Research on the identification of the two-phase flow pattern of gas-liquid in a vertical rising tube based on BP neural networks

Abstract

Research on the identification of the two-phase flow pattern of gas-liquid in a vertical rising pipe is of great significance for improving the production capacity and production efficiency of the petrochemical industry. In order to address the problem of the accuracy of the identification of the two-phase flow pattern of gas-liquid, this paper proposes a method for identifying the two-phase flow pattern of gas-liquid in a vertical rising pipe based on BP neural networks. In the study, the Fluent software was used to numerically simulate different two-phase flow velocities. The pipes were all constructed as vertical rising pipes with an inner diameter of 20 mm and a length of 2000 mm. Three flow pattern cloud diagrams and their related data were obtained for bubble flow, elastic flow, and annular flow. The gas content of the three flow types was used to collect data to form a database. The BP neural network was used to classify and identify the three flow patterns, but the result was only 90.73%. We again used the Adam algorithm to optimise the BP neural network and regularise it, and the flow pattern recognition result reached 96.68%, which was a better recognition