Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow
Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow
复制标题
用于表征两相流非线性动态行为的多元加权复杂网络分析
DOI：
10.1016/j.expthermflusci.2014.09.008
复制
发表时间：
2015-01-01
影响因子：
3.2
通讯作者：
Jin, Ning-De
中科院分区：
文献类型：
--
作者：
Gao, Zhong-Ke;Fang, Peng-Cheng;Jin, Ning-De
气液两相流的非线性动力学行为是一个具有重要意义的当代挑战性问题。本文首先系统地进行了小管径气液两相流实验，测量了不同流型下的局部流动信息。然后，我们提出了一个模态转换为基础的网络映射到一个有向加权复杂网络的实验多元测量。特别是，我们从不同的流动条件下得到的多元复杂网络，并证明所生成的网络对应于不同的流动模式表现出不同的拓扑结构。对于每一个生成的网络，我们利用加权聚类系数和接近中心性定量探测与动态流行为相关的网络拓扑特性。结果表明，我们的多变量复杂网络分析可以定量地揭示不同的流动模式的过渡，并产生深入的洞察气液流动的非线性动力学行为。(C)2014 Elsevier Inc. All rights reserved.
Charactering nonlinear dynamic behavior in gas-liquid two-phase flow is a contemporary and challenging problem of significant importance. We in this paper first systematically carry out gas-liquid two-phase flow experiments in a small diameter pipe for measuring local flow information from different flow patterns. Then, we propose a modality transition-based network for mapping the experimental multivariate measurements into a directed weighted complex network. In particular, we derive multivariate complex networks from different flow conditions and demonstrate that the generated networks corresponding to different flow patterns exhibit distinct topological structures. For each generated network, we exploit weighted clustering coefficient and closeness centrality to quantitatively probe the network topological properties associated with dynamic flow behavior. The results suggest that our multivariate complex network analysis allows quantitatively uncovering the transitions of distinct flow patterns and yields deep insights into the nonlinear dynamic behavior underlying gas-liquid flows. (C) 2014 Elsevier Inc. All rights reserved.