Antenna Placement Optimization on Large, Airborne, Naval Platforms Navy SBIR FY2010.1 Sol No.: Navy SBIR FY2010.1 Topic No.: N101-022 Topic Title: Antenna Placement Optimization on Large, Airborne, Naval Platforms Proposal No.: N101-022-0077 Firm: Delcross Technologies, LLC 3015 Village Office Place Champaign, Illinois 61822 Contact: Tod Courtney Phone: (217) 363-3396 Web Site: www.delcross.com Abstract: Modern naval aircraft can be large in dimensions and may carry a large number of antennas. For many of these systems, the surface area of the platform is in the tens of thousands of square wavelengths. In this case, the use of full-wave solvers to assess the on-platform performance of an antenna or the interaction between two antennas is impractical, both in terms of computing resources required and length of execution time. The best choice is to use a high-frequency code, which provides an approximate solution but requires modest computer resources and tends to be highly parallelizable. However, even high-frequency codes can take substantial time to execute depending on platform size and complexity. During this project, we propose to investigate multiple hardware-based parallelization strategies for our high-frequency computational electromagnetics code. The parallelization strategies will target different types of commercially available parallel processing hardware, including computer clusters and graphics processors. Benefits: While high-frequency solvers are computationally efficient methods, there are many scenarios when run times can be longer than users would prefer and significant decreases in run time would be beneficial. The parallelization strategies we propose for this project will benefit all users of the software by directly reducing runtimes, or when desirable, improving simulation fidelity by making more accurate simulations computationally feasible. The Navy, who use high-frequency tools to simulate multiple configurations of antennas on electrically large, complex platforms, will directly benefit from the parallelization speedups in the software through the increase in productivity, experiment processing throughput, and reduction in idle time. The improved computational performance will increase the usability and performance of the software, making it more attractive to commercial customers and increasing their productivity when using it. Return