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.
We investigate the dynamic characteristics of oil-gas-water three-phase flow in terms of chaotic attractor comparison. In particular, we extract a statistic to characterize the dynamical difference in attractor probability distribution. We first take time series from Logistic chaotic system with different parameters as examples to demonstrate the effectiveness of the method. Then we use this method to investigate the experimental signals from oil-gas-water three-phase flow. The results indicate that the extracted statistic is very sensitive to the change of flow parameters and can gain a quantitatively insight into the dynamic characteristics of different flow patterns.
