Image identification for two-phase flow patterns based on CNN algorithms. (July 2022) - Nie, Feng; Wang, Haocheng; Song, Qinglu; Zhao, Yanxing; Shen, Jun; Gong, Maoqiong
Highlights: An automatic image classification method for two-phase flow patterns based on convolutional neural network algorithms is adopted. A large amount of image data to test and evaluate the feasibility of this method. An independent nitrogen flow condensing experiment is conducted to validate the universality of this automatic program. Abstract: Flow patterns are essential and useful to model the interfacial structures and heat transfer in gas-liquid two-phase flow. However, the current two-phase flow patterns classification methods mostly depend on direct visual observation. This study adopted a new flow pattern classification method based on convolutional neural network (CNN) algorithms to achieve an automatic and objective identification of two-phase flow patterns. A database of 696 test conditions, including 105642 condensing flow pattern images of methane and tetrafluoromethane in a horizontal circular tube, is collected as the input of the data-driven algorithms. After 80% of image data is fed to train and fit the parameters in the algorithms, the trained models with acceptable universality are obtained to identify five flow patterns: annular flow, bubbly flow, churn flow, slug flow and stratified flow. Compared with the manual classification, the proposed method can accurately predict two-phase flow patterns with a prediction accuracy of more than 90.63% and 91.45% for the test dataset and the entire database, respectively. The average accuracy for predicting all data points in the database is more than 97.56%. The results showed that using images as input, CNN algorithms can provide objective prediction with satisfactory accuracy and universality for two-phase flow pattern identification.