Flame reconstruction of hydrogen fueled-scramjet combustor based on multi-source information fusion

Using various types of data to extract different features and reconstruct flame spontaneous image, the rapid acquisition of flame propagation data and automatic identification of the combustion state in a scramjet combustion chamber in confined space can be promoted. A reconstruction model based on multi-source information fusion is proposed that can effectively reconstruct a flame self-luminous image based on the combustor wall pressure and schlieren image data. A dense convolutional neural network is used to construct and reconstruct the model and train the network based on the experimental datasets. The ground pulse combustion wind tunnel test is conducted under the condition of hydrogen fuel self-ignition using the variation rules of different injection pressures. The evolution process of the combustor wall pressure, schlieren image, and flame self-luminescence image is obtained over time and a dataset is constructed. The reconstruction accuracy of the flame spontaneous luminescence image using the schlieren image and the fusion of the schlieren image and wall pressure under different injection pressure is comprehensively analyzed. Moreover, both qualitative and quantitative results show that the prediction accuracy of flame spontaneous illumination based on multi-source information fusion well matches the experimental result, and the flame boundary, flame head region, flame area, and burning intensity are all well reconstructed. The average peak signal to noise ratio reached 31.57 dB, average structural similarity was 0.939, average mean square error was 0.979, correlation coefficient was 0.0013, and intersection over union reached 0.876.