Flame state recognition method of a scramjet based on PLIF image fusion features and an artificial neural network

Abstract

In this study, a pattern recognition model is proposed to differentiate the flame state of a scramjet using an artificial neural network. The flame images are obtained from a scramjet ground test utilizing planar laser-induced fluorescence (PLIF). By extracting basic features, Hu moments and Zernike moments, the preprocessed flame images are compressed to mine valuable information. In order to reduce redundant input features and improve the efficiency of model operation, the partial least squares (PLS) method is introduced for feature screening and fusion. Then, a back propagation neural network (BPNN) model for multi-flame classification is established and analyzed. Finally, the flame states are determined by comparing the probabilities of different states. In order to optimize the recognition performance, the fusion features are studied and discussed. Experimental results show that when the filtered 11-dimensional features are used as input, the average recognition rate for the four states can reach 97.4%. These results demonstrate the significant potential of integrating PLIF and advanced data analysis methods, thereby broadening their application to intricate combustion fields.