Parallel Wideband MLFMA for Analysis of Electrically Large, Nonuniform, Multiscale Structures
Parallel Wideband MLFMA for Analysis of Electrically Large, Nonuniform, Multiscale Structures
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DOI：
10.1109/tap.2018.2882621
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发表时间：
2019-02
影响因子：
5.7
通讯作者：
S. Hughey;H. Aktulga;M. Vikram;Mingyu Lu;B. Shanker;E. Michielssen
中科院分区：
文献类型：
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作者：
S. Hughey;H. Aktulga;M. Vikram;Mingyu Lu;B. Shanker;E. Michielssen
具有多尺度特征的电大尺寸目标的电磁散射是计算电磁学中一个日益重要的问题。传统的方法是使用基于积分方程的求解器，然后用加速器增强，一个流行的选择是并行多级快速多极子算法（MLFMA）。多尺度特征的一个后果是局部密集离散化，这导致低频故障，需要非均匀树。据作者所知，文献中的并行MLFMA的多尺度分布能够任意精度是稀疏的，本文的目的是填补这个利基。我们规定的算法，克服了这个瓶颈。我们证明的准确性（相对于分析数据）和性能的算法PEC散射体和点云高达755美元，数亿未知数和非均匀树深16级。
Electromagnetic scattering from electrically large objects with multiscale features is an increasingly important problem in computational electromagnetics. A conventional approach is to use an integral equation-based solver that is then augmented with an accelerator, a popular choice being a parallel multilevel fast multipole algorithm (MLFMA). One consequence of multiscale features is locally dense discretization, which leads to low-frequency breakdown and requires nonuniform trees. To the authors’ knowledge, the literature on parallel MLFMA for such multiscale distributions capable of arbitrary accuracy is sparse; this paper aims to fill this niche. We prescribe an algorithm that overcomes this bottleneck. We demonstrate the accuracy (with respect to analytical data) and performance of the algorithm for both PEC scatterers and point clouds as large as $755{\lambda }$ with several hundred million unknowns and nonuniform trees as deep as 16 levels.