A Novel Single Source Surface Integral Equation for Electromagnetic Analysis by Multilayer Embedded Objects

Abstract

This paper presents a new single-source surface integral equation (SS-SIE) to model composite penetrable objects. In the proposed formulation, the surface electric and magnetic fields on all interior boundaries are first eliminated through combining integral solutions inside each object. Then, by enforcing the surface electric fields in the original and equivalent configurations are equal to each other, an equivalent model with only the electric current density on the outermost boundaries is derived. Compared with other SIEs, like the PMCHWT formulation, all unknowns are residing on the outermost boundaries in the proposed formulation and therefore, less count of unknowns can be obtained. Finally, two numerical examples are carried out to validate the effectiveness of the proposed SS-SIE.

Towards a Unified Approach to Electromagnetic Analysis by Objects Embedded in Multilayers

“…For the original object with the lth (l ≥ 2) layer media, it is required to test Equ. (24) on the ith layer as…”

“…The approach proposed in this paper can significantly simplify the analysis and provide a unified approach to analyze the multilayer embedded objects and reduce the overall number of unknowns for the complex objects with large number of multilayer enclosed media. In [24], we reported some preliminary results upon this idea. In this paper, we greatly extended technique contents and presented comprehensive investigations upon the proposed approach.…”

Formulation of Single-Source Surface Integral Equation for Electromagnetic Analysis of Composite Penetrable Objects

“…However, for objects embedded in multilayers, those formulations suffer from efficiency issues since the overall count of unknowns increases rapidly with the count of interfaces. To solve this problem, [11]- [13] have proposed an approach based on the DSAO to model objects embedded in multilayers, in which only a single electric current density is enforced on the outermost boundarys. Therefore, significant performance improvements in terms of the overall count of unknowns, memory consumption, and matrix conditioning can be obtained.…”