Physically based feature tracking for CFD data

Autores
Clyne, John; Mininni, Pablo Daniel; Norton, Alan
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Clyne, John. National Center for Atmospheric Research; Estados Unidos de América;
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
Fil: Norton, Alan. National Center for Atmospheric Research; Estados Unidos de América;
Materia
CFD
FEATURE TRACKING
FLOW VISUALIZATION
TIME-VARYING DATA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/2503

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spelling Physically based feature tracking for CFD dataClyne, JohnMininni, Pablo DanielNorton, AlanCFDFEATURE TRACKINGFLOW VISUALIZATIONTIME-VARYING DATAhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Numerical 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.Fil: Clyne, John. National Center for Atmospheric Research; Estados Unidos de América;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; ArgentinaFil: Norton, Alan. National Center for Atmospheric Research; Estados Unidos de América;IEEE Computer Society2013-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/2503Clyne, 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; 1020-10331077-2626enginfo: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=instinfo:eu-repo/semantics/altIdentifier/doi/10.1109/TVCG.2012.171info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:37:02Zoai:ri.conicet.gov.ar:11336/2503instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:37:02.36CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Physically based feature tracking for CFD data
title Physically based feature tracking for CFD data
spellingShingle Physically based feature tracking for CFD data
Clyne, John
CFD
FEATURE TRACKING
FLOW VISUALIZATION
TIME-VARYING DATA
title_short Physically based feature tracking for CFD data
title_full Physically based feature tracking for CFD data
title_fullStr Physically based feature tracking for CFD data
title_full_unstemmed Physically based feature tracking for CFD data
title_sort Physically based feature tracking for CFD data
dc.creator.none.fl_str_mv Clyne, John
Mininni, Pablo Daniel
Norton, Alan
author Clyne, John
author_facet Clyne, John
Mininni, Pablo Daniel
Norton, Alan
author_role author
author2 Mininni, Pablo Daniel
Norton, Alan
author2_role author
author
dc.subject.none.fl_str_mv CFD
FEATURE TRACKING
FLOW VISUALIZATION
TIME-VARYING DATA
topic CFD
FEATURE TRACKING
FLOW VISUALIZATION
TIME-VARYING DATA
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Clyne, John. National Center for Atmospheric Research; Estados Unidos de América;
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
Fil: Norton, Alan. National Center for Atmospheric Research; Estados Unidos de América;
description 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.
publishDate 2013
dc.date.none.fl_str_mv 2013-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/2503
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
1077-2626
url http://hdl.handle.net/11336/2503
identifier_str_mv 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
1077-2626
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 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
info:eu-repo/semantics/altIdentifier/doi/10.1109/TVCG.2012.171
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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