Conceptual Aerothermal-Structural Design Space Exploration Using Adaptive Machine Learning
Document Type
Conference Proceeding
Publication Date
7-29-2024
Abstract
Excerpt: This study presents an active machine learning approach for exploring the conceptual design space of hypersonic air vehicles. Hypersonic vehicles endure extreme thermal loads caused by aerodynamic heating, resulting in a strong coupling between structural performance and aerothermodynamics. Therefore, it is crucial to consider aerothermal-structural interactions from the early stage of conceptual design development.
Source Publication
AIAA AVIATION FORUM AND ASCEND 2024
Recommended Citation
Beachy, A. J., Bae, H., Boston, J., Cawley, E. D., Camberos, J. A., & Grandhi, R. (2024). Conceptual Aerothermal-Structural Design Space Exploration Using Adaptive Machine Learning. AIAA AVIATION FORUM AND ASCEND 2024, Session: Metamodeling and Reduced-Order Models, AIAA 2024-4578. https://doi.org/10.2514/6.2024-4578
Comments
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Conference Session: Metamodeling and Reduced-Order Models