Parallel Wideband MLFMA for Analysis of Electrically Large, Nonuniform, Multiscale Structures

作     者：Hughey, Stephen Aktulga, H. M. Vikram, Melapudi Lu, Mingyu Shanker, Balasubramaniam Michielssen, Eric 

作者机构：Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci E Lansing MI 48824 USA GE Global Res Ctr Bengaluru 560066 India West Virginia Univ Inst Technol Dept Elect & Comp Engn Beckley WV 25801 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA 

出 版 物：《IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION》 (IEEE天线与传播汇刊)

年 卷 期：2019年第67卷第2期

页      面：1094-1107页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：NSF [ECCS-1408115] Department of Defense High Performance Computing and Modernization Program 

主　　题：Adaptive algorithms computational electromagnetics method of moments (MoM) multilevel fast multipole algorithm (MLFMA) parallel algorithms 

摘      要：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.