Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is su...Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.展开更多
The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this paper.In the convent...The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this paper.In the conventional MLCBFM based on Foldy-Lax multiple scattering equations,the improvement is only made in the generation of characteristic basis functions(CBFs).However,it does not provide a change in impedance matrix filling and reducing matrix calculation procedure,which is time-consuming.In reality,all the impedance and reduced matrix of each level of the MLCBFM have low-rank property and can be calculated efficiently.Therefore,ACA is used for the efficient generation of two-level CBFs and the fast calculation of reduced matrix in this study.Numerical results are given to demonstrate the accuracy and efficiency of the method.展开更多
文摘Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.
基金supported by the National Natural Science Foundation of China (No.61401003)the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20123401110006)the Natural Science Research Project of Anhui Education ( No. KJ2015A436)
文摘The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this paper.In the conventional MLCBFM based on Foldy-Lax multiple scattering equations,the improvement is only made in the generation of characteristic basis functions(CBFs).However,it does not provide a change in impedance matrix filling and reducing matrix calculation procedure,which is time-consuming.In reality,all the impedance and reduced matrix of each level of the MLCBFM have low-rank property and can be calculated efficiently.Therefore,ACA is used for the efficient generation of two-level CBFs and the fast calculation of reduced matrix in this study.Numerical results are given to demonstrate the accuracy and efficiency of the method.
