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dc.contributor.author
Clyne, John
dc.contributor.author
Mininni, Pablo Daniel
dc.contributor.author
Norton, Alan
dc.date.available
2015-10-13T20:35:11Z
dc.date.issued
2013-04
dc.identifier.citation
Clyne, John; Mininni, Pablo Daniel; Norton, Alan; Physically based feature tracking for CFD data; IEEE Computer Society; IEEE Transactions on Visualization and Computer Graphics; 19; 6; 4-2013; 1020-1033
dc.identifier.issn
1077-2626
dc.identifier.uri
http://hdl.handle.net/11336/2503
dc.description.abstract
Numerical simulations of turbulent fluid flow in areas ranging from solar physics to aircraft design are dominated by the presence of repeating patterns known as coherent structures. These persistent features are not yet well understood, but are believed to play an important role in the dynamics of turbulent fluid motion, and are the subject of study across numerous scientific and engineering disciplines. To facilitate their investigation a variety of techniques have been devised to track the paths of these structures as they evolve through time. Heretofore all such feature tracking methods have largely ignored the physics governing the motion of these objects at the expense of error prone and often computationally expensive solutions. In this paper we present a feature path prediction method that is based on the physics of the underlying solutions to the equations of fluid motion. To the knowledge of the authors the accuracy of these predictions is superior to methods reported elsewhere. Moreover, the precision of these forecasts for many applications is sufficiently high to enable the use of only the most rudimentary and inexpensive forms of correspondence matching. Finally, our method is easy to implement, and computationally inexpensive to execute, making it well suited for very high resolution simulations.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
IEEE Computer Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CFD
dc.subject
FEATURE TRACKING
dc.subject
FLOW VISUALIZATION
dc.subject
TIME-VARYING DATA
dc.subject.classification
Física de los Fluidos y Plasma
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Physically based feature tracking for CFD data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-03-30 10:35:44.97925-03
dc.journal.volume
19
dc.journal.number
6
dc.journal.pagination
1020-1033
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Washington
dc.description.fil
Fil: Clyne, John. National Center for Atmospheric Research; Estados Unidos de América;
dc.description.fil
Fil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Física del Sur; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
dc.description.fil
Fil: Norton, Alan. National Center for Atmospheric Research; Estados Unidos de América;
dc.journal.title
IEEE Transactions on Visualization and Computer Graphics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/Xplore/defdeny.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fstamp%2Fstamp.jsp%3Ftp%3D%26arnumber%3D6269875%26userType%3Dinst&denyReason=-134&arnumber=6269875&productsMatched=null&userType=inst
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TVCG.2012.171
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