Markov transition probability-based network from time series for characterizing experimental two-phase flow

作     者：高忠科 胡沥丹 金宁德 

作者机构：School of Electrical Engineering and AutomationTianjin University 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2013年第22卷第5期

页      面：226-231页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 080704[工学-流体机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 0805[工学-材料科学与工程（可授工学、理学学位）] 0704[理学-天文学] 

基　　金：Project supported by the National Natural Science Foundation of China ( Grant Nos. 61104148, 41174109, and 50974095) the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2011ZX05020-006) the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110032120088) 

主　　题：complex network time series analysis chaotic dynamics two-phase flow pattern 

摘      要：We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.