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

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 Zusammenfassung: 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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Sprache(n): eng - Englisch
 Datum: 2023-06-052023-06-05
 Publikationsstatus: Final veröffentlicht
 Seiten: 11
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
 Art des Abschluß: -

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Titel: Chaos
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 33 (6) Artikelnummer: 063108 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)