The Recognition Algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention
Jinsong Zhang, Xinpeng Wei, Zhiliang Wang
Abstract
The two-phase flow in a microchannel consists of liquid-liquid and gas-liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid-liquid two-phase flows (NaAlg-Oil, GaInSn-Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid-liquid and gas-liquid two-phase flows (water-soybean oil, water-lubricating oil, and argon-water).