network-based study of lagrangian transport and mixing
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ID: 228763
2017
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Abstract
Transport and mixing processes in fluid flows are crucially influenced by coherent structures and the characterization of these
Lagrangian objects is a topic of intense current research. While established
mathematical approaches such as variational methods or transfer-operator-based
schemes require full knowledge of the flow field or at least high-resolution
trajectory data, this information may not
be available in applications. Recently, different computational methods have been proposed to identify coherent behavior in flows
directly from Lagrangian trajectory data, that is, numerical or measured time
series of particle positions in a fluid flow. In this
context, spatio-temporal clustering algorithms have been proven to be very effective for the extraction of coherent sets from sparse
and possibly incomplete trajectory data. Inspired by these recent approaches, we consider an unweighted, undirected network, where
Lagrangian particle trajectories serve as network nodes. A link is established between two nodes if the respective trajectories come
close to each other at least once in the course of time. Classical graph concepts are then employed to analyze the resulting
network. In particular, local network measures such as the node degree, the average degree of neighboring nodes, and the clustering
coefficient serve as indicators of highly mixing regions, whereas spectral graph partitioning schemes allow us to extract coherent
sets. The proposed methodology is very fast to run and we demonstrate its applicability in two geophysical flows – the Bickley jet as
well as the Antarctic stratospheric polar vortex.
| Reference Key |
padberg-gehle2017nonlinearnetwork-based
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|---|---|
| Authors | ;K. Padberg-Gehle;C. Schneide |
| Journal | BMC research notes |
| Year | 2017 |
| DOI |
10.5194/npg-24-661-2017
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| URL | |
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