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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/2503
Ver los metadatos del registro completo
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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 |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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