The pursuit of high-resolution flow fields is meaningful for the development of hypersonic technology. Flow field super-resolution (SR) based on deep learning is a novel and effective method to provide HR flow fields in a scramjet isolator. Single-path and multiple-path network models based on convolutional neural networks (CNNs) have been developed to augment the spatial resolution of the experimental supersonic flow field. The single-path model uses a simple convolutional layer and fully connected layer serial architecture, and the multiple-path model increases the branch path by adding pooling layers to achieve a fusion structure architecture. Ground experiments of flow in a supersonic isolator at various working conditions are conducted to establish an experimental dataset. The trained single-path and multiple-path CNNs are compared with the traditional interpolation method on the flow field SR reconstruction accuracy. The results demonstrated that single-path CNNs have certain learning ability, but the SR accuracy is not satisfactory; multiple-path CNNs significantly improve the accuracy of flow field SR, and the multiple-path CNN with one branch path achieves the best SR performance.

The prediction of flowfield evolution can provide valuable reference information for the development of hypersonic technology. Flowfield prediction with the introduction of deep learning techniques...

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.

Accurate and comprehensive flow field prediction is indispensable for promptly monitoring the flow state of supersonic cascades. This paper proposes a time sequence prediction architecture based on the full convolutional neural network (FCNN) to predict the future flow parameters of supersonic cascades based on the wall pressure at the previous moment. Considering the complicated spatial-temporal characteristics of the time sequence prediction of flow field structures, FCNN embeds the convolution into the long short-term memory (LSTM) and replaces the fully connected layer with a convolution in the output layer. Wind tunnel experiments with different flap rotation rates were performed to obtain the dataset required for model training and verification. For pressure-to-schlieren time sequence prediction, FCNN takes the cascade wall pressure at the previous moment as input and the future schlieren of the flow field structure captured by a high-speed camera as its output. The experimental results show that FCNN can accurately predict the position of the leading edge and that the maximum relative error is less than 4.4%. Moreover, for pressure-to-pressure time sequence prediction, the bidirectional LSTM (BiLSTM) was used to predict the wall pressure of the cascade channel. Results show that the BiLSTM can accurately capture the nonlinear characteristics of the wall pressure changing over time. Thus, the results of this study lay a solid foundation for the subsequent determination of the stability margin of flow fields.

Micro-ramps for shock train control were investigated experimentally in a Mach 1.85 supersonic isolator flow using three device heights. A mechanical flap was mounted downstream and linearly choked...