Physically-based feature tracking for CFD data

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...

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Detalles Bibliográficos
Autores principales:	Clyne, J., Mininni, P., Norton, A.
Formato:	JOUR
Materias:	CFD Feature tracking flow visualization time-varying data Coherent structure Engineering disciplines Feature-tracking High resolution simulations Persistent feature Repeating patterns Time-varying data Turbulent fluid flow Data visualization Flow visualization Computational fluid dynamics
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_10772626_v19_n6_p1020_Clyne
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_10772626_v19_n6_p1020_Clyne
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spelling	todo:paper_10772626_v19_n6_p1020_Clyne2023-10-03T16:03:31Z Physically-based feature tracking for CFD data Clyne, J. Mininni, P. Norton, A. CFD Feature tracking flow visualization time-varying data Coherent structure Engineering disciplines Feature-tracking High resolution simulations Persistent feature Repeating patterns Time-varying data Turbulent fluid flow Data visualization Flow visualization Computational fluid dynamics 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. We also provide insight on the relationship between the internal time stepping used in a CFD simulation, and the evolution of coherent structures, that we believe is of benefit to any feature tracking method applicable to CFD. Finally, our method is easy to implement, and computationally inexpensive to execute, making it well suited for very high-resolution simulations. © 1995-2012 IEEE. Fil:Mininni, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_10772626_v19_n6_p1020_Clyne
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	CFD Feature tracking flow visualization time-varying data Coherent structure Engineering disciplines Feature-tracking High resolution simulations Persistent feature Repeating patterns Time-varying data Turbulent fluid flow Data visualization Flow visualization Computational fluid dynamics
spellingShingle	CFD Feature tracking flow visualization time-varying data Coherent structure Engineering disciplines Feature-tracking High resolution simulations Persistent feature Repeating patterns Time-varying data Turbulent fluid flow Data visualization Flow visualization Computational fluid dynamics Clyne, J. Mininni, P. Norton, A. Physically-based feature tracking for CFD data
topic_facet	CFD Feature tracking flow visualization time-varying data Coherent structure Engineering disciplines Feature-tracking High resolution simulations Persistent feature Repeating patterns Time-varying data Turbulent fluid flow Data visualization Flow visualization Computational fluid dynamics
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. We also provide insight on the relationship between the internal time stepping used in a CFD simulation, and the evolution of coherent structures, that we believe is of benefit to any feature tracking method applicable to CFD. Finally, our method is easy to implement, and computationally inexpensive to execute, making it well suited for very high-resolution simulations. © 1995-2012 IEEE.
format	JOUR
author	Clyne, J. Mininni, P. Norton, A.
author_facet	Clyne, J. Mininni, P. Norton, A.
author_sort	Clyne, J.
title	Physically-based feature tracking for CFD 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
url	http://hdl.handle.net/20.500.12110/paper_10772626_v19_n6_p1020_Clyne
work_keys_str_mv	AT clynej physicallybasedfeaturetrackingforcfddata AT mininnip physicallybasedfeaturetrackingforcfddata AT nortona physicallybasedfeaturetrackingforcfddata
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Physically-based feature tracking for CFD data

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