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首页> 外文期刊>Applied Computational Electromagnetics Society journal >Augmented MLFMM for Analysis of Scattering from PEC Object with Fine Structures
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Augmented MLFMM for Analysis of Scattering from PEC Object with Fine Structures

机译：增强型MLFMM用于分析具有精细结构的PEC对象的散射

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摘要

In this paper, a multilevel Green's function interpolation method (MLGFIM) combined with multilevel fast multipole method (MLFMM) is presented for solving the electromagnetic scattering from the objects with fine structures. In the conventional MLFMM, the size of the finest cube must be larger than a definite value, which is typically 0.2 X; it often generates a large number of unknowns in each finest cube especially for objects with fine structures. Accordingly, it requires a lot of memory to store the near-field impedance matrix in MLFMM. In order to decrease the memory requirement of the near-field matrix in the MLFMM, the MLGFIM is introduced to calculate the near-field interactions. The number of unknowns in each cube can be less than a required number regardless of the size of the cube in the MLGFIM. To further reduce the computational complexity, many recompressed techniques, such as the adaptive cross approximation (ACA), QR factorization, and singular value decomposition (SVD), are applied to compress the low rank Green's function matrix for speeding up the matrix-vector multiplication. Numerical results are given to demonstrate the accuracy and efficiency of the proposed method.
机译：本文提出了一种多级格林函数插值方法（MLGFIM）和多级快速多极子方法（MLFMM）相结合的方法，以解决结构精细的物体的电磁散射问题。在传统的MLFMM中，最精细的立方体的大小必须大于一个确定的值，通常为0.2X。它通常会在每个最精细的立方体中生成大量未知数，尤其是对于具有精细结构的对象。因此，需要大量的存储器来将近场阻抗矩阵存储在MLFMM中。为了减少MLFMM中近场矩阵的存储需求，引入了MLGFIM来计算近场相互作用。无论MLGFIM中多维数据集的大小如何，每个多维数据集中的未知数都可以小于所需的数目。为了进一步降低计算复杂度，应用了许多重新压缩的技术，例如自适应交叉逼近（ACA），QR分解和奇异值分解（SVD），以压缩低秩格林函数矩阵，从而加快矩阵矢量乘法。数值结果表明了该方法的准确性和有效性。

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