The Community for Technology Leaders
RSS Icon
Issue No.04 - July-Aug. (2012 vol.32)
pp: 46-54
Wesley Kendall , University of Tennessee, Knoxville
Jian Huang , University of Tennessee, Knoxville
Tom Peterka , Agronne National Laboratory
Interactive exploration of flow features in large-scale 3D unsteady-flow data is one of the most challenging visualization problems today. To comprehensively explore the complex feature spaces in these datasets, a proposed system employs a scalable framework for investigating a multitude of characteristics from traced field lines. This capability supports the examination of various neighborhood-based geometric attributes in concert with other scalar quantities. Such an analysis wasn't previously possible because of the large computational overhead and I/O requirements. The system integrates visual analytics methods by letting users procedurally and interactively describe and extract high-level flow features. An exploration of various phenomena in a large global ocean-modeling simulation demonstrates the approach's generality and expressiveness as well as its efficacy.
Feature extraction, Geometry, Large-scale systems, Visualization, Three dimensional displays, Visual analytics,parallel I/O, Feature extraction, Geometry, Large-scale systems, Visualization, Three dimensional displays, Visual analytics, large-scale data analysis, Feature extraction, Geometry, Tornadoes, Visualization, Pipelines, Ocean temperature, computer graphics, geometric flow analysis, feature extraction, particle tracing
Wesley Kendall, Jian Huang, Tom Peterka, "Geometric Quantification of Features in Large Flow Fields", IEEE Computer Graphics and Applications, vol.32, no. 4, pp. 46-54, July-Aug. 2012, doi:10.1109/MCG.2012.49
1. K. Shi et al., “Path Line Attributes: An Information Visualization Approach to Analyzing the Dynamic Behavior of 3D Time-Dependent Flow Fields,” Topology-Based Methods in Visualization II, Springer, 2009, pp. 75–88.
2. T. Peterka et al., “A Study of Parallel Particle Tracing for Steady-State and Time-Varying Flow Fields,” Proc. IEEE Int'l Parallel and Distributed Processing Symp. (IPDPS 11), IEEE, 2011, pp. 577–588.
3. W. Kendall et al., “Terascale Data Organization for Discovering Multivariate Climatic Trends,” Proc. 23rd Int'l Conf. Supercomputing (SC 09), ACM, 2009, pp. 1–12.
4. T. McLoughlin et al., “Over Two Decades of Integration-Based, Geometric Flow Visualization,” Computer Graphics Forum, vol. 29, no. 6, 2010, pp. 1807–1829.
5. W. Kendall et al., “Toward a General I/O Layer for Parallel Visualization Applications,” IEEE Computer Graphics and Applications, vol. 31, no. 6, 2011, pp. 6–10.
6. I.A. Sadarjoen et al., “Selective Visualization of Vortices in Hydrodynamic Flows,” Proc. Conf. Visualization (Vis 98), IEEE CS, 1998, pp. 419–422.
7. I.A. Sadarjoen and F.H. Post, “Detection, Quantification, and Tracking of Vortices Using Streamline Geometry,” Computers & Graphics, vol. 24, no. 3, 2000, pp. 333–341.
8. H. Doleisch, M. Gasser, and H. Hauser, “Interactive Feature Specification for Focus + Context Visualization of Complex Simulation Data,” Proc. Symp. Visualization (VisSym 03), Eurographics Assoc., 2003, pp. 239–248.
9. J. Woodring and H.-W. Shen, “Multivariate, Time Varying, and Comparative Visualization with Contextual Cues,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, 2006, pp. 909–916.
10. M. Jiang, R. Machiraju, and D. Thompson., “A Novel Approach to Vortex Core Region Detection,” Proc. Symp. Visualization (VisSym 02), Eurographics Assoc., 2002, pp. 217–225.
11. M.E. Maltrud and J.L. McClean,“An Eddy Resolving Global 1/10 Ocean Simulation,” Ocean Modelling, vol. 8, nos. 1–2, 2005, pp. 31–54.
12. T.O. Manley and K. Hunkins, “Mesoscale Eddies of the Arctic Ocean,” J. Geophysical Research, vol. 90, no. C3, 1985, pp. 4911–4930.
80 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool