Identifying Flow Patterns in Water Pipelines Using Complex Network Theory
Publication: Journal of Hydraulic Engineering
Volume 147, Issue 6
Abstract
Air pockets trapped in water pipelines are a common phenomenon and can lead to different air-water two-phase flow patterns: stratified, blowback, plug, and bubbly flows. The two former flows contain a large amount of air and should be carefully monitored for pipeline safety, while the two latter flows have relatively low air fractions and can be regarded as normal operating states of pipelines. Hence, flow pattern identification is key to diagnosing the operating state of pipelines. In this paper, a new data analysis method based on complex network theory is proposed to identify the features of flow patterns using pressure signals. The pressure signals of different flow patterns, collected from an experimental facility, were used to characterize the nodes and edges (i.e., connections) in the complex network. The closely linked nodes with dense edges could be aggregated to form a cluster (i.e., community). An unsupervised machine learning technique is then used for community clustering in the network. The results show that the complex network constructed from pressure signals can be divided into several communities, representing different phases (i.e., air, water, or mixed phases) of the air-water flows. Therefore, the flow patterns can be identified in terms of the cluster features and topological features, which are represented by indicators including modularity, graph density, average path length, and transitivity. The impacts of two structural parameters of the complex network, i.e., window size and sliding step, are analyzed. Sliding step is shown to have a more significant impact on the flow pattern identification than window size. This study shows that the complex network approach is effective for flow pattern identification in air-water two-phase flows and could be potentially used for identification of pipeline operational states.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The study is financially support by the National Key Research and Development Program of China (No. 2017YFC0406006), the National Natural Science Foundation of China (Grant No. 51708086), and the UK Royal Society through an international collaboration project (Reference IEC\NSFC\170249). The second author is funded by the Liaoning Natural Science Foundation (2019-MS-043) and the Fundamental Research Funds for the Central Universities [DUT18RC(3)072]. Author K. Yang received financial support from the Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (No. QA201613). The experimental facility is set up at Harbin Institute of Technology, which generated the data that are used in this study.
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Journal of Hydraulic Engineering
Volume 147 • Issue 6 • June 2021
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© 2021 American Society of Civil Engineers.
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Received: Feb 5, 2020
Accepted: Dec 2, 2020
Published online: Mar 27, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 27, 2021
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