A machine learning method for transition prediction in hypersonic flows over a cone with angles of attack

The prediction of the transition location (TL) in three-dimensional (3D) hypersonic boundary layers is of great importance in hypersonic engineering. In the present work, a method using machine learning techniques is presented for the prediction of TLs based on experiment data over a Mach 6.5 inclin...

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Bibliographic Details
Published in:AIP advances 2022-02, Vol.12 (2), p.025116-025116-5
Main Authors: Meng, Deying, Shi, Mingtao, Shi, Yipeng, Zhu, Yiding
Format: Article
Language:English
Subjects:
Angle of attack
Boundary layer transition
Extrapolation
Fluid flow
Hypersonic flow
Machine learning
Neural networks
Reynolds number
Three dimensional boundary layer
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Summary:The prediction of the transition location (TL) in three-dimensional (3D) hypersonic boundary layers is of great importance in hypersonic engineering. In the present work, a method using machine learning techniques is presented for the prediction of TLs based on experiment data over a Mach 6.5 inclined cone. A mapping function is directly constructed between TLs and the circumferential angle θ by neural networks (NNs). The results show that the present NN predicts well for both interpolations of both the angle of attack (AOA) and unit Reynolds number Re0 and extrapolation of only Re0 whereas errors increase for the extrapolation of a higher AOA. This work sheds new light on the fast prediction of TLs in hypersonic complex 3D boundary layers.
ISSN:2158-3226
2158-3226
DOI:10.1063/5.0077734