Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks
Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks
复制标题
复杂网络中气液两相流的流型识别和非线性动力学
DOI：
10.1103/physreve.79.066303
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发表时间：
2009-06-01
影响因子：
2.4
通讯作者：
Jin, Ningde
中科院分区：
文献类型：
--
作者：
Gao, Zhongke;Jin, Ningde
流型识别是多相系统中一个基本而重要的问题。由于气液两相流中相相互作用的复杂性，很难客观地判别其流型。本文采用复杂网络对垂直向上的气液两相流进行了系统的研究。提出了三种独特的网络构建方法，构建流态复杂网络（FPCN）、流体动力复杂网络（FDCN）和流体结构复杂网络（FSCN）三种类型的网络。通过基于k均值聚类的群落检测算法对FPCN的群落结构进行检测，得到了有用且有趣的结果，可用于识别五种垂直向上的气液两相流模式。为了研究气液两相流的动态特性，我们构建了50个不同流动条件下的FDCNs，发现幂律指数和网络信息熵对流型转换敏感，都可以表征气液两相流的非线性动力学特性。此外，我们还构建了FSCN，并演示了如何使用网络统计来揭示气液两相流的流体结构。本文从不同的角度将复杂网络理论引入到气液两相流的研究中，表明复杂网络在实践中可能是探索非线性时间序列的有力工具。
The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K-mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.