2015
DOI: 10.1098/rspa.2014.0639
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Automated detection of coherent Lagrangian vortices in two-dimensional unsteady flows

Abstract: Coherent boundaries of Lagrangian vortices in fluid flows have recently been identified as closed orbits of line fields associated with the Cauchy-Green strain tensor. Here, we develop a fully automated procedure for the detection of such closed orbits in largescale velocity datasets. We illustrate the power of our method on ocean surface velocities derived from satellite altimetry.

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Cited by 50 publications
(94 citation statements)
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“…The presence of closed orbits is linked to the existence of singularities in the CG tensor field, which is used as a means for identifying their location. 44 By virtue of their transport properties, these are also considered elliptical LCS. There are two elliptic barriers in the double-gyre system, and they are presented as green lines in Figure 2.…”
Section: A Finite-time Lyapunov Exponentsmentioning
confidence: 99%
“…The presence of closed orbits is linked to the existence of singularities in the CG tensor field, which is used as a means for identifying their location. 44 By virtue of their transport properties, these are also considered elliptical LCS. There are two elliptic barriers in the double-gyre system, and they are presented as green lines in Figure 2.…”
Section: A Finite-time Lyapunov Exponentsmentioning
confidence: 99%
“…The algorithms are developed as part of the open-source CoherentStructures.jl project. These methods in principle scale well with the size of the domain, but existing implementations related to the publications [32,29,20,38] failed to fully leverage this; we therefore had to make significant conceptual and implementation modifications.…”
Section: Introductionmentioning
confidence: 99%
“…Conceptually, our work is based on the index-theory-based methodology developed in [29], whose implementation was a mixture of methods implemented in [11,32,20]. In [38], Serra & Haller identified (i) the detection of tensor field singularities (points of repeated tensor eigenvalues) of tensor fields and (ii) the identification of their topological type as major computational bottlenecks in the implementation of [29]. Moreover, these steps required a number of parameters whose choice had-at times-unpredictable impact on the computational outcome.…”
Section: Introductionmentioning
confidence: 99%
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