Abstract: The ablation phenomenon of hypersonic vehicle head model in high temperature flow field is studied. The thermal test is made in an arc wind tunnel. The temperature evolution data of the arc wind tunnel ablation model with time is obtained using the colorimetric temperature measurement method of CCD camera image. By tracking the change of the temperature field at the edge of the model, the deformation process of the model during ablation is given, and the time series of stagnation point and other edge points are extracted. Least squares method and long-term short-term memory(LSTM) network method are used to fit and predict the ablation recession data. The LSTM method mainly includes network structure design, network training, objective function setting and algorithm implementation of prediction process. Through comparison between the time series prediction and experiment of ablation recession at different positions of the ablation model, it is found that the least square method is mainly suitable for the fitting and prediction of ablation recession in the linear region; the LSTM method is not only suitable for the linear ablation recession, but also suitable for the fitting and prediction of the nonlinear change of ablation recession.

Key words: hypersonicvehicleablation, chargecoupleddevicecamera, colorimetrictemperaturemeasurement, recession, long-termshort-termmemorynetworkmodel