基于物理光学的特征基有限元法(CBFEM)在大规模电磁问题中的应用

O. Ozgun, R. Mittra, M. Kuzuoglu
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引用次数: 0

摘要

近年来,特征基函数法(CBFM)被引入求解大规模电磁问题。CBFM是一种非迭代的域分解方法,它使用称为高级物理基函数的特征基函数(cbf)来表示每个子域内的字段。该技术最初是在矩量法(MoM)的背景下用于求解时谐电磁问题[1]。最近,CBFM程序首次在有限元法(FEM)中得到应用,并被命名为“特征基有限元法(CBFEM)”[2-4]。该方法不同于以往基于mom的CBFM,已分别通过点电荷和偶极子型源产生CBFs,用于准静态[2]和时谐区域[3-4]。CBFEM的两个主要特点是:(i)它产生一个简化矩阵,可以通过使用直接求解而不是迭代求解来处理;(ii)可以利用其并行性来减少总体计算时间。CBFEM算法的基本步骤总结如下:(i)将计算域划分为若干子域;生成适合每个单独子领域的CBFs;将未知数表示为CBFs的加权总和;使用Galerkin程序将原始矩阵转换为较小的矩阵(称为约简矩阵),该程序使用CBFs作为基函数和测试函数;(v)求解权系数的约简矩阵,将系数代入级数表达式,求出整个计算域内的未知数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A version of the Characteristic Basis Finite Element Method (CBFEM) by utilizing Physical Optics for large-scale electromagnetic problems
During the last few years, the Characteristic Basis Function Method (CBFM) has been introduced to solve large-scale electromagnetic problems. The CBFM is a non-iterative domain decomposition approach that employs characteristic basis functions (CBFs), called the high-level physics-based basis functions, to represent the fields inside each sub-domain. This technique was first introduced to solve time-harmonic electromagnetic problems in the context of the Method of Moments (MoM) [1]. Quite recently, the CBFM procedure has been utilized for the first time in the Finite Element Method (FEM), and has been named the “Characteristic Basis Finite Element Method (CBFEM)” [2–4]. This method, which is different from the previous MoM-based CBFM, has been used in both the quasi-static [2] and the time-harmonic regimes [3–4], by generating the CBFs via point charges and dipole-type sources, respectively. Two major features of the CBFEM are: (i) it leads to a reduced-matrix that can be handled by using direct—as opposed to iterative—solvers; and (ii) its parallelizable nature can be taken advantage of to reduce the overall computation time. The basic steps of the CBFEM algorithm are summarized as follows: (i) Divide the computational domain into a number of subdomains; (ii) Generate the CBFs that are tailored to each individual subdomain; (iii) Express the unknowns as a weighted sum of CBFs; (iv) Transform the original matrix into a smaller one (called reduced-matrix) by using the Galerkin procedure, which uses the CBFs as both basis and testing functions; (v) Solve the reduced matrix for the weight coefficients, and substitute the coefficients into the series expressions to find the unknowns inside the entire computational domain.
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