Recurrence Network Analysis Of Experimental Signals For Uncovering Dynamic Transitions Of Two-phase Flow

Two-phase flow, as a complex nonlinear system, widely exists in many industrialapplications. But so far, there is no satisfactory understanding of the underlyingdynamics by means of traditional theoretical analysis method. On one hand, we needto develop effective sensors according to distinct flow conditions to obtainobservation signals which contain two-phase flow information. On the other hand, weneed to develop and enrich time series analysis methods to gain deep insight intotwo-phase flow dynamic transitions.Complex networks, which provide us with a new viewpoint and an effective toolfor understanding a complex system from the relations between the elements in aglobal way, not only may be a powerful tool for revealing information embedded intime series but also can be used for studying nonlinear dynamic systems that can notbe perfectly described by theoretical model. By discussing different networkconstruction methods, we focus on phase space recurrence network and furtherdevelop this theory. The results indicate that the recurrence network could be apowerful tool for the dynamic characterization of two-phase flows.First, we apply the analytical framework of recurrence network to the simulationof low-dimensional chaotic maps and high-dimensional continuous chaotic systems.The results indicate that local clustering coefficient of recurrence network is feasibleto characterize chaotic dynamics of nonlinear time series. Then, we constructrecurrence networks based on the observation signals of vertical upward gas-liquidtwo-phase flow and vertical upward oil-water two-phase flow. We calculate localclustering coefficient of the resulting networks and find that there is good consistencybetween the distribution of clustering coefficient and the transition of flow patterns.Next, we construct multivariate recurrence networks based on the observationsignals of horizontal oil-water two-phase flow. Interestingly, the constructed networkexhibits the topological structure of a “network of networks”, which is a perfectfusion of the observation signals derived by the distributed array conductance sensor.By investigating the cross-clustering coefficients of multivariate recurrence network,we find that these parameters are very sensitive to transitions among different flowpatterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. Finally, aiming to investigate the formation and transitionof horizontal oil-water segregated flow (Stratified flow-ST and Stratified flow withmixing at interface-ST&MI), we analyze the cross-transitivity for each constructednetwork. We find that the cross-transitivity could reflect the transform of local flowconditions and allows quantitatively uncovering the flow behavior when the stratifiedflow evolves from a stable state (ST) to an unstable one (ST&MI).