number gxgwfx2018025]; the Natural Science Foundation of Anhui Provincial Education Department [grant number KJ2016A669].
Publisher Copyright:
© ACES

PY - 2019

Y1 - 2019

N2 - In this paper, a new construction method of reduced matrix equation is proposed to improve the iterative solution efficiency of characteristic basis function method (CBFM). Firstly, the singular value decomposition (SVD) technique is applied to compress the incident excitations and these new excitations retained on each block after SVD are defined as the excitation basis functions (EBFs). Then, the characteristic basis functions (CBFs) of each block are solved from these EBFs. Lastly, these EBFs and CBFs are used as the testing functions and the basis functions to construct the reduction matrix equation, respectively. The diagonal sub-matrices of the reduced matrix constructed by the proposed method are all identity matrices. Thus, the condition of the reduced matrix is improved resulting in a smaller number of iterations required for the solution of the reduced matrix equation. The numerical results validate the accuracy of the proposed method. Compared with the traditional CBFM, the iterative solution efficiency of the reduced matrix equation constructed by the proposed method is significantly improved.

AB - In this paper, a new construction method of reduced matrix equation is proposed to improve the iterative solution efficiency of characteristic basis function method (CBFM). Firstly, the singular value decomposition (SVD) technique is applied to compress the incident excitations and these new excitations retained on each block after SVD are defined as the excitation basis functions (EBFs). Then, the characteristic basis functions (CBFs) of each block are solved from these EBFs. Lastly, these EBFs and CBFs are used as the testing functions and the basis functions to construct the reduction matrix equation, respectively. The diagonal sub-matrices of the reduced matrix constructed by the proposed method are all identity matrices. Thus, the condition of the reduced matrix is improved resulting in a smaller number of iterations required for the solution of the reduced matrix equation. The numerical results validate the accuracy of the proposed method. Compared with the traditional CBFM, the iterative solution efficiency of the reduced matrix equation constructed by the proposed method is significantly improved.

KW - Characteristic basis function method

KW - Characteristic basis functions

KW - Reduced matrix equation

KW - Singular value decomposition

KW - Testing functions

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M3 - Article

AN - SCOPUS:85077952455

VL - 34

SP - 1814

EP - 1820

JO - Applied Computational Electromagnetics Society Journal

JF - Applied Computational Electromagnetics Society Journal

SN - 1054-4887

IS - 12

ER -