Millimeter wave scattering of 2-D frequency selective surface by efficient method of lines with preconditioned CG technique

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

2 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1529-1543
Journal / PublicationInternational Journal of Infrared and Millimeter Waves
Volume23
Issue number10
Publication statusPublished - Oct 2002

Abstract

In this paper, both banded and symmetric successive overrelaxation (SSOR) preconditioned conjugate gradient (PCG) techniques are combined with method of lines (MOL) to further enhance the computational efficiency of this semi-analytic method. The electromagnetic wave scattering of 2-D frequency-selective surface is used as the examples to describe its implementation, whose analysis usually needs fast algorithms because of electrically large dimension. For arbitrary incident wave, helmholte equation and boundary condition are used to calculate the impedance matrix and then to obtain reduced current-voltage linear matrix equation in spatial domain. Both banded and effective symmetric successive overrelaxation preconditioned conjugate gradient iterative method are chosen to solve this matrix equation. Our numerical results show that PCG methods can converge to accurate solution in much fewer iteration steps for analysis of the electromagnetic wave scattering from 2-D frequency-selective surface.

Research Area(s)

  • Conjugate gradient method, Millimeter wave scattering, Preconditioning technique, Symmetric successive overrelaxation

Citation Format(s)

Millimeter wave scattering of 2-D frequency selective surface by efficient method of lines with preconditioned CG technique. / Mo, L.; Tsang, K. F.; Yung, Edward K.N.; Chen, R. S.; Fang, D. G.

In: International Journal of Infrared and Millimeter Waves, Vol. 23, No. 10, 10.2002, p. 1529-1543.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal