Identification of two-phase flow regime in the energy industry based on modified convolutional neural network

Abstract

The identification of the two-phase flow regime is the most fundamental and crucial target of fluid mechanics in the nuclear thermal-hydraulic analysis since the accuracy of fluid and heat transfer models depends on the reasonable prediction of flow regimes. The trend is quantitative identification based on the measurement of quantifiable parameters or the quantitative information of the two-phase flow images. Machine learning is currently the mainstream methodology for flow regime identification. The state-of-the-art method is using flow regime images for identification based on convolutional neural networks (CNNs). To achieve a higher classification accuracy and overcome the disadvantage of the traditional CNN (time-consuming owing to a large number of images required for training), this paper proposes a more efficient methodology for two-phase flow regime identification, which combines the advantages of CNN for image feature information extraction and the support vector machine (SVM) for classification - the CNN-SVM method. Furthermore, the traditional CNN needs a large number of images for training. Considering the limited flow regime dataset, the CNN-SVM method in this paper uses the idea of transfer learning. A small-scale flow regime dataset is used for validating the effectiveness of the proposed method. The accuracies for SVM, CNN and CNN-SVM were 93.9%, 97.2% and 98.8% respectively, which show that the proposed integrated method can realize better classification results since it combines the capability of strong feature extraction of CNN and classification techniques of SVM. The improvement is mainly reflected in the higher classification accuracy of slug flow and churn flow. Additionally, according to the results, the proposed method is also more robust.