Research on flame prediction in a scramjet combustor using a data-driven model
Chen Kong, Ziao Wang, Junlong Zhang, Xuan Wang, Kai Wang, Yunfei Li, Juntao Chang
Abstract
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.