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

• Published in: Journal of mechanical science and technology Vol. 36; no. 3; pp. 1547 - 1556
• Main Authors: Lee, Minwoo, Yoon, Sangwoong, Kim, Juhan, Wang, Yuangang, Lee, Keeman, Park, Frank Chongwoo, Sohn, Chae Hoon
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01-03-2022; Springer Nature B.V; 대한기계학회
• Subjects: Artificial neural networks; Classification; Control; Dynamical Systems; Engineering; Equivalence ratio; Fluid flow; Image classification; Industrial and Production Engineering; Jet flow; Jet impingement; Machine learning; Mechanical Engineering; Neural networks; Original Article; Reynolds number; Thermoacoustics; Vibration; 기계공학; Impinging jet flame; Transfer learning; Thermoacoustic oscillation; Convolutional neural network; Image classification
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-022-0240-5

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.