多元时间序列复杂网络流型动力学分析  

Complex networks from multivariate time series for characterizing nonlinear dynamics of two-phase flow patterns

作　　者：高忠科[1] 金宁德[1] 杨丹[1] 翟路生[1] 杜萌[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》2012年第12期131-139,共9页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:61104148;50974095;41174109);高等学校博士学科点专项科研基金(批准号:20110032120088);国家科技重大专项(批准号:2011ZX05020-006)资助的课题~~

摘　　要：针对气液两相流流动特性,利用有限元分析方法设计变曲率对壁式电导传感器.采用设计加工的传感器在多相流装置上进行气液两相流动态实验,并测得多组对应于不同流型的电导波动信号.基于测量数据,采用多元时间序列复杂网络构建算法构建对应于不同流型的复杂网络.在此基础上,对网络的社团特性进行了分析,研究发现,不同的社团结构对应于不同的流型,而社团内部网络特征可有效刻画不同流型内在动力学特性.多元时间序列复杂网络分析可为两相流流型演化动力学特性研究及流型识别提供新理论、开拓新途经.We use finite element analysis method to optimize and design a new curve half-ring conductance sensor for gas-liquid two-phase flow system. Then we carry out gas-liquid two-phase flow experiment in multiphase flow loop facility, and use the designed sensor to measure multivariate time series corresponding to different flow patterns. According to the measured signals, we construct complex networks from multivariate time series for different flow patterns by a network inference method. Through investigating the community structures of the constructed networks, we find that different communities correspond to different flow patterns and the network statistics in community can be used to effectively characterize the dynamic behavior of different flow patterns. In this regard, our method can be a powerful tool for identifying flow patterns and uncovering the nonlinear dynamics governing the evolution of different flow patterns.

关 键 词：气液两相流 复杂网络 社团结构 流体动力学 

分 类 号：O359.1[理学—流体力学]