Technical Section: Surface-based flow visualization
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TL;DR: An up-to-date overview of the current state-of-the-art flow visualization techniques, including surface construction techniques and visualization methods applied to surfaces, with a focus on surface-based techniques.
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About: This article is published in Computers & Graphics. The article was published on 01 Dec 2012. and is currently open access. The article focuses on the topics: Visualization & Flow visualization.
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Figures
![Figure 5: Visualization of the flow field of a tornado with a point-based stream surface. The stream surface is seeded along a straight line in the center of the respective image. Image courtesy of D.Weiskopf et al. [STWE07].](/figures/figure-5-visualization-of-the-flow-field-of-a-tornado-with-a-2ciroqfb.png)
Figure 5: Visualization of the flow field of a tornado with a point-based stream surface. The stream surface is seeded along a straight line in the center of the respective image. Image courtesy of D.Weiskopf et al. [STWE07]. ![Figure 6: Time surface mesh in the Ellipsoid dataset. Although the surface has undergone strong deformation, the mesh remains in good condition. Image courtesy of C.Garth et al. [KGJ09].](/figures/figure-6-time-surface-mesh-in-the-ellipsoid-dataset-although-386ytyfu.png)
Figure 6: Time surface mesh in the Ellipsoid dataset. Although the surface has undergone strong deformation, the mesh remains in good condition. Image courtesy of C.Garth et al. [KGJ09]. ![Figure 14: Dense flow fields are first converted into a scalar field, and then displayed and analyzed by means of level-sets in this field. Image courtesy of R.Westermann et al. [WJE00].](/figures/figure-14-dense-flow-fields-are-first-converted-into-a-1rkecge1.png)
Figure 14: Dense flow fields are first converted into a scalar field, and then displayed and analyzed by means of level-sets in this field. Image courtesy of R.Westermann et al. [WJE00]. ![Figure 13: Particle based surface visualization. Red particles correspond to points on the separating surface. Green particles serve as context information. They correspond to points on time surfaces, which are released from the planar probe at a fixed frequency. Image courtesy of H.Theisel et al. [FBTW10]. c© IEEE/TVCG.](/figures/figure-13-particle-based-surface-visualization-red-particles-9f2ajrzi.png)
Figure 13: Particle based surface visualization. Red particles correspond to points on the separating surface. Green particles serve as context information. They correspond to points on time surfaces, which are released from the planar probe at a fixed frequency. Image courtesy of H.Theisel et al. [FBTW10]. c© IEEE/TVCG. ![Figure 21: A set of streamsurfaces seeded automatically on a tornado simulation. The image shows surfaces with edge highlighting improving the perception and allows insight into the behavior of the inner flow structures. Image courtesy of M.Edmunds et al. [EML∗11].](/figures/figure-21-a-set-of-streamsurfaces-seeded-automatically-on-a-1bj5z1om.png)
Figure 21: A set of streamsurfaces seeded automatically on a tornado simulation. The image shows surfaces with edge highlighting improving the perception and allows insight into the behavior of the inner flow structures. Image courtesy of M.Edmunds et al. [EML∗11]. ![Figure 22: This visualization demonstrates the feature-centered approach to stream surface placement. The vortex shedding is represented by the stream surfaces along with the contextual information of the complete flow field. Image courtesy of M.Edmunds et al. [ELM∗12]. This is a direct numerical Navier Stokes simulation by Simone Camarri and Maria-Vittoria Salvetti (University of Pisa), Marcelo Buffoni (Politecnico of Torino), and Angelo Iollo (University of Bordeaux I) [CSBI05] which is publicly available [Int]. We use a uniformly resampled version which has been provided by Tino Weinkauf and used in von Funck et al. for smoke visualizations [vFWTS08b].](/figures/figure-22-this-visualization-demonstrates-the-feature-2ddvd2hl.png)
Figure 22: This visualization demonstrates the feature-centered approach to stream surface placement. The vortex shedding is represented by the stream surfaces along with the contextual information of the complete flow field. Image courtesy of M.Edmunds et al. [ELM∗12]. This is a direct numerical Navier Stokes simulation by Simone Camarri and Maria-Vittoria Salvetti (University of Pisa), Marcelo Buffoni (Politecnico of Torino), and Angelo Iollo (University of Bordeaux I) [CSBI05] which is publicly available [Int]. We use a uniformly resampled version which has been provided by Tino Weinkauf and used in von Funck et al. for smoke visualizations [vFWTS08b].
![Figure 5: Visualization of the flow field of a tornado with a point-based stream surface. The stream surface is seeded along a straight line in the center of the respective image. Image courtesy of D.Weiskopf et al. [STWE07].](/figures/figure-5-visualization-of-the-flow-field-of-a-tornado-with-a-2ciroqfb.webp)
![Figure 6: Time surface mesh in the Ellipsoid dataset. Although the surface has undergone strong deformation, the mesh remains in good condition. Image courtesy of C.Garth et al. [KGJ09].](/figures/figure-6-time-surface-mesh-in-the-ellipsoid-dataset-although-386ytyfu.webp)
![Figure 14: Dense flow fields are first converted into a scalar field, and then displayed and analyzed by means of level-sets in this field. Image courtesy of R.Westermann et al. [WJE00].](/figures/figure-14-dense-flow-fields-are-first-converted-into-a-1rkecge1.webp)
![Figure 13: Particle based surface visualization. Red particles correspond to points on the separating surface. Green particles serve as context information. They correspond to points on time surfaces, which are released from the planar probe at a fixed frequency. Image courtesy of H.Theisel et al. [FBTW10]. c© IEEE/TVCG.](/figures/figure-13-particle-based-surface-visualization-red-particles-9f2ajrzi.webp)
![Figure 21: A set of streamsurfaces seeded automatically on a tornado simulation. The image shows surfaces with edge highlighting improving the perception and allows insight into the behavior of the inner flow structures. Image courtesy of M.Edmunds et al. [EML∗11].](/figures/figure-21-a-set-of-streamsurfaces-seeded-automatically-on-a-1bj5z1om.webp)
![Figure 22: This visualization demonstrates the feature-centered approach to stream surface placement. The vortex shedding is represented by the stream surfaces along with the contextual information of the complete flow field. Image courtesy of M.Edmunds et al. [ELM∗12]. This is a direct numerical Navier Stokes simulation by Simone Camarri and Maria-Vittoria Salvetti (University of Pisa), Marcelo Buffoni (Politecnico of Torino), and Angelo Iollo (University of Bordeaux I) [CSBI05] which is publicly available [Int]. We use a uniformly resampled version which has been provided by Tino Weinkauf and used in von Funck et al. for smoke visualizations [vFWTS08b].](/figures/figure-22-this-visualization-demonstrates-the-feature-2ddvd2hl.webp)
Citations
Feature Flow Fields
Holger Theisel,Hans-Peter Seidel,Georges-Pierre Bonneau,Stefanie Hahmann,Charles Hansen,Stephen N. Spencer +5 more
- 01 Jan 2003
TL;DR: This paper introduces a method for feature tracking which is based on the integration of stream lines of a certain vector field called feature flow field, and shows how to construct the feature flow fields for particular classes of features.
169
Visualization in Meteorology—A Survey of Techniques and Tools for Data Analysis Tasks
Marc Rautenhaus,Michael Böttinger,Stephan Siemen,Robert R. Hoffman,Robert M. Kirby,Mahsa Mirzargar,Niklas Röber,Rüdiger Westermann +7 more
TL;DR: An overview of visualization techniques from the fields of display design, 3D visualization, flow dynamics, feature-based visualization, comparative visualization and data fusion, uncertainty and ensemble visualization, interactive visual analysis, efficient rendering, and scalability and reproducibility is presented.
162
The State of the Art in Vortex Extraction
Tobias Günther,Holger Theisel +1 more
TL;DR: Vortices are commonly understood as rotating motions in fluid flows.
124
Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing
TL;DR: A dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition, which outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems.
38
Advection-Based Sparse Data Management for Visualizing Unsteady Flow.
TL;DR: This work explores a novel advection-based scheme to manage flow field data for both efficiency and scalability and shows significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications.
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