两相流流型复杂网络社团结构及其统计特性  

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

作　　者：高忠科[1] 金宁德[1] 

机构地区：[1]天津大学电气与自动化工程学院,天津300072

出　　处：《物理学报》2008年第11期6909-6920,共12页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:50674070;60374041);国家高技术研究发展项目(批准号:2007AA06Z231)资助的课题~~

摘　　要：利用气液两相流电导波动信号构建了流型复杂网络.基于K均值聚类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泡状流、段塞流及混状流的三个社团,并且两个社团间联系紧密的点分别对应于相应的过渡流型.基于复杂网络理论从全新的角度探讨了两相流流型复杂网络社团结构及统计特性问题,并取得了满意的流型识别效果,与此同时,在对该网络特性进一步分析的基础上,发现了对两相流流动参数变化敏感的相关复杂网络统计量,为更好地理解两相流流型动力学特性提供了参考.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.

关 键 词：两相流流型 复杂网络 社团探寻算法 网络统计特性 

分 类 号：O359.1[理学—流体力学]