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arxiv: 1702.02365 · v2 · pith:Q2LFVZIDnew · submitted 2017-02-08 · ⚛️ physics.flu-dyn · nlin.CD

Lagrangian Flow Network approach to an open flow model

classification ⚛️ physics.flu-dyn nlin.CD
keywords flownetworklocationopensaddlechaoticentropyfinite-time
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Concepts and tools from network theory, the so-called Lagrangian Flow Network framework, have been successfully used to obtain a coarse-grained description of transport by closed fluid flows. Here we explore the application of this methodology to open chaotic flows, and check it with numerical results for a model open flow, namely a jet with a localized wave perturbation. We find that network nodes with high values of out-degree and of finite-time entropy in the forward-in-time direction identify the location of the chaotic saddle and its stable manifold, whereas nodes with high in-degree and backwards finite-time entropy highlight the location of the saddle and its unstable manifold. The cyclic clustering coefficient, associated to the presence of periodic orbits, takes non-vanishing values at the location of the saddle itself.

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