Efficient Analysis of Object with Fine Structures by Combined MLSSM/MLFMA via Compressed Block Decomposition Preconditioner

摘要

A large dense fine mesh is used to model object with fine structures to guarantee good solution accuracy, and this in turn places an inordinately heavy burden on the CPU in terms of both memory requirement and computational complexity. To analyze the large dense complex linear system efficiently, the combined MLSSM/ MLFMA is used to accelerate the matrix-vector multiplication. Multilevel fast multipole algorithm (MLFMA) cannot be used to analyze the box's size of tree structure below 0.2 wavelength, because the "low frequency breakdown" phenomenon would happen. For the large-scale problems, the matrix assembly time of multilevel simply sparse method (MLSSM) is much longer than that of MLFMA. This combined method takes advantage of the virtues of both MLFMA and MLSSM, which is more efficient than either conventional MLFMA or conventional MLSSM. An efficient preconditioning technique based on compressed block decomposition (CBD) is applied to speed up the convergence rate. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method.