Multilayer Network from Multivariate Time Series for Characterizing Nonlinear Flow Behavior

2017 ◽  
Vol 27 (04) ◽  
pp. 1750059 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Shan-Shan Zhang ◽  
Wei-Dong Dang ◽  
Shan Li ◽  
Qing Cai
Keyword(s):  
Time Series ◽  
Flow Behavior ◽  
Multivariate Time Series ◽  
Bubbly Flows ◽  
Nonlinear Flow ◽  
Multilayer Network ◽  
Two Phase ◽  
Macro Scale ◽  
Flow Information ◽  
Oil Water

The exploration of two-phase flows, as a multidisciplinary subject, has drawn a great deal of attention on account of its significance. The dynamical flow behaviors underlying the transitions of oil–water bubbly flows are still elusive. We carry out oil–water two-phase flow experiments and capture multichannel flow information. Then we propose a novel methodology for inferring multilayer network from multivariate time series, which enables to fuse multichannel flow information at different frequency bands. We employ macro-scale, meso-scale and micro-scale network measures to characterize the generated multilayer networks, and the results suggest that our analysis allows uncovering the nonlinear flow behaviors underlying the transitions of oil-in-water bubbly flows.

scholarly journals Complex networks from multivariate time series for characterizing nonlinear dynamics of two-phase flow patterns

2012 ◽  
Vol 61 (12) ◽  
pp. 120510
Author(s):  
Gao Zhong-Ke ◽  
Jin Ning-De ◽  
Yang Dan ◽  
Zhai Lu-Sheng ◽  
Du Meng
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Complex Networks ◽  
Two Phase Flow ◽  
Multivariate Time Series ◽  
Flow Patterns ◽  
Phase Flow ◽  
Two Phase

Influences of Water Injection Rate and Oil Length on Oil Slug Mobilization in a Capillary Model

2013 ◽  
Author(s):  
Liming Dai ◽  
Yihe Zhang
Keyword(s):  
Flow Behavior ◽  
Water Injection ◽  
Injection Rate ◽  
Flow Time ◽  
Shape Variation ◽  
Two Phase ◽  
Capillary Model ◽  
Numerical Research ◽  
Oil Water ◽  
Pressure Magnitude

In this paper, numerical research has been investigated for oil-water two-phase flow in a capillary model by software FLUENT. The flow behavior of the oil slug and the influences of both water injection rate and oil slug length have been considered. Results indicate that numerical model performs well in simulating oil slug shape variation; meanwhile, the maximum driven pressure magnitude is proportional to the water injection rate and the oil slug length, and the flow time is inversely proportional to the water injection rate.

Multilayer Network from Multiple Entropies for Characterizing Gas-Liquid Nonlinear Flow Behavior

2020 ◽  
Vol 30 (01) ◽  
pp. 2050014 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Dong-Mei Lv ◽  
Wei-Dong Dang ◽  
Ming-Xu Liu ◽  
Xiao-Lin Hong
Keyword(s):  
Liquid Flow ◽  
Flow Behavior ◽  
Flow Patterns ◽  
Research Field ◽  
Clustering Coefficient ◽  
Nonlinear Flow ◽  
Permutation Entropy ◽  
Multilayer Network ◽  
Time Frequency ◽  
Gas Liquid Flow

Characterizing nonlinear dynamic behaviors underlying multiphase flow has attracted considerable attention from the nonlinear research field. In this paper, the authors develop a novel multiple entropy-based multilayer network (MEMN) for exploring the complex gas-liquid two-phase flow. At first, we carry out the gas-liquid flow experiments to get the multichannel measurements. Then, MEMN is constructed based on the fusion of three nonlinear entropies, namely weighted permutation entropy (WPE), wavelet packet energy entropy (WPEE), and amplitude entropy (AE). For each derived projection network of MEMN, spectral radius and global clustering coefficient are both calculated and they allow effectively uncovering the nonlinear flow behaviors in the transition of different gas-liquid flow patterns. In addition, we perform wavelet time-frequency representation for the two typical flow patterns and the results support our findings well. All these demonstrate that our MEMN framework can effectively characterize the nonlinear evolution of gas-liquid flow from the perspective of complex network theory. And this also provides a novel idea for studying nonlinear complex systems from the observed multivariate time series.

scholarly journals Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series

2017 ◽  
Vol 27 (08) ◽  
pp. 1750123 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Shan Li ◽  
Wei-Dong Dang ◽  
Yu-Xuan Yang ◽  
Younghae Do ◽  
...  
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Dynamical Behavior ◽  
Weighted Average ◽  
Nonlinear Time Series ◽  
Clustering Coefficient ◽  
Nonlinear Flow ◽  
Permutation Entropy ◽  
Wavelet Coefficients ◽  
Oil Water

Characterizing complicated behavior from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. We in this paper propose a novel wavelet multiresolution complex network (WMCN) for analyzing multivariate nonlinear time series. In particular, we first employ wavelet multiresolution decomposition to obtain the wavelet coefficients series at different resolutions for each time series. We then infer the complex network by regarding each time series as a node and determining the connections in terms of the distance among the feature vectors extracted from wavelet coefficients series. We apply our method to analyze the multivariate nonlinear time series from our oil–water two-phase flow experiment. We construct various wavelet multiresolution complex networks and use the weighted average clustering coefficient and the weighted average shortest path length to characterize the nonlinear dynamical behavior underlying the derived networks. In addition, we calculate the permutation entropy to support the findings from our network analysis. Our results suggest that our method allows characterizing the nonlinear flow behavior underlying the transitions of oil–water flows.

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