Stage-Wise Densely Connected Network for Parameter Measurement of Two-Phase Flows

Abstract

Gas-liquid two-phase flow is attracting increasing attention in industrial areas. Precise measurement of total rate and void fraction, key parameters in two-phase flows, can significantly improve the industrial efficiency, which is still a challenging issue due to their complex characteristics. To cope with this problem, we design a Stage-wise Densely Connected Network (SDCN) to simultaneously measure the total rate and void fraction. First, we conduct series of vertical upward gas-liquid two-phase flow experiments to collect signals from the four-sector conductance sensor. Then, we combine the stage-wise strategy, dense connections and decomposed convolution kernels to construct the SDCN. Using the stage-wise strategy, we introduce the flow structures to improve the accuracy of the measurement. During this process, dense connections between blocks and decomposed convolution kernels are conducted to better extract the features. Finally, we compare the SDCN and its variations to demonstrate the importance of these improvements. We also show some other competitive methods for parameter measurement. The results suggest that our proposed SDCN could accurately measure both the total rate and void fraction of gas-liquid two-phase flow, which provides a new solution in parameter measurement of two-phase flows.