Large-Scale Characteristic Mode Analysis With Fast Multipole Algorithms

Large-scale characteristic mode analysis (CMA) poses challenges in computational electromagnetics as it calls for efficient solutions of large dense generalized eigenvalue problems. In this paper, we consider two applications that involve large-scale CMA, and demonstrate that fast multipole algorithms (FMAs) can be easily incorporated into the implicitly restarted Arnoldi method (IRAM) for eigenanalysis after simple modifications. The first application performs CMA for large platforms made by closed perfectly conducting surfaces. Multilevel FMA (MLFMA) is embedded into a combined field integral equation-based theory of characteristic mode (TCM). The second application addresses multiscale modeling of small but geometrically complicated objects, which possess fine subwavelength structures. An augmented electric field integral equation-based TCM is formulated, and low-frequency (LF-)FMA is adopted to accelerate the required matrix-vector products.