Characterizing gas–liquid two-phase flow behavior using complex network and deep learning

Abstract

Gas–liquid two-phase flow is polymorphic and unstable, and characterizing its flow behavior is a major challenge in the study of multiphase flow. We first conduct dynamic experiments on gas–liquid two-phase flow in a vertical tube and obtain multi-channel signals using a self-designed four-sector distributed conductivity sensor. In order to characterize the evolution of gas–liquid two-phase flow, we transform the obtained signals using the adaptive optimal kernel time-frequency representation and build a complex network based on the time-frequency energy distribution. As quantitative indicators, global clustering coefficients of the complex network at various sparsity levels are computed to analyze the dynamic behavior of various flow structures. The results demonstrate that the proposed approach enables effective analysis of multi-channel measurement information for revealing the evolutionary mechanisms of gas–liquid two-phase flow. Furthermore, for the purpose of flow structure recognition, we propose a temporal-spatio convolutional neural network and achieve a classification accuracy of 95.83%.

Detecting non-uniform structures in oil-in-water bubbly flow experiments

“…We employ the time-frequency spectrum as the VAE-GAN's input instead of the original fluid fluctuation signal for the following reasons. Firstly, the time-frequency spectrum is a powerful two-phase flow characterizing tool [46] [47], and the joint time-frequency representations are rich in fluid dynamic information, such as the complexity [48], the motion frequency of discrete bubbles [49], the flow pattern transition [50], and the fluctuation energy of the fluid [51]. This makes the time-frequency spectra to offer clear and efficient features of the flowing oil bubbles.…”