Abstract
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.
Highlights
Cross-flow effects couple with the evolution of fundamental hypersonic Mack modes2,3 so that simulations and theoretical analysis are intolerably time-consuming.4–6 a data-driven prediction method is suitable for this complex problem.7
A neural network (NN) is one of the machine learning methods that can get the prediction by the input database
Based on the advantages of the NN in dealing with nonlinear problems, it is used in this work to predict the transition location (TL) by building models from the experimental database from the Peking University Mach 6.5 quiet wind tunnel by some algorithms, which have the ability to judge and predict
Summary
Cross-flow effects couple with the evolution of fundamental hypersonic Mack modes2,3 so that simulations and theoretical analysis are intolerably time-consuming.4–6 a data-driven prediction method is suitable for this complex problem.7 Based on the measured TL data, artificial NNs are trained to construct the mapping function between the TL and θ. With a well-trained NN, TLs can be predicted at both interpolated and extrapolated AOAs and unit Reynolds numbers Re0.
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