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Parallel Vectors Criteria for Unsteady Flow Vortices

Raphael Fuchs, Ronald Peikert, Helwig Hauser, Filip Sadlo, Philipp Muigg

ARTICLE, IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), May, 2008

Abstract

Feature-based flow visualization is naturally dependent on feature extraction. To extract flow features, often higher-order properties of the flow data are used such as the Jacobian or curvature properties, implicitly describing the flow features in terms of their inherent flow characteristics (e.g., collinear flow and vorticity vectors). In this paper we present recent research which leads to the (not really surprising) conclusion that feature extraction algorithms need to be extended to a time-dependent analysis framework (in terms of time derivatives) when dealing with unsteady flow data. Accordingly, we present two extensions of the parallel vectors based vortex extraction criteria to the time-dependent domain and show the improvements of feature-based flow visualization in comparison to the steady versions of this extraction algorithm both in the context of a high-resolution dataset, i.e., a simulation specifically designed to evaluate our new approach, as well as for a real-world dataset from a concrete application.

Published

IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)

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BibTeX

@article{fuchs08parallel,
  title =      "Parallel Vectors Criteria for Unsteady Flow Vortices",
  author =     "Raphael Fuchs and Ronald Peikert and Helwig Hauser and Filip
               Sadlo and Philipp Muigg",
  year =       "2008",
  abstract =   "Feature-based flow visualization is naturally dependent   on
               feature extraction. To extract flow features, often
               higher-order   properties of the flow data are used such as
               the Jacobian or   curvature properties, implicitly
               describing the flow features in   terms of their inherent
               flow characteristics (e.g., collinear flow   and vorticity
               vectors). In this paper we present recent research   which
               leads to the (not really surprising) conclusion that feature

               time-dependent analysis framework (in terms   of time
               derivatives) when dealing with unsteady flow data.  
               Accordingly, we present two extensions of the parallel
               vectors based vortex   extraction criteria to the
               time-dependent domain and show the improvements of  
               feature-based flow visualization in comparison to the  
               steady versions of this extraction algorithm both in the
               context of   a high-resolution dataset, i.e., a simulation
               specifically designed   to evaluate our new approach, as
               well as for a real-world dataset   from a concrete
               application.",
  pages =      "615--626",
  month =      "May",
  journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)},
  number =     "3",
  volume =     "14",
  keywords =   "Time-Varying Data Visualization, Vortex Feature Detection",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2008/fuchs_raphael_2007_par/",


}






 Last Modified: Jean-Paul Balabanian, 2013-05-29