Joint Recurrence Network-Based Dynamic Parameter Measurement of Gas–Liquid Two-Phase Flow

Gas–liquid two-phase flow is vital in industrial production. The transient and unstable nature of two-phase flow makes it a challenging task to accurately measure and analyze the flow parameters. In this article, we first design a dynamic experiment of gas–liquid two-phase flow and use the designed four-sector sensor to obtain the dynamic experimental data. Then, we construct the joint recurrence network from the collected data and calculate the average node degree for characterizing the evolution of gas–liquid two-phase flow. Finally, we propose a recurrence-network-based densely connection neural network for flow parameter measurements in two-phase flow, and in the end, the accuracy of flow pattern identification is 95.17%, and the mean absolute percentage error (MAPE) of our model is less than 2.8% and the mean squared error (mse) is less than 0.0023. At the same time, our method also offers a novel approach for industrial soft measurement models.