Date of Award
9-2025
Document Type
Thesis
Degree Name
Master of Science in Aeronautical Engineering
Department
Department of Aeronautics and Astronautics
First Advisor
Jose A. Camberos, PhD
Abstract
Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that only 10 HF samples are required to achieve accurate reconstructions of the pressure coefficient distribution.
AFIT Designator
AFIT-ENY-MS-25-S-013
Recommended Citation
Jackman, Ethan S., "Multi-Fidelity Machine Learning Modeling for Aerodynamic Response Prediction of Aerospace Vehicles" (2025). Theses and Dissertations. 8359.
https://scholar.afit.edu/etd/8359
Comments
An embargo was observed for posting this dissertation on AFIT Scholar.
Approved for Public Release, Distribution A: Distribution Unlimited. PA Case Number 88ABW-2025-0783