倾斜油水两相流复杂网络社团结构探寻

Complex network community structure detection in inclined oil-water two-phase flow

Flow pattern identification is an important issue in multiphase systems.Because of the gravitational component normal to the flow direction,there exists complex water-dominated countercurrent flow patterns in the inclined oil-water two-phase flow,which is difficult to be discerned objectively with traditional nonlinear analysis methods.The inclined oil-water two-phase flow is studied using complex networks,and the flow pattern complex network is constructed with the conductance fluctuating signals measured from oil-water two-phase flow experiments.Hence,a new method based on time-delay embedding and modularity is proposed to construct the network from nonlinear time series.Through detecting the community structure of the resulting network using the community-detection algorithm based on data field theory,useful and interesting results are found,which can be used to identify three inclined oil-water flow patterns.From a new perspective,the complex network theory is introduced to the study of oil-water two-phase flow,and may be a powerful tool for exploring nonlinear time series in practice.