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.

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The effects of an alternative permeable material on the hypersonic boundary layer transition are investigated. The new permeable material is shown to be effective in delaying the transition, although the second mode grows faster on the material surface. Experiments are conducted on a flared cone using Rayleigh-scattering flow visualization, fast-response pressure sensors, and infrared thermography. On the permeable wall, the second mode appears earlier and persists over a longer distance along the flow direction. By applying bicoherence analysis, it is determined that the second mode decays more slowly on the permeable wall due to weaker nonlinear interactions.

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