Robust CNN-based flow pattern identification for horizontal gas-liquid pipe flow using flow-induced vibration

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Determination of flow patterns is crucial for the prediction of unsteady flow parameters, which is important for multiphase flow pipeline integrity management. Thus far, various methods have been put forward, however, their robustness has yet to be thoroughly tested across varying environments. Despite recent progress in flow-induced vibration (FIV) of multiphase flows, variability due to measurement approach and pipeline conditions remains an under-investigated area. This paper proposes a non-intrusive, robust convolutional neural network (CNN) flow pattern identification method based on FIV analysis. FIV for horizontal gas–liquid pipe flow under various flow patterns is investigated experimentally by simultaneously analyzing acquired FIV signals and high-speed video. A range of pipeline scenarios is thoroughly studied, encompassing variations in flow conditions, measurement axes, pipe material, and installation conditions. Both Hilbert-Huang Transform (HHT) and Short Time Fourier Transform (STFT) are used to investigate the characteristics of FIV. The results show distinct flow patterns with the FIV signal varying with measurement axes and pipe conditions. Three processes are applied to increase identification robustness: HHT images to eliminate variability due to structure signatures and flow conditions; Ostu’s threshold to eliminate image magnitude difference; and data augmentation, applied to both time signals and images, to increase database diversity. From these, the morphological features associated with flow patterns are extracted. Pattern identification results using a neural network architecture trained by GoogLeNet show over 95% accuracy. To test the generalizability of the proposed flow pattern identification method, the trained method is employed to make predictions in various pipeline scenarios. The results show that the trained model attains an accuracy of over 90% when predicting the flow pattern using data from different axes, and an accuracy above 80% when predicting with different pipe material and installation conditions. During the model training phase, the integration of multiple databases or the implementation of transfer learning can result in a substantial improvement in accuracy, reaching a respectable level of at least 90%.