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...

Descripción completa

Detalles Bibliográficos
Autor principal: Mininni, Pablo Daniel
Publicado: 2013
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:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10772626_v19_n6_p1020_Clyne
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 paper:paper_10772626_v19_n6_p1020_Clyne
record_format dspace
spelling paper:paper_10772626_v19_n6_p1020_Clyne2023-06-08T16:05:27Z Physically-based feature tracking for CFD data Mininni, Pablo Daniel 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. 2013 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10772626_v19_n6_p1020_Clyne 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
Mininni, Pablo Daniel
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.
author Mininni, Pablo Daniel
author_facet Mininni, Pablo Daniel
author_sort Mininni, Pablo Daniel
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
publishDate 2013
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10772626_v19_n6_p1020_Clyne
http://hdl.handle.net/20.500.12110/paper_10772626_v19_n6_p1020_Clyne
work_keys_str_mv AT mininnipablodaniel physicallybasedfeaturetrackingforcfddata
_version_ 1768544238816985088

Ejemplares similares