Mission-Driven Optimization of Vehicle Geometry and Trajectory for Hypersonic Glide Vehicles

Abstract

Hypersonic vehicles operate in a complex regime where thermal, aerodynamic, and structural considerations constrain possible vehicle designs and trajectories, motivating multidisciplinary analysis and optimization (MDAO) of these coupled systems. Such vehicles are frequently optimized at single or multiple design point conditions, which can give poor performance at off-design conditions experienced during flight. Control co-design instead optimizes a vehicle and its trajectory together for a specified mission. We present a gradient-based control co-design framework for hypersonic glide vehicles, applied to two missions: a maximum range sprint and a maximum-range flight through spatial obstacles. The co-designed vehicles outperform a point-designed vehicle for their respective missions, flying 13% farther on the sprint mission and 5% farther on the obstacle mission than the point-design vehicle. Each converges to a distinct geometry parameterized by 31 design parameters that set its aerodynamic behavior and mass, with the optimizer trading one against the other as each mission requires. Fidelity escalation also proves beneficial: vehicles codesigned with surface-inclination and computational fluid dynamics (CFD) aerodynamics are more physically similar than vehicles co-designed for different missions, and warm-starting a CFD co-design optimization from a low-fidelity design reduces the compute time to 20%. The framework extends readily to thermal, structural, and propulsive modeling, and the gradient-based approach keeps such high-dimensional problems tractable.