Prediction and Optimization Framework for Assessing Aerothermal Performance of Hypersonic Reentry Vehicles

Identification and characterization of the maximum and integrated aerothermal loads drive the thermal management and thermal protection systems in the design of hypersonic vehicles. External vehicle geometry, reentry or mission profile, and attitude control can impart important changes to unsteady thermal loading. The accurate characterization of maximum and time-integrated thermal load represents a multiscale problem with strong multiphysics coupling. The present work proposes a low-fidelity optimization framework to assist in the initial design considerations of high-speed reentry capsules or vehicles. Using a low-fidelity modeling approach, we propose the use of models for reentry trajectory simulation, hypersonic shock structure formation, spatiotemporal aerothermal load prediction, and conjugate heat transfer to the vehicle. These effects are integrated into the open-source multifidelity framework Stanford University Aerospace Vehicle Environment (SUAVE), which enables vehicle design and trajectory optimization. This work first evaluates the accuracy of the various aerothermal modeling assumptions for both sharp and blunt geometries, followed by the application of the framework to a realistic reentry case and the optimization of its geometry and trajectory in order to minimize the peak surface temperature on the vehicle.