Abstract:

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.

Key words:

倾斜油水两相流, 复杂网络, 流型, 社团结构

摘要： 提出一种基于延迟嵌入和模块度的复杂网络构建方法，并利用倾斜油水两相流电导波动信号构建了流型复杂网络。基于数据场理论的社团探寻算法对该网络的社团结构进行了分析，发现该网络存在分别对应于D O/W PS流型, D O/W CT流型和过渡流型的3个社团。基于复杂网络理论从全新的角度探讨了两相流流型辨识问题，并指出复杂网络是非线性时间序列分析的一个有效工具。

关键词:

倾斜油水两相流, 复杂网络, 流型, 社团结构