Xuan Wang

The rapid and accurate prediction of the flow field during supersonic isolator operation is crucial. Deep learning-based pressure monitoring during operation is an effective method for flow field prediction. A supersonic isolator flow field dataset was produced for a ground-based experiment with a variable incoming Mach number and back pressure. An approach for predicting the future flow field based on isolator pressure monitoring was proposed. A flow field prediction model incorporating long short-term memory, temporal convolutional network, and convolutional block attention module structures has been proposed. The performance of the proposed model was analyzed and compared with those of other time-series neural networks for flow field prediction. The location of the shock train leading edge was introduced as a priori information to enhance the model prediction performance. The impact of the weights associated with the a priori information in network training on the performance of the flow field prediction model was analyzed and discussed. This study presents an optimization scheme for neural network flow field prediction models specifically tailored for the supersonic isolator flow field prediction problem.

Flame prediction using deep learning technology could promote the research and development of flame propagation in scramjet combustors. A data-driven prediction model is proposed to effectively predict a future flame based on the flame sequence at the previous moments. A convolutional neural network is used to construct the prediction model, and the network training is performed using an experimental dataset. Ground experiments are conducted in a scramjet combustor using different equivalence ratio variation laws, and the flame evolution in the experiments is recorded and processed into a dataset. The flame prediction accuracy of the proposed model under different equivalence ratio variation laws is analyzed in detail. Moreover, both subjective and objective analysis results show that the flame prediction well agrees with the experimental result, and the flame boundary and area are accurately predicted to a certain extent. The influence of the prediction span on the flame prediction accuracy is also discussed.