Network measures of mixing

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as those obtained from particle tracking experiments. Recent work in this context highlights the application of graph-based approaches, where trajectories serve as nodes and some similarity or distance measure between them is employed to build a (possibly weighted) network, which is then analyzed using spectral methods. Here, we consider the simplest case of an unweighted, undirected network and analytically relate local network measures such as node degree or clustering coefficient to flow structures. In particular, we use these local measures to divide the family of trajectories into groups of similar dynamical behavior via manifold learning methods.

OriginalspracheEnglisch
Aufsatznummer063125
ZeitschriftChaos
Jahrgang29
Ausgabenummer6
ISSN1054-1500
DOIs
PublikationsstatusErschienen - 06.2019

Zugehörige Projekte

  • Trajektorien-basierte Lagrangesche Ansätze zur Extraktion und Charakterisierung kohärenter Strukturen in turbulenter Konvektion

    Projekt: Forschung

  • European Innovative Training Network "Critical Transitions in Complex Systems"

    Projekt: Forschung

  • Trajektorien-basierte Lagrangesche Ansätze zur Extraktion und Charakterisierung kohärenter Strukturen in turbulenter Konvektion

    Projekt: Forschung

  • European Innovative Training Network "Critical Transitions in Complex Systems"

    Projekt: Forschung

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DOI