Improved Generalized Single-Source Tangential Equivalence Principle Algorithm with Contact-Region Modeling Method for Array Structures

Yao Han,Hanru Shao,Jianfeng Dong

doi:10.1155/2018/9875041

Abstract

An improved generalized single-source tangential equivalence principle algorithm (GSST-EPA) is proposed for analyzing array structures with connected elements. In order to use the advantages of GSST-EPA, the connected array elements are decomposed and computed by a contact-region modeling (CRM) method, which makes that each element has the same meshes. The unknowns of elements can be transferred onto the equivalence surfaces by GSST-EPA. The scattering matrix in GSST-EPA needs to be solved and stored only once due to the same meshes for each element. The shift invariant of translation matrices is also used to reduce the computation of near-field interaction. Furthermore, the multilevel fast multipole algorithm (MLFMA) is used to accelerate the matrix-vector multiplication in the GSST-EPA. Numerical results are shown to demonstrate the accuracy and efficiency of the proposed method.

Highlights

Surface integral equation (SIE) methods have been widely used in the simulation of complex electromagnetic phenomena in practical engineering
It can be seen that the result of contact-region modeling (CRM) agrees very well with Mie series solution, which proves that the decomposition does not change the physics of the original problem
The array structures are decomposed into subdomains by CRM

Summary

Introduction

Surface integral equation (SIE) methods have been widely used in the simulation of complex electromagnetic phenomena in practical engineering. For many multiscale electromagnetic problems, the dimension of the matrix is large and the matrix is ill-conditioned, which are still the challenges for the fast solvers. It is desirable for more efficient and robust SIE solutions to deal with those challenges. The DD methods rely on decomposing the problem into several smaller subdomains and solving each subdomain independently. In this manner, the dimension of the matrix can be reduced and the condition number of the matrix can be improved

Methods

Results

Conclusion