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中文摘要：
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.
英文摘要：
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.
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