Classification of impinging jet flames using convolutional neural network with transfer learning

Abstract

Depending on the equivalence ratio and the Reynolds number, impinging jet flames exhibit several modes of thermoacoustic oscillation. In this study, we present a machine-learning-based method for classifying the regimes of thermoacoustic oscillation. We perform transfer learning to train the convolutional neural network model designed to classify flame images. We show that an accurate classification of impinging jet flames is achieved with an accuracy of 93.6 % by using just a single snapshot image. This study constitutes the first demonstration of transfer learning in classifying fluid images, opening up new possibilities for robust image-based diagnostics of various fluid and combustion systems.