Complex network community structure of two-phase flow pattern and its statistical characteristics

Abstract

We extract the flow pattern complex network from the measured data. 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 are connected tightly between two communities correspond to the transitional flow. In this paper, from a new perspective, we not only achieve good identification of flow patterns in gas/liquid two-phase flow based on complex network theory, but also find the characteristics of flow pattern complex network that are sensitive to the flow parameters, which provide reference to the study of dynamic properties of two-phase flow.