Scalable Electromagnetic Simulation Environment

• Published in: Computer modeling in engineering & sciences 2004, Vol.5 (5), p.443-453
• Main Authors: Namburu, Raju R, Mark, Eric R, Clarke, Jerry A
• Format: Article
• Language: English
• Subjects: Acoustics; Armored vehicles; Broadband; Combat vehicles; Computational electromagnetics; Computer simulation; Distributed memory; Finite difference time domain method; Grid generation (mathematics); Military applications; Military vehicles; Radar cross sections; Simulation; Software; Survivability; Target recognition; Time domain analysis; Vehicles; Visualization; Wideband communications
• ISSN: 1526-1492; 1526-1506
• DOI: 10.3970/cmes.2004.005.443

Computational electromagnetic (CEM) simulations of full-range military vehicles play a critical role in enhancing the survivability and target recognition of combat systems. Modeling of full-range military systems subjected to high frequencies may involve generating large-scale meshes, solving equations, visualization, and analysis of results in the range of billions of unknowns or grid points. Hence, the overall objective of this research is to develop and demonstrate a scalable CEM software environment to address accurate prediction of radar cross sections (RCS) for full- range armored vehicles with realistic material treatments and complex geometric configurations. A software environment consisting of scalable preprocessing, postprocessing, and an accurate CEM algorithmic approach is needed to achieve a significant reduction in overall simulation time for practical military applications. In addition to RCS, this high-fidelity scalable software environment can be easily extended to address wideband communications applications. This paper presents a scalable computational environment or framework for large-scale computational electromagnetics and acoustics (CEA) applications consisting of (a) scalable grid generation based on implicit surfaces and voxel methods, (b) scalable finite difference time domain method, (c) eXtensible Data Model and Format, and (d) parallel visualization utilizing a network distributed global memory approach. Two different applications are presented to illustrate the capabilities of the proposed approach. The first application demonstrates the scalability and validity of the proposed CEM software environment. The second application demonstrates the capability of the proposed approach to model and analyze a very large-scale application, namely, a full-scale combat vehicle simulation consisting of 2.56 billion cells.