Network Measures of Mixing

Abstract

Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as 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.

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Cited by in corpus (7)

• Explicit and implicit network connectivity: Analytical formulation and application to transport processes
• Evolutionary clustering of Lagrangian trajectories in turbulent Rayleigh-Bénard convection flows
• Lagrangian coherent sets in turbulent Rayleigh-Bénard convection
• Network analysis of Reynolds number scaling in wall-bounded Lagrangian mixing
• Lagrangian studies of coherent sets and heat transport in constant heat flux-driven turbulent Rayleigh-Bénard convection
• Open-flow mixing and transfer operators
• Visibility analysis of boundary layer transition