We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.
Based on the conductance fluctuation signals measured from vertical upward oil-gas-water three-phase flow experiment, time frequency representation and surrogate data method were used to investigate dynamical characteristics of oil-in-water type bubble and slug flows. The results indicate that oil-in-water type bubble flow will turn to deterministic motion with the increase of oil phase fraction f o and superficial gas velocity U sg under fixed flowrate of oil-water mixture Q mix . The dynamics of oil-in-water type slug flow becomes more complex with the increase of U sg under fixed flowrate of oil-water mixture. The change of f o leads to irregular influence on the dynamics of slug flow. These interesting findings suggest that the surrogate data method can be a faithful tool for characterizing dynamic characteristics of oil-in-water type bubble and slug flows.
