Publication Date

2023

Document Type

Dissertation

Committee Members

Harok Bae, Ph.D. (Advisor); Edwin Forster, Ph.D. (Committee Member); Ramana Grandhi, Ph.D. (Committee Member); Mitch Wolff, Ph.D. (Committee Member); Nathan Klingbeil, Ph.D. (Committee Member); Jose Camberos, Ph.D. (Committee Member)

Degree Name

Doctor of Philosophy (PhD)

Abstract

The design of Hypersonic Vehicles (HVs) requires meeting multiple unconventional and often conflicting design requirements in a hostile, high-energy environment. The most fundamental difference between ordinary aerospace design and hypersonic flight is that the extreme conditions of hypersonic flight require parts to perform multiple functions and be tightly integrated, resulting in significant coupled effects. Critical couplings among the disciplines of aerodynamics, structures, propulsion, and thermodynamics must be investigated in the early stages of design exploration to reduce the risk of requiring major design changes and cost overruns later. In addition, due to a lack of validated test data within the coupled high-dimensional design domains, concept design exploration of HVs poses unprecedented challenges, especially in terms of computational costs and decision-making under uncertainty. A common design exploration technique is to sample the expensive physics-based models in a design of experiments and then use the sample data to train an inexpensive metamodel. Conventional metamodels include Polynomial Chaos Expansion, kriging, and neural networks. However, many simulation evaluations are needed for the design of experiments because of the large number of independent parameters for each design and the complex responses resulting from interactions across multiple disciplines. Because each simulation is expensive, the total costs are often computationally intractable. Computational cost reduction is often achieved using Multi-Fidelity (MF) modeling and Active Learning (AL). MF models supplement High-Fidelity (HF) simulations with less accurate but inexpensive Low-Fidelity (LF) simulations. AL generates training data in an iterative process: rebuilding the metamodel after each HF sample is added, and then using the metamodel to select the next HF sample. Location-specific uncertainty information is critical for making this determination. To address the technical challenges in HV concept design exploration, this work presents a novel machine learning framework. This framework combines NN architectures which robustly integrate LF models with high, low, or unknown accuracy; an ensemble technique to estimate epistemic modeling uncertainty for active learning; and a method for rapidly training neural networks so computational modeling costs remain low. These techniques are demonstrated to enable rapid and meaningful exploration of various hypersonic vehicle design concepts.

Page Count

128

Department or Program

Ph.D. in Engineering

Year Degree Awarded

2023

ORCID ID

0000-0002-0922-804x

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