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 f...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.展开更多
Flow pattern identification is an important issue in multiphase systems.Because of the gravitational component normal to the flow direction,there exists complex water-dominated countercurrent flow patterns in the incl...Flow pattern identification is an important issue in multiphase systems.Because of the gravitational component normal to the flow direction,there exists complex water-dominated countercurrent flow patterns in the inclined oil-water two-phase flow,which is difficult to be discerned objectively with traditional nonlinear analysis methods.The inclined oil-water two-phase flow is studied using complex networks,and the flow pattern complex network is constructed with the conductance fluctuating signals measured from oil-water two-phase flow experiments.Hence,a new method based on time-delay embedding and modularity is proposed to construct the network from nonlinear time series.Through detecting the community structure of the resulting network using the community-detection algorithm based on data field theory,useful and interesting results are found,which can be used to identify three inclined oil-water flow patterns.From a new perspective,the complex network theory is introduced to the study of oil-water two-phase flow,and may be a powerful tool for exploring nonlinear time series in practice.展开更多
To discuss the relationship between complexity measures extracted from gray image time series and flow pattern transition in gas-liquid two-phase flow,three complexity measures,including Lempel-Ziv complexity,fractal ...To discuss the relationship between complexity measures extracted from gray image time series and flow pattern transition in gas-liquid two-phase flow,three complexity measures,including Lempel-Ziv complexity,fractal box dimension,and Shannon information entropy were extracted from sixty flow pattern image signals of gas-liquid two-phase flow in the horizontal pipe by using digital high speed video systems.Based on the above studies,the chaos dynamic characteristics of three complexity measures in different gas superficial velocities,and the recognition capability of gas-liquid two-phase flow pattern were analyzed.The results indicated that these three complexity measures were sensitive to the flow pattern transition in gas-liquid two-phase flow.By analyzing the changes of three complexity measures with gas-liquid two-phase flow parameters,the dynamics structure inversion characteristics of gas-liquid two-phase flow could be got,which provided an efficient,supplementary diagnostic tool to reveal the flow pattern transition mechanism of gas-liquid two-phase flow and quantitatively identify flow pattern.展开更多
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)
文摘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.
文摘Flow pattern identification is an important issue in multiphase systems.Because of the gravitational component normal to the flow direction,there exists complex water-dominated countercurrent flow patterns in the inclined oil-water two-phase flow,which is difficult to be discerned objectively with traditional nonlinear analysis methods.The inclined oil-water two-phase flow is studied using complex networks,and the flow pattern complex network is constructed with the conductance fluctuating signals measured from oil-water two-phase flow experiments.Hence,a new method based on time-delay embedding and modularity is proposed to construct the network from nonlinear time series.Through detecting the community structure of the resulting network using the community-detection algorithm based on data field theory,useful and interesting results are found,which can be used to identify three inclined oil-water flow patterns.From a new perspective,the complex network theory is introduced to the study of oil-water two-phase flow,and may be a powerful tool for exploring nonlinear time series in practice.
文摘To discuss the relationship between complexity measures extracted from gray image time series and flow pattern transition in gas-liquid two-phase flow,three complexity measures,including Lempel-Ziv complexity,fractal box dimension,and Shannon information entropy were extracted from sixty flow pattern image signals of gas-liquid two-phase flow in the horizontal pipe by using digital high speed video systems.Based on the above studies,the chaos dynamic characteristics of three complexity measures in different gas superficial velocities,and the recognition capability of gas-liquid two-phase flow pattern were analyzed.The results indicated that these three complexity measures were sensitive to the flow pattern transition in gas-liquid two-phase flow.By analyzing the changes of three complexity measures with gas-liquid two-phase flow parameters,the dynamics structure inversion characteristics of gas-liquid two-phase flow could be got,which provided an efficient,supplementary diagnostic tool to reveal the flow pattern transition mechanism of gas-liquid two-phase flow and quantitatively identify flow pattern.
