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

doi:10.1088/1674-1056/22/5/050507

中国物理B ›› 2013, Vol. 22 ›› Issue (5): 50507-050507.doi: 10.1088/1674-1056/22/5/050507

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

高忠科, 胡沥丹, 金宁德

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

收稿日期:2012-10-20 修回日期:2012-11-14 出版日期:2013-04-01 发布日期:2013-04-01
基金资助:
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), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110032120088).

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

Gao Zhong-Ke (高忠科), Hu Li-Dan (胡沥丹), Jin Ning-De (金宁德)

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

Received:2012-10-20 Revised:2012-11-14 Online:2013-04-01 Published:2013-04-01
Contact: Jin Ning-De E-mail:ndjin@tju.edu.cn
Supported by:
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), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110032120088).

摘要/Abstract

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 quantitatively characterizing the dynamic flow behavior in the transitions among different gas-liquid flow patterns.

关键词: complex network, time series analysis, chaotic dynamics, two-phase flow pattern

Abstract: 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 quantitatively characterizing the dynamic flow behavior in the transitions among different gas-liquid flow patterns.

Key words: complex network, time series analysis, chaotic dynamics, two-phase flow pattern

中图分类号: (Time series analysis)

05.45.Tp

05.45.-a (Nonlinear dynamics and chaos)

高忠科, 胡沥丹, 金宁德. Markov transition probability-based network from time series for characterizing experimental two-phase flow[J]. 中国物理B, 2013, 22(5): 50507-050507.

Gao Zhong-Ke (高忠科), Hu Li-Dan (胡沥丹), Jin Ning-De (金宁德). Markov transition probability-based network from time series for characterizing experimental two-phase flow[J]. Chin. Phys. B, 2013, 22(5): 50507-050507.

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

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

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