1. Flow Visualization: The State-of-the-Art
Robert S. Laramee
The Visual and Interactive Computing Group
Computer Science Department
Swansea University
Swansea, Wales, UK 1

2. 2 http://cs.swan.ac.uk/~csbob/
Overview
Introduction to Flow Visualization (FlowViz)
What is Flow Visualization? A Brief Introduction
What approaches have been developed?
Classification:
Direct
Texture-based
Geometric
Feature-based flow visualzation
Applications
Conclusions and Future Work
A note on scope: An overview is provided with references to more depth. 2

3. What is Flow Visualization?
a classic topic within scientific visualization
depiction of vector quantities (as opposed to scalar quantities)
applications include automotive simulation, aerodynamics, turbomachinery, meteorology, oceanography, medical visualization
Challenges:
to effectively visualize both magnitude + direction, often simultaneously
large data sets
time-dependent data
multi-field visualization
What should be visualized? (data filtering/feature extraction) 3

4. Computational vs. Experimental FlowVis
Computational FlowVis -using computers for FlowVis
data resulting from flow simulation, measurements, or flow modelling, e.g., computational fluid dynamics (CFD)
computer-generated images and animations, often mimicking experimental FlowVis
Visualization of actual fluids, e.g. water and air
dye injection
interferometry
Schlieren/shadows
flow topology graphs
etc. 4

5. Simulation Data vs. Experimental Data
Data from Simulation:
FOR EACH cell in (irregular) grid:
compute flow direction
compute flow magnitude (explicitly or implicitly)
compute pressure (à la Navier-Stokes equations)
further attributes
Data from measurements:
FOR EACH location of a (regular) grid:
flow direction (reconstructed)
measure flow magnitude
Data from modelling:
Vector Field represented by analytic function
Flow direction and magnitude a function of location (and time) 5

6. Data Characterized by Many Dimensions
Spatial dimensions:
2D (planar flow, simplified or synthetic)
2.5D (boundary flow, flow on surface)
3D (real-world flow)
Temporal dimension:
steady flow -1 time step (or instantaneous or static flow)
time-dependent flow -multiple time steps (or unsteady or transient, real-world)
caution is advised in the context of animation
Simulation Data Attributes a.k.a. Data Dimensions:
velocity
temperature
pressure
and many more... 6

7. Flow Visualization Classification
direct: overview of vector field, minimal computation, e.g. glyphs, color mapping
texture-based: covers domain with a convolved texture, e.g., Spot Noise, LIC, ISA, IBFV(S)
geometric: a discrete object(s) whose geometry reflects flow characteristics, e.g. streamlines
feature-based: both automatic and interactive feature-based techniques, e.g. flow topology 7

8. Direct Flow Visualization
Direct Mapping of Flow Attributes to Visualization Space
Advantages:
simplicity
less computation time
intuitive
Disadvantage(s):
does not always clearly show flow properties and features, e.g., flow orientation
2D unsteady flow, color coding highlights rotation 8

9. Direct FlowViz: Color Coded Slicing
Color Coding in 2D, instantaneous: mapping flow attribute(s) to hue
multiple slices for vortex visualization (missing color coded boundary)
slicing probe for vortex visualization
(Schulz et al) 9

10. Direct FlowViz: Arrow/Hedgehog plots in 2D and 3D, static10

11. Direct FlowViz: Contours in 2D and Isosurfaces in 3D
Contours in a slice
Isosurface (and color coding) in 3D 11

12. Direct FlowViz: Volume Rendering Combined with Color Coding
the curvilinear bluntfin data set
(Westermann) 12

13. 13 http://cs.swan.ac.uk/~csbob/
Direct FlowViz: Hybrid Solutions -Arrow Plots and Color Coding in 2D, Steady and Unsteady 13

14. Direct Flow Visualization
For more information on direct flow visualization techniques, please see:
Frits H. Post, Benjamin Vrolijk, Helwig Hauser, Robert S. Laramee, and Helmut Doleisch, Feature Extraction and Visualization of Flow Fields in EUROGRAPHICS 2002, State of the Art Reports, pages , September , Saarbruecken, Germany
(1st STAR) 14

15. Texture-Based Flow Visualization
Computing textures that provide a dense coverage/visualization of a vector field.
Advantages:
detailed view of vector field
clearer perception of characteristics
contains elements of direct + geometric FlowViz
Disadvantages:
computation time
perception in 3D
aliasing 15

16. Texture-Based FlowViz: LIC (Cabral and Leedom) in 2D, Instantaneous16

17. 17 http://cs.swan.ac.uk/~csbob/
Texture-Based FlowViz: Spot Noise in 2D, Instantaneous with Color Coding
Good for visualizing detail. (de Leeuw) 17

18. Texture-Based FlowViz: Time-Dependent Texture Advection in 2D
Unsteady FlowViz of the Gulf of Mexico (Jobard et al) 18

19. Texture-Based FlowViz: 2D Unsteady Flow
Image Based Flow Visualization (IBFV, Van Wijk) 19

20. Texture-Based FlowViz: LIC on Surfaces, Unsteady
A comparison of 3 LIC techniques (left) UFLIC, (middle) ELIC, and (right) PLIC (Verma et. al.) 20

21. Texture-Based FlowViz: Texture-Advection on Surfaces, Unsteady
Image Space Advection (ISA, Laramee et al.) and Image Based Flow Visualization for Curved Surfaces (IBFVS, van Wijk) 21

22. Texture-Based FlowViz: LIC in 3D, Instantaneous
3D LIC with halos (Interrante and Grosch) 22

23. Texture-Based FlowViz: Texture Advection in 3D, Unsteady
3D IBFV (Telea and Van Wijk) 23

24. Texture-Based FlowViz: Texture Advection in 3D, Unsteady
3D texture-based flow vis with illumination, velocity masking, and focus+context (Weiskopf et al.) 24

25. Texture-Based Flow Visualization
For more information on texture-based flow visualization techniques, please see:
Robert S. Laramee, Helwig Hauser, Helmut Doleisch, Benjamin Vrolijk, Frits H. Post, and Daniel Weiskopf, The State of the Art in Flow Visualization: Dense and Texture-Based Techniques in Computer Graphics Forum, Vol. 23, No. 2, 2004, pages
(2nd STAR) 25

26. Geometric Flow Visualization
The computation of discrete objects whose shape is directly related to underlying geometry
Advantages:
intuitive
clearer perception of characteristics
Disadvantages:
placement
visual complexity in 3D 26

27. Geometric FlowViz: Some Terminology
Stream vs. Path vs Streak vs Time lines
Streamline
everywhere tangent to flow at instantaneous time, t0 (blue/aqua)
Pathline
path traced by a particle over time, t (red/maroon)
Streakline
line traced by continuous injection at location, x0 (light green)
Timeline
temporal evolution of initial line, l0 (yellow) 27

28. Geometric FlowViz: Streamlines and Streamlets in 2D, Steady-State
Evenly Space Streamlines (Jobard and Lefer) 28

29. Geometric FlowViz: Pathlines and Streamlets in 2D, Unsteady
Pathlines and particles using textures (Van Wik) 29

30. Geometric FlowViz: Timelines in 2.5D and 3D (B. Girod) (unsteady)
2.5D timelines using textures (Laramee et al.) and 3D timelines as discrete objects (B. Girod) 30

31. Geometric FlowViz: Seeding in 2D and 3D
Image-based (Jobard and Lefer) and interactive seeding (Schulz et al.) strategies 31

32. Geometric FlowViz: Streamribbons and Streamtubes, 3D, Steady-state32

33. Geometric FlowViz: Perceptual Issues in 3D, Steady-State
Illuminated Streamlines (Zoeckler et al)
StreamRunner (Laramee) 33

34. Geometric FlowViz: Streaklines in 3D
Streaklines in 3D as discrete objects (B. Girod) 34

35. 35 http://cs.swan.ac.uk/~csbob/
Geometric FlowViz: StreamBalls, StreamSurfaces, StreamArrows, 3D, Steady-State
Streamballs (Brill et a), Streamsurfaces (Hultquist), and StreamArrows (Loeffelmann et al.) 35

36. Geometric FlowViz: Flow Volumes (3D), Steady and Unsteady
A subset of 3D flow domain specified by initial 2D patch (Crawfis) 36

37. Geometric FlowViz: High Quality Animation, 3D, Unsteady
Visualization of Hurricane Isabel (Helgeland et al.) 37

38. Geometric Flow Visualization
For more information on geometric flow visualization techniques, please see:
Frits H. Post, Benjamin Vrolijk, Helwig Hauser, Robert S. Laramee, and Helmut Doleisch, Feature Extraction and Visualization of Flow Fields in EUROGRAPHICS 2002, State of the Art Reports, pages , September , Saarbruecken, Germany
(1st STAR) 38

39. Flow Visualization: An Application
swirl motion: characterized by motion about cylinder-aligned axis
more stable (easier)
tumble motion: characterized by motion about axis orthogonal to cylinder
unstable, more difficult 39

40. Flow Visualization: An Application
Achieving ideal patterns of motion leads to optimal mixing (of air and fuel) conditions
e.g., higher exhaust/gas ratio (EGR)
decrease in fuel consumption
lower emissions
Can visualization provide insight into or verify characteristic shape/behavior of flow?
What tools help to visualize swirl/tumble motion?
Where (in the combustion chamber) are ideal ideal flow pattern not being realized? 40

41. Flow Visualization: An Application
Direct, geometric, and texture-based flow visualization methods are used in 2D, 2.5D, and 3D. 41

42. 42 http://cs.swan.ac.uk/~csbob/
Feature-Based Flow Visualization
What is Feature-Based Flow Visualization?
Recall: What is Flow Visualization?
a classic topic within scientific visualization
depiction of vector quantities (as opposed to scalar quantities)
Challenges:
to effectively visualize both magnitude + direction, often simultaneously
large data sets
time-dependent data
multi-field visualization
What should be visualized? (data filtering/feature extraction) 42

43. What is Feature-Based Flow Visualization?
What is a feature?
feature: “A prominent or distinctive aspect, quality, or characteristic”, from dictionary.com
feature: any subset of the flow domain deemed interesting by an onlooker, i.e., the viewer (Bob’s definition)
What is feature-based flow visualization?
feature-based flow visualization: the focus on and resulting depiction of a subset of the flow domain. (Bob’s definition) 43

44. Feature-Based Flow Visualization Pipeline
Feature-Based flow visualization involves extracting features from the vector field domain.
selection: conceptually, filtering the data
clustering: coherency is established from point selection
attribute calculation: quantification, e.g., position, volume, orientation --> leads to features 44

45. Feature-Based Flow Visualization: Motivation
Why?
data reduction: original data set is represented with important features
perception: visualization of 3D and 4D flow is problematic in the absence of feature-based techniques
new insight: “new” characteristics of the flow can be observed
technical advantages: less memory consumption, faster interaction and rendering 45

46. Feature Based Flow Visualization: 3D Steady and Unsteady
Vector field clustering (Telea and Van Wijk)
Vortex extraction (Post et al.) 46

47. Feature Based Flow Visualization: 3D Unsteady
Cores of swirling particle motion in unsteady flow, extraction based on pathlines (Wienkauf et al.) 47

48. Feature Based Flow Visualization: 3D, Unsteady, Interactive
SimVis: interactive, multiple connected views (Doleisch et al.) 48

49. Feature-Based Flow Visualization
For more information on feature-based flow visualization techniques, please see:
Frits H. Post, Benjamin Vrolijk, Helwig Hauser, Robert S. Laramee, and Helmut Doleisch, The State of the Art in Flow Visualisation: Feature Extraction and Tracking in Computer Graphics Forum, Vol. 22, No. 4, 2003, pages
(3rd STAR) 49

50. Topology-Based Flow Visualization
Can be considered a sub-field of feature-based flow visualization:
singularities in flow field are extracted, loosely: locations where flow velocity approaches zero, e.g., sources, sinks, etc.
the relationship, connectivity, or topology between singularities is then analyzed and visualized
the topology of vector field is often called “skeleton” of the flow 50

51. Topology-Based Flow Visualization, 2.5D Steady
a single framework can be used to extract sources, sinks, saddle points, and periodic orbits
uncertainty due to discrete nature of simulation, interpolation, and integration can be factored into extraction and visualization
(Chen et al.) 51

52. Topology-Based Flow Visualization
For more information on topology-based flow visualization techniques, please see:
Robert S. Laramee, Helwig Hauser, Lingxiao Zhao, and Frits H. Post, Topology-Based Flow Visualization, The State of the Art, in Topology-Based Methods in Visualization (Proceedings of Topo-In-Vis 2005), Visualization and Mathematics, pages 1-19, 2007, Springer-Verlag
(4th! STAR) 52

53. Feature-Based Flow Visualization: An Application
Four major design goals:
an even distribution of flow to each engine cylinder
avoid regions of stagnant flow
avoid very high velocity flow
minimize fluid pressure loss between inlet and outlet 53

54. Feature-Based Flow Visualization: An Application
A range of direct, texture-based, geometric, feature- based, and topology-based visualization methods are applied 54

55. Flow Visualization: Challenges
FlowViz in 3D -perceptual issues, seeding strategies
Unsteady FlowViz in 3D -computation time
What should be extracted and visualized?
How can features be extracted and visualized? e.g. vortices
costly in terms of processing time
interpretation can be challenging
correctness: verification of result (sometimes ignored)
An area still rich in unsolved problems. 55

56. 56 http://cs.swan.ac.uk/~csbob/
Acknowledgements
Thank you for your attention! Any questions?
We would like to thank the following:
G. Chen, R. Crawfis, H. Doleisch, C. Garth, B. Girod, H. Hauser, A. Helgeland, V. Interrante, B. Jobard, W. de Leeuw, H. Loeffelmann, F. H. Post, A. Telea, H. Theisel, X. Tricoche, V. Verma, J. J. van Wijk. T. Weinkauf, D. Weiskopf, R. Westermann, E. Zhang
PDF versions of STARS 1-4 and MPEG movies available at: 56