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On the relationships between local vortex identification schemes

CHAKRABORTY, PINAKI, BALACHANDAR, S., ADRIAN, RONALD J.·2005·Journal of Fluid Mechanics

Abstract

We analyse the currently popular vortex identification criteria that are based on point-wise analysis of the velocity gradient tensor. A new measure of spiralling compactness of material orbits in vortices is introduced and using this measure a new local vortex identification criterion and requirements for a vortex core are proposed. The inter-relationships between the different criteria are explored analytically and in a few flow examples, using both zero and non-zero thresholds for the identification parameter. These inter-relationships provide a new interpretation of the various criteria in terms of the local flow kinematics. A canonical turbulent flow example is studied, and it is observed that all the criteria, given the proposed usage of threshold, result in remarkably similar looking vortical structures. A unified interpretation based on local flow kinematics is offered for when similarity or differences can be expected in the vortical structures educed using the different criteria.

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Metadata

Authors
CHAKRABORTY, P., BALACHANDAR, S., & ADRIAN, R. J.
Venue
Journal of Fluid Mechanics
Published
2005-07-05
Volume
535
Pages
189-214
ISSN
  • 0022-1120 (ISSN-L)
  • 1469-7645
OpenAlex
W1981799239
Cited by
923
References
0
FWCI
20.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: On the relationships between local vortex identification schemes. https://app.cochat.ai/papers/10.1017/s0022112005004726
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CHAKRABORTY, P., BALACHANDAR, S., & ADRIAN, R. J. (2005). On the relationships between local vortex identification schemes. Journal of Fluid Mechanics. https://doi.org/10.1017/s0022112005004726

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.