Geometric Design of Hypersonic Vehicles for Optimal Mission Performance with High-Fidelity Aerodynamic Models

Abstract

Recent advances in efficient optimization algorithms and high-performance computing allow the construction of integrated design frameworks wherein the traditionally segregated disciplines such as airframe design, aerodynamics, and trajectory analysis can be coupled together in order to undertake the design and optimization of vehicles as integrated systems in a larger design space. The particular interest of this paper is a potential approach to incorporating high-fidelity aerodynamic models and trajectory optimization techniques in hypersonic vehicle designs by incrementally varying the geometric parameters of the vehicle to observe induced performance variations and optimize these parameters for specific mission profiles using data-driven optimization. First, the exigency of creating integrated design frameworks considering high-fidelity disciplinary models such as aerodynamics and trajectory modeling is justified. Then, an energy-based problem formulation for hypersonic trajectory optimization is introduced. A panel method based on the modified Newtonian flow theory and Eckert’s reference model is used to produce high-fidelity aerodynamic force and heating coefficients, based on which a pseudospectral optimal control package is used to solve for optimal trajectories. Finally, a novel, iterative, data-driven framework employing Bayesian optimization and machine learning is established to integrate these disciplinary models and successively search for the geometry that enables the optimal mission performance. Simulation results demonstrate the feasibility and performance of the developed approach.

Parametric Design Method and Lift/Drag Characteristics Analysis for a Wide-Range, Wing-Morphing Glide Vehicle

“…Viviani [7] employed the free-form deformation (FFD) method for the multidisciplinary design of re-entry vehicles, focusing on aerodynamic performance and thermal protection as the optimization objectives under typical conditions along the re-entry trajectory. Brian [8] applied multidisciplinary optimization using machine learning, targeting flight performance in multiple flow conditions along the flight trajectory. Liu and Peng [9,10] used the aerodynamic layout of the waverider configuration and used the generation parameters of the cone-guided waverider as variables to optimize the L/D and volume ratio but did not set the blunt leading edge.…”