Gas/Liquid Two-Phase Flow Pattern Identification Method Using Gramian Angular Field and Densely Connected Network

Abstract

Accurate identification of flow patterns is of great importance for industrial production. A flow pattern identification method based on Gramian angular field (GAF) and densely connected network (DenseNet) was proposed. Experimental data of gas–liquid two-phase flow in a vertical upward pipeline were collected using a resistance sensor array, and the multivariate measurement data were downscaled into univariate time series for feature reduction. Then, the data were encoded into 2-D images using the Gramian angular summation field (GASF) and Gramian angular difference field (GADF) to highlight the characteristic differences between flow patterns, and the evolutionary behavior of flow patterns was further analyzed based on the images. A DenseNet model was established, and the GAF images were used as model inputs for training and testing to achieve the flow pattern identification. The results show that the GAF images can effectively reflect the characteristics of different flow patterns; in particular, the combination of GADF and DenseNet has the best recognition effect, and the average flow pattern identification accuracy reaches 98.3%. This method provides a new perspective for distinguishing complex gas–liquid two-phase flow patterns.