Flame development prediction of supersonic combustion flow based on lightweight cascaded convolutional neural network

Abstract

The flame development prediction of a scramjet combustor forecasts the combustion state and provides valuable information for active flow control. Experiments were performed on a hydrogen-fueled scramjet at different equivalence ratios in a ground pulse combustion wind tunnel with a Mach-2.5 incoming flow. Five image datasets of the flame evolution process were constructed at different predicted periods. The memory fusion cascade network (MFCN) was developed to predict flame images after a certain span using flame image sequences of the previous periods. A complete evaluation system was constructed to compare and analyze the performances of MFCN, Kongs, and ResNet16 models in multi- and long-span conditions. Experimental results show that MFCN achieves a maximum increase of 46.16% of the peak signal-to-noise ratio index, 69.14% of the structural correlation coefficient index, and 5.72% of the correlation coefficient index in the independent test set. Moreover, the volume of the model only reaches the KB level, which has the characteristics of being lightweight. MFCN outperforms other methods in terms of the prediction accuracy and maintains stable prediction results during multi- and long-span tasks.