Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

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

Urheber*innen

Du,  Meng
External Organizations;

Wei,  Jie
External Organizations;

Li ,  Meng-Yu
External Organizations;

Gao,  Zhong-ke
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)

29107oa.pdf
(Verlagsversion), 8MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_29107
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.