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  Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas-liquid two-phase flow

Du, M., Wei, J., Li, M.-Y., Gao, Z.-k., Kurths, J. (2023): Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas-liquid two-phase flow. - Chaos, 33, 6, 063108.
https://doi.org/10.1063/5.0146259

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 Creators:
Du, Meng 1, Author
Wei, Jie1, Author
Li , Meng-Yu 1, Author
Gao, Zhong-ke 1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The complex phase interactions of the two-phase flow are a key factor in understanding the flow pattern evolutional mechanisms, yet these complex flow behaviors have not been well understood. In this paper, we employ a series of gas–liquid two-phase flow multivariate fluctuation signals as observations and propose a novel interconnected ordinal pattern network to investigate the spatial coupling behaviors of the gas–liquid two-phase flow patterns. In addition, we use two network indices, which are the global subnetwork mutual information (⁠ ⁠) and the global subnetwork clustering coefficient (⁠ ⁠), to quantitatively measure the spatial coupling strength of different gas–liquid flow patterns. The gas–liquid two-phase flow pattern evolutionary behaviors are further characterized by calculating the two proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a novel tool for a deeper understanding of the evolutional mechanisms of the multi-phase flow system, and it can also be used to investigate the coupling behaviors of other complex systems with multiple observations.

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Language(s): eng - English
 Dates: 2023-06-052023-06-05
 Publication Status: Finally published
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0146259
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Energy
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 33 (6) Sequence Number: 063108 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)