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Research of the Algebraic Multigrid Method for Electron Optical Simulator.
Wang, Zhi; Hu, Quan; Zhu, Xiao-Fang; Li, Bin; Hu, Yu-Lu; Huang, Tao; Yang, Zhong-Hai; Li, Liang.
Afiliación
  • Wang Z; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Hu Q; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Zhu XF; Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518000, China.
  • Li B; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Hu YL; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Huang T; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Yang ZH; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Li L; Vacuum Electronics National Laboratory, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China.
Entropy (Basel) ; 24(8)2022 Aug 16.
Article en En | MEDLINE | ID: mdl-36010797
ABSTRACT
At present, electron optical simulator (EOS) takes a long time to solve linear FEM systems. The algebraic multigrid preconditioned conjugate gradient (AMGPCG) method can improve the efficiency of solving systems. This paper is focused on the implementation of the AMGPCG method in EOS. The aggregation-based scheme, which uses two passes of a pairwise matching algorithm and the K-cyle scheme, is adopted in the aggregation-based algebraic multigrid method. Numerical experiments show the advantages and disadvantages of the AMG algorithm in peak memory and solving efficiency. The AMGPCG is more efficient than the iterative methods used in the past and only needs one coarsening when EOS computes the particle motion trajectory.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Entropy (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China
Texto completo
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Entropy (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China