Fast Hybrid Computational Technique for the Analysis of Radome Structures Using Dual Domain Decomposition

This work details a technique tailored to the analysis of complex radome structures based on the non-overlapping separation of two different domains: antenna and radome. Both domains are analyzed isolated using the method of moments with the multilevel fast multipole algorithm (MoM-MLFMA) for the antenna domain and a modified characteristic basis function method with the multilevel fast multipole algorithm approach for the radome domain. An iterative procedure is then applied to compute the effect of each domain over the complementary domain. This approach usually converges into a few iterations, yielding very good results and significant efficiency improvements with respect to other efficient approaches such as a full-wave MoM-MLFMA analysis of the full problem. A realistic test case is included, considering a radome with an embedded frequency selective structure on one of its interfaces. The results show a very good agreement considering only three iterations between domains, requiring only one-third of the CPU-time needed by the conventional approach.

Acceleration of the Multi-Level Fast Multipole Algorithm Using K-Means Clustering

The multilevel fast multipole algorithm (MLFMA) using K-means clustering to accelerate electromagnetic scattering analysis for large complex targets is presented. By replacing the regular cube grouping with the K-means clustering, the addition theorem is more accurately approximated. The convergence rate of an iterative solver is thus improved significantly. However, irregular centroid locations as a result of the K-means clustering increase the amount of explicit transfer function calculations, compared with the regular cubes. In the MLFMA, a multilevel hierarchical structure is applied to the finite multipole method (FMM) to reduce transfer function calculations. Therefore, the MLFMA is suitable for applying K-means clustering. Simulation results with both canonical and realistic targets show an improvement in the computation time of the proposed algorithm.