Identification of Flow Pattern in Two-Phase Flow Based on Complex Network Theory

We construct the flow pattern complex network from the conductance fluctuating signals. After detecting the community structure of the network through the community detection algorithm which is based on k-means clustering, we find that there are three communities in the network, which correspond to the bubble flow, slug flow and churn flow respectively, and the nodes of the network that connect tightly between two communities corresponding to the transitional flow. In this paper, from a new perspective, we achieve good identification of flow pattern in gas/liquid two-phase flow based on complex network theory, which provide reference to study the dynamic character of two-phase flow.