Authors:

Ian Laudo

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About Paper:

A critical component in a multi-disciplinary design analysis and optimization (MDAO) framework for an aircraft is the mission analysis, which seeks to optimize the vehicle's flight trajectory profile. This is particularly true when evaluating hypersonic vehicles, which are highly interconnected systems that often fly unconventional, high-speed trajectories. The objective of this work is to create an open-source tool which is capable of being incorporated into pre-existing MDAO frameworks that optimizes a flight trajectory for a reusable hypersonic testbed vehicle. The proposed approach uses the conceptual Talon-P reusable hypersonic testbed vehicle for a climb-cruise-descent mission profile where the vehicle is dropped from an altitude of 9 km at a speed of Mach 0.7 and has to achieve an altitude of 26 km and a cruising speed of Mach 6. Aerodynamic models for the vehicle were built using machine learning methods and computational fluid dynamics (CFD) data to characterize the physics of the vehicle's motion. The algorithm was successfully evaluated using the optimal control package Dymos to discretize the proposed trajectory into pseudospectral points to be implicitly solved via collocation methods. These results were then passed into an optimizer built using the Python package OpenMDAO to minimize the amount of time needed to arrive at the end conditions. The next steps of this work include extending the vehicle trajectory to incorporate a powered maximum-time-to-cruise segment and an unpowered maximum-time-to-glide descent segment, as well as comparing the results to a minimum-fuel-to- climb segment. Keywords: Aerospace Engineering; Design Optimization; Hypersonics; Multidisciplinary Design Analysis and Optimization; Systems Engineering

Source:

Purdue University / 2024

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Co-authors:

Ian Laudo