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Proceedings Paper

Multivariate visualization of 3D turbulent flow data
Author(s): Sheng-Wen Wang; Victoria Interrante; Ellen Longmire
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Paper Abstract

Turbulent flows play a critical role in many fields, yet our understanding of the fundamental physics of turbulence remains in its infancy. One of the long term goals in turbulence research is to develop an improved understanding of the dynamic evolution, interaction and organization of vortices in three-dimensional turbulent flow. However this task is complicated by the lack of a clear, mathematically precise definition of what a vortex is. We believe that the design of effective methods for vortex identification and segmentation in complicated turbulent flows can be facilitated by the clear, detailed visual presentation of the multiple scalar and vector quantities potentially relevant to the feature identification process. In this paper, we present several different methods aimed at facilitating the integrated understanding of a variety of local measures extracted from 3D multivariate flow data, including quantities, directions, and orientation. A key focus of our work is on the development of methods for illustrating the local relationships between scalar and vector values important to the vortex identification process such as vorticity, swirl, and velocity, along with their direction and magnitude. Our methods include the use of arrows and glyphs or 3D texture along with different color coding strategies. We demonstrate our methods on a range of data including 3D turbulent boundary flow data and time varying ring data. The variety of multi-variate visualization methods that we have developed has succeeded in supporting fluids researchers in their efforts to gain deeper insights into their data.

Paper Details

Date Published: 18 January 2010
PDF: 12 pages
Proc. SPIE 7530, Visualization and Data Analysis 2010, 75300N (18 January 2010); doi: 10.1117/12.839093
Show Author Affiliations
Sheng-Wen Wang, Univ. of Minnesota (United States)
Victoria Interrante, Univ. of Minnesota (United States)
Ellen Longmire, Univ. of Minnesota (United States)

Published in SPIE Proceedings Vol. 7530:
Visualization and Data Analysis 2010
Jinah Park; Ming C. Hao; Pak Chung Wong; Chaomei Chen, Editor(s)

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