Abstract: More recently, the United States Department of Defense (DoD) has been actively pursuing and supporting the development of hypersonic weapons and vehicles owing to the continued threat from adversaries. The Pentagon’s FY2021 budget request for all hypersonic related research is at $3.2B – up from $2.6B in FY2020. Hypersonic vehicles fly at speeds of at least Mach 5, which is five times the speed of sound in air. These extreme operational conditions pose unique challenges in the design, manufacturing, and sustainment of these vehicles. Also, the lifetime of these defense vehicles and weapons span over decades.

Some of the major challenges in the design of hypersonic vehicles are in the areas of aerothermodynamics, propulsion, structures and materials, thermal management, and phenomena such as communication blackout and aero-optical effects. This presentation will focus on how Ansys physics-based simulation technologies can help the designers and analysts address these challenges in the context of hypersonic vehicle design. Simulation can provide a framework for validation of these designs, reduce the need for physical tests resulting in cost savings and compressed design cycle time.   

This talk presents a demonstration of digital systems engineering at a System of Systems level. The focus is on the deployment of various sensor platforms to provide tracking opportunities for various hypersonic trajectories. Requirements are generated based on high level system performance and how the overall laydown contributes to the maximum viewing time allowed for the system of systems. The talk outlines the process of generating a threat vignette and using that to develop requirements based on system performance. The laydown consists of fixed and mobile sensor platforms and an optimization study is performed to determine the best placement of mobile sensors within operational areas. In addition, a probabilistic analysis is performed to calculate the most likely position within 3 sigma around each optimized mobile sensor location. These values are used to determine threshold and goal key performance parameters that can be used in trade studies to see the effects of modifying the sensor parameters and ensuring that requirements are still being met.This talk also presents a digital framework that was used to create the workflow and integrate various simulations tools such as STK and MATLAB into the process. The framework consists of a Model Based Systems Engineering tool, No Magic’s Cameo; a integration tool and simulation execution engine, Phoenix Model Center, and performance models developed in Systems Tool Kit and MATLAB. Outputs from these studies are configuration managed in a SQL database and performance metrics can be view through a Digital Dashboard, such as Microsoft’s PowerBI tool. Finally, all simulations are ran in a virtual environment hosted in Microsoft Azure.