Flow Visualization Overview

1. Flow VisualizationOverview “Line Integral Convolution” Szigyarto Tamas Peter, Saint-Petersburg State University, Faculty of Applied Mathematics – Control Processes Department of Computer Modeling and Multiple Processors Systems

2. Agenda • Introduction • Mathematical Model • Classification of visualization approach • LIC technique • Conclusion Agenda

3. Introduction • Application: • Automotive industry • Aerodynamics • Turbo machinery design • Weather simulation • Medical visualization • Climate modeling Introduction

4. Mathematical Model • Basic definitions • Particle-Tracing • Numerical model Mathematical Model >> Overview

5. Vector fields and Integral curves • Time-dependent vector field • Integral curves The collection of allpossible integral curves for a vector field constitutes the corresponding flow Mathematical Model >> Basic Definitions

6. Two types of flow fields • Steady flows • Unsteady flows Mathematical Model >> Basic Definitions

7. Streamlines, pathlines and streaklines • Pathlines • Streamlines • Streaklines Mathematical Model >> Particle Tracing

8. Reconstruction of flow data • velocity is usually not given in analyticform, but requires reconstruction from the discrete simulation output • The output of flow simulationusually represented by many sample vectors , which are discretely representthe solution of the simulation processon large-sized grids • Reconstruction filter we need to get a continuous velocity Mathematical Model >> Numerical Model

9. Numerical integration Mathematical Model >> Numerical Model

10. Grids • Grids involved in flow simulation: • cartesian, (b) regular, • (c) general rectilinear, • (d) structuredor curvilinear, • (e) unstructured, • (f) unstructured triangular. Mathematical Model >> Numerical Model

11. Classification of visualization approach • Overview • Point-based direct flow visualization • Sparse representation for particle-tracing technique • Dense representation for particle-tracing technique • Feature-based visualization approach Classification Of Visualization Approach

12. Overview • Direct flow visualization:common approaches are drawing arrows or color codingvelocity. Intuitive pictures can be provided, especially in the case of two dimensions.Solutions of this kind allow immediate investigation of the flow data. • Dense, texture-based flow visualization: similar to direct flow visualization, a texture iscomputed that is used to generate a dense representation of the flow.A notion of where the flow moves is incorporated through co-related texture values alongthe vector field. • Geometric flow visualization: integration-based approaches first integrate the flow data and then use geometric objects as a basis for flow visualization. Examples include streamlines,streaklines,and pathlines. • Feature-based flow visualization:another approach makes use of an abstraction and/orextraction step which is performed before visualization. Special featuresareextracted fromtheoriginal dataset, such as important phenomena or topological information of the flow. Classification Of Visualization Approach >> Overview

13. Example of circular flow at the surface of a ring direct visualization by the use of arrow glyphs texture-based by the use ofLIC visualization based on geometric objects, here streamlines Classification Of Visualization Approach >> Overview

14. Point-Based Direct Flow Visualization • Traditional techniques: • arrow plots based on glyphs • direct line segments (the length represent the magnitude ofthe velocity) • Additional features: • applying arrow-plots to time-dependent flow fields • illumination and shadows • use complex glyphs with respect to velocity, acceleration, curvature, local rotation, shear, or convergence Classification Of Visualization Approach >> Point-Based Direct Flow Visualization

15. Examples Glyph-based 3D flow visualization, combined with illuminated streamlines Traditional arrow plot Classification Of Visualization Approach >> Point-Based Direct Flow Visualization

16. Problems • 3D representation issues: • the position and orientation of an arrow is more difficult tounderstand due to the projection onto the 2D image plane • arrow might occludeother arrows in the background • the problem of clutter • Solutions: • use of semi-transparency to avoid occlusion problems • highlightingarrows with orientations in a range specified by the user, or by selectively seeding thearrows to avoid clutter problem Classification Of Visualization Approach >> Point-Based Direct Flow Visualization

17. Feature-based visualization approach • Basic concept: • seek to compute a more abstract representation that already contains theimportant properties in a condensed form and suppresses superfluous information • Examples of the abstract data: • flow topology based on • critical points • vortices • shockwaves • Methods: • to emphasize special attributes for each type of feature, suitable representations must be used • glyphs or icons can be employed for vortices or for critical points • ellipses or ellipsoids to encode the rotation speed and other attributes of vortices Classification Of Visualization Approach >> Feature-based Visualization Approach

18. Examples Large vortex formed by detatching flow at the stay vane leading edge Topology-based visualization Classification Of Visualization Approach >> Feature-based Visualization Approach

19. Sparse Representations for Particle-TracingTechniques • Traditional approach: • compute characteristic curves (streamlines, pathlines, streaklines) and draws them asthin lines • streamlets – lines generated by particles traced for a very short time • use of geometric objects of finite extent perpendicular to the particle trace • streamribbon: • an area swept out by a deformable line segment along a streamline. The strip-like shape of a streamribbon displays the rotational behavior of a 3D flow. • streamtubes: • is a thick tube-shaped streamline whose radial extent shows the expansion of the flow • stream polygons Classification Of Visualization Approach >> Sparse Representation For Partical Tracing Technique

20. Examples Combination of streamlines, streamribbons, arrows, and color coding for a 3D flow (courtesy of BMW Group and Martin Schulz). Classification Of Visualization Approach >> Sparse Representation For Partical Tracing Technique

21. Examples Sparse representation based on the use of streamlets Classification Of Visualization Approach >> Sparse Representation For Partical Tracing Technique

22. Dense Representations for Particle-TracingTechniques • Dense representation typically built upon texture-based techniques among their: • Spot Noise • Line Integral Convolution (LIC) Classification Of Visualization Approach >> Dense Representation For Partical Tracing Technique

23. Spot noise • produces a texture by generating a set of spots on the spatial domain (spot is an ellipse or another shape that wrapes and distributed over domain) • each spot representsa particle moving over a short period of time and results in a streak in the direction of theflow at the position of the spot • enhanced spot noise adds the visualization of the velocitymagnitude and allows for curved spots • common form Classification Of Visualization Approach >> Dense Representation For Partical Tracing Technique

24. Examples A snapshot of the unsteady spot noise algorithm. Image courtesy of DeLeeuw and Van Liere Classification Of Visualization Approach >> Dense Representation For Partical Tracing Technique

25. Line Integral Convolution (LIC) • common form • LIC was one of the first dense, texture-based algorithms able to accurately reflect velocity fields with high local curvature Line Integral Convolution >> Foundation

26. LIC-based hierarchy • LIC extends directions: • adding flow orientation cues; • (2) showing local velocity magnitude; • (3) adding support for non-rectilinear grids; • (4) animating the resulting textures such that the animation shows the upstream and downstream flow direction; • (5) allowing real-time and interactive exploration; • (6) extending LIC to 3D; • (7) extending LIC to unsteady vector fields; Line Integral Convolution >> Foundation

27. Curvilinear and unsteady LIC • Basic challenges for original LIC: • LIC portrays a vector field with uniform velocity magnitude • LIC operates over a steady flows • LIC uses only a Cartesian grids • Solutions (by Forsell and Cohen): • curvilinear LIC introduces technique for displaying vector magnitude • use streaklines instead streamlines, so the LIC can trace a path that incorporates multiple time steps Line Integral Convolution >> Extentions

28. Fast LIC (by Stalling and Hege) • Fast LIC comparison with original technique: • Fast LIC approximately one order magnitude faster than original LIC • Key parts of the fast LIC: • fast LIC minimizes the computation ofredundant streamlines present in the original method • fast LIC exploits similarconvolution integrals along a single streamline and thus reuses parts of the convolutioncomputation from neighboring streamline texels Line Integral Convolution >> Extentions

29. Fast LIC modifications • Parallel fast LIC:computes animation sequences on a massively parallel distributed memory computer. • Fast LIC on the surfaces:The approach by Forssell and Cohen was limited to surfaces represented bycurvilinear grids. The proposed method works by tessellating a given surface representation withtriangles. • Volume LIC:introduces theuse of halos in order to enhance depth perception such that the user has a better chance atperceiving the 3D space covered in the visualization • Enhanced fast LIC and LIC with normal:Hege and Stallingexperimentwith higher order filter kernels in order to enhance the quality of the resultingLIC textures.Scheuermannaddress this missing orthogonal vector field component by extending fast LIC to incorporatea normal component into the visualization. Line Integral Convolution >> Extentions

30. Fast LIC example A result from the volume LIC method. Image courtesy of Interrante and Grosch Line Integral Convolution >> Extentions

31. Dynamic LIC • DLIC:Sundquist presents an extension to fast LICin order tovisualize time-dependent electromagnetic fields • Assumption:the motion of thefield is not necessarily along thedirection of the field itself in the case of electromagneticfields • Result:proposed algorithm handles the case of when the vector field and thedirection of the motion of the field lines are independent Line Integral Convolution >> Extentions

32. Directional problems with LIC • Dye injection:Shen address the problem of directional cues in LIC by incorporatinganimation and introducing dye advection into the computation. The simulation of dye maybe used to highlight features of the flow. But, modelling of dye transport isnot always physically correct since dye is dispersed not only by advection, but also bydiffusion. • Oriented LIC:address the problem of direction of flow in still images. By orientation, means the upstream and downstream directions of the flow, not visible in the original LIC implementation. Conceptually,the OLIC algorithm makes use of a sparse texture consisting of many separatedspots that are smeared in the direction of the local vector field through integration. • Fast Rendering OLIC:A fastversion of OLICis achieved byWegenkittl andGroller via a trade-off of accuracy for time. Line Integral Convolution >> Extentions

33. Dye injection examples • Dye injection is used to highlight areas of the flow: • in combination on the boundary, • in combination with a low-contrast LIC texture. • The data set is a slice through an intake port and combustion chamber from CFD Line Integral Convolution >> Extentions

34. Unsteady Flow LIC • UFLIC: Shen and Kao extend the original LIC algorithm to handle unsteady flows • Idea: introduce a new convolution filter that better models the nature of unsteady flow • Why? According to Shen and Kao, Forssell and Cohens approach (ULIC) has multiple limitations including: • lack of clarity with respect to spatial coherence • deriving current flow valuesfrom future flow values • unclear exposition with respect to temporal coherence • lack of accurate time stepping All of these problems are addressed by UFLIC!!! Line Integral Convolution >> Extentions

35. UFLIC in action Results from A Texture-BasedFramework for Spacetime-Coherent Visualization of Time-Dependent Vector Fields, by D. Weiskopf, G. Erlabacher, and T. Ertl. Line Integral Convolution >> Extentions

36. 3D LIC • Rezk-Salama propose rendering methods to effectively display the results of 3DLIC computations. They utilize texture-based volume rendering in an effort to provideexploration of 3D LIC textures at interactive frame rates • Proposed approach: • use of transfer functions • allow user to see through portions of the LIC textures deemed uninteresting by the user • use of clipping planes Line Integral Convolution >> Extentions

37. 3D LIC examples An LIC visualization showing a simulation offlow around a wheel. The appropriate choice of transferfunction results in a sparser noise texture. Image courtesy of Rezk-Salama. Line Integral Convolution >> Extentions

38. Spot Noise vs. LIC • Spot noise is capable of reflecting velocity magnitude withinthe amount of smearing in the texture, thus freeing up huefor the visualization of another attribute such as pressure, temperature etc. • LIC is more suited forthe visualization of critical points which is a key elementin conveying the flow topology. The vector magnitudes arenormalized thus retaining lower spatial frequency texture inareas of low velocity magnitude Line Integral Convolution >> Conclusion

39. Spot Noise vs. LIC (visual comparison) Visualization of flow past a box using (left) spot noise and (right) LIC. Line Integral Convolution >> Conclusion

40. References [1] The State of the Art in Flow Visualization: Dense and Texture-BasedTechniques, Robert S. Laramee, Helwig Hauser, Helmut Doleisch, Benjamin Vrolijk, FritsH. Post, and Daniel Weiskopf,http://www.vrvis.at/ar3/pr2/star/ [2] Flow Visualization Overview, Daniel Weiskopf and Gordon Erlebacher [3] Scientific Visualization of Large-Scale Unsteady Fluid Flows, David A. Lane [4] Analysis and Visualization of Features in Turbomachinery Fluid Flow,Turbomachinery CFD Flow Visualization,http://www.cg.inf.ethz.ch/~ebauer/turbo/ References

41. Thanx for your attention!!!