Network-based study of Lagrangian transport and mixing
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...
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ftdoajarticles:oai:doaj.org/article:606c3c7de1cb4ab1ba002d93f225f951 2023-05-15T13:42:40+02:00 Network-based study of Lagrangian transport and mixing K. Padberg-Gehle C. Schneide 2017-10-01T00:00:00Z https://doi.org/10.5194/npg-24-661-2017 https://doaj.org/article/606c3c7de1cb4ab1ba002d93f225f951 EN eng Copernicus Publications https://www.nonlin-processes-geophys.net/24/661/2017/npg-24-661-2017.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 doi:10.5194/npg-24-661-2017 1023-5809 1607-7946 https://doaj.org/article/606c3c7de1cb4ab1ba002d93f225f951 Nonlinear Processes in Geophysics, Vol 24, Pp 661-671 (2017) Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 article 2017 ftdoajarticles https://doi.org/10.5194/npg-24-661-2017 2022-12-31T12:13:38Z 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. Article in Journal/Newspaper Antarc* Antarctic Directory of Open Access Journals: DOAJ Articles Antarctic The Antarctic Nonlinear Processes in Geophysics 24 4 661 671 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 K. Padberg-Gehle C. Schneide Network-based study of Lagrangian transport and mixing |
topic_facet |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
K. Padberg-Gehle C. Schneide |
author_facet |
K. Padberg-Gehle C. Schneide |
author_sort |
K. Padberg-Gehle |
title |
Network-based study of Lagrangian transport and mixing |
title_short |
Network-based study of Lagrangian transport and mixing |
title_full |
Network-based study of Lagrangian transport and mixing |
title_fullStr |
Network-based study of Lagrangian transport and mixing |
title_full_unstemmed |
Network-based study of Lagrangian transport and mixing |
title_sort |
network-based study of lagrangian transport and mixing |
publisher |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/npg-24-661-2017 https://doaj.org/article/606c3c7de1cb4ab1ba002d93f225f951 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
Nonlinear Processes in Geophysics, Vol 24, Pp 661-671 (2017) |
op_relation |
https://www.nonlin-processes-geophys.net/24/661/2017/npg-24-661-2017.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 doi:10.5194/npg-24-661-2017 1023-5809 1607-7946 https://doaj.org/article/606c3c7de1cb4ab1ba002d93f225f951 |
op_doi |
https://doi.org/10.5194/npg-24-661-2017 |
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Nonlinear Processes in Geophysics |
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24 |
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4 |
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661 |
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671 |
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1766170990151204864 |