Min Hyun HAN, Soo Hyung PARK

DOI Number: XXX-YYY-ZZZ

Conference number: HiSST 2024- 0045

In the development process of a high-speed aircraft, acquiring data through wind tunnel experiments is essential. However, setting up wind tunnel test facilities, designing experiments, and conducting them incur significant costs. In this study, deep learning was applied using supersonic wind tunnel experiment data. The training dataset included Shadowgraph images and flat plate surface pressure data obtained from shock wave-boundary layer interaction experiment. A trained deep learning model was used to predict shock wave structures and surface pressure for experimental cases that are challenging to conduct in the experimental environment. These predictions were then compared and validated against Computational Fluid Dynamics (CFD) simulations. The research results confirmed that the deep learning model produced physically valid results.

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In Categories: 1.Events, High-Speed Aerodynamics and Aerothermodynamics, HiSST 2024
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