Tony McLoughlin 1 Part 2: Overview  Part 2: Tony McLoughlin  Effective Particle Tracing  Point-Based Seeding in 3D

Tony McLoughlin 2 Computing a Particle Trace  Point Location  Determine which cell the point lies within.  More difficult for unstructured grids – may involve a search.  Interpolate Velocity  Determine the cell velocity values  Interpolate velocity at the location of the point.  Integrate  Runge Kutta  Adaptive integrators  Greatly influences the accuracy of the fieldlines.

Tony McLoughlin 3 Efficient Particle Tracing in 3D Efficient Streamline, Streamribbon, and Streamtube Constructions on Unstructured Grids (Ueng et al. '96)‏ Streamtubes and Streamribbons computed by tracing streamlines. Transform from physical-space to canonical coordinates. Reduces streamline computation. Canonical coordinates also used for the cell searching.

Tony McLoughlin 4 Efficient Particle Tracing in 3D UFLOW: Visualizing uncertainty in Fluid Flow (Lodha et al. '96)‏ A system for analyzing the changes resulting from the use of different integrators and step-sizes. A pair of streamlines are seeded: Uncertainty is depicted by glyphs, strips, tubes.

Tony McLoughlin 5 Efficient Particle Tracing in 3D Prictical Tracing Algorithms for 3D Curvilinear grids (Sadarjoen et al. '97)‏ Present a comparison of several algorithms for particle tracing on 3D curvilinear grids. A thorough discussion between physical-space algorithms vs computational-space algorithms. - Results demonstrate the physical-based methods are actually perform better.

Tony McLoughlin 6 Efficient Particle Tracing in 3D Particle Tracing in σ-Transformed Grids Using Tetrahedral 6-Decomposition (Sadarjoen et al. '98)‏ σ- transformed grids: - Hexahedral Cells - x and y dimensions differ by 2-3 orders of magnitude from the z dimension. - Very thin cells. Each cell decomposes into 6 tetrahedral elements - As opposed to the 5 tet cells as is more common. - Prevents a center tetrahedron covering the center of the cell. Thus, simplifying point location. - Reduces chance of infinite loops compared to tet-5 decomposition.

Tony McLoughlin 7 Efficient Particle Tracing in 3D Interactive Visualization of Fluid Dynamics Simulations in Locally Refined Cartesian Grids (Schulz et al. '99)‏ Uses multi-resolution grids. - Finer resolution at object boundary areas. Collision detection. - Terminate particle - Follow path along object boundary. - Prevents artifacts where a trace enters the objects and reappears randomly elsewhere.

Tony McLoughlin 8 Efficient Particle Tracing in 3D Comparative Flow Visualization (Verma and Pang ‘04)‏ Investigate the effect of sub-sampling dataset has on the accuracy of resulting visualizations. Techniques based on UFLOW are used to compare the visualizations. Streamribbons are compared by straightening a ribbon and transforming a second ribbon so that relative distances are maintained. Dense bundles of streamlines are compared by creating a scalar field containing the relative differences. Standard scalar field visualization techniques can then by applied.

Tony McLoughlin 9 Efficient Particle Tracing in 3D UFAT: A Particle Trace for Time-dependant Flow Fields (Lane. '94)‏ Particle Tracing in unsteady flow fields. Demonstrated on moving grids. Requires that two time-steps need to be stored in memory at once. Streaklines stored at each time- step for animation.

Tony McLoughlin 10 Efficient Particle Tracing in 3D Interactive Time-Dependant Particle Tracing Using Tetrahedral Decomposition (Kenwright and Lane. ‘96)‏ Uses more accurate physical-space computations. Decomposes a curvilinear grid into tetrahedral elements – performed on the fly as the size of large unsteady simulations prohibits pre-computation. A new point location strategy for tetrahedral grids is utilized.

Tony McLoughlin 11 Point-based Seeding in 3D Steady-State Domains Visualization of Turbulent Flow with Particles (Hin and Post. ‘93)‏ Based on Reynolds’ decomposition - Mean flow + fluctuation. Fluctuation determined stochastically - Using random walk models. Over many time-steps leads to the effect of turbulent behaviour.

Tony McLoughlin 12 Point-based Seeding in 3D Steady-State Domains Interactive Visualization of 3D-Vector Fields using Illuminated Streamlines (Zockler et al. ‘96)‏ No native support for lighting lines in OpenGL -> No unique normal vector. Light, tangent and view vectors for each vertex are used to compute t1 = V ● T and t2 = L ● T. t1 and t2 are then used to look up into specially constructed textures. Diffuse and specular terms are computed per pixel.

Tony McLoughlin 13 Point-based Seeding in 3D Steady-State Domains Strategies for Interactive Exploration of 3D Flow Using Evenly-Spaced Illuminated Streamlines (Mattausch et al. ‘03)‏ Combines Illuminated Lines from [ZSH96] with evenly-spaced streamlines from [JL97a]. Evenly-spaced streamlines extended to 3D. - There are an infinite number of candidate positions to insert new seed points at distance d_sep. - This is restricted to six positions to make this method feasible.

Tony McLoughlin 14 Point-based Seeding in 3D Steady-State Domains Illuminated Lines Revisited (Mallo et al. ‘05)‏ Improved illuminated lines method. Based on cylinder averaging. - Calculates the diffuse and specular terms from the infinitesimal facets of a cylinder. Does not produce bi-directional lighting like [ZSH96]. Thus, provides clearer depth and orientation without having to resort to a strong specular term. GPU implementation.

Tony McLoughlin 15 Point-based Seeding in 3D Steady-State Domains Geometric Flow Visualization Techniques for CFD Simulation data (Laramee and Hauser ‘05)‏ Presents a variety of novel techniques: - Oriented streamlines for static images - Stream comet - Streamlets - Animated streamlines - Uses a stipple pattern.

Tony McLoughlin 16 Point-based Seeding in 3D Steady-State Domains Strategy for Seeding 3D Streamlines (Ye et al. ‘05)‏ Aims – Sufficient coverage of the domain and an uncluttered visualization. Scans the domain for critical points and identifies areas of interest. Different seeding strategies are defined for the differing types of critical points. Can detect how close critical points are to each other and merge to two most appropriate seeding templates. Streamlines are added to low density regions and removed from high density regions to reduce visual clutter.

Tony McLoughlin 17 Point-based Seeding in 3D Steady-State Domains Similarity-Guided Streamline Placement with Error Evaluation (Chen et al. ‘07)‏ Does not solely rely on density or feature extraction. Compares candidate streamlines based upon their shape and direction in addition to the Euclidean distance. Windows are placed on streamline points. These are used to select the samples that are to be compared. Points are removed by these tests. A d_sep parameter is used to control the distance of parallel streamlines.

Tony McLoughlin 18 Point-based Seeding in 3D Steady-State Domains Image-Based Streamline Generation and Rendering (Li and Shen ‘07)‏ Image-based approach to 3D Flow Vis. Streamlines seeded in image-space, then projected back into object-space. - Depth map used to store the depth at every pixel. Problem with overlapping streamlines in image-space. - Halos used to address this problem.

Tony McLoughlin 19 Point-based Seeding in 3D Time-Varying Domains The Virtual Wind Tunnel (Bryson and Levit ‘92)‏ Virtual environment – Head tracked stereoscopic display. User can interact and manipulate objects, such as seed positions - Glove. Performance of the application greatly influences user experience. - Less than 10fps reduces coordination with the environment.

Tony McLoughlin 20 Point-based Seeding in 3D Time-Varying Domains Eyelet Particle Tracing – Steady Visualization of Unsteady Flow (Wiebel and Scheuermann ‘05)‏ A method to visualize a time-dependant scene in a static image. Streaklines and pathlines that pass through and eyelet at different times. Surface construction using these trace lines. Quality dependant upon the placement of the eyelet.

Tony McLoughlin 21 Point-based Seeding in 3D Time-Varying Domains High Quality and Interactive Animations of 3D Time- Varying Vector Fields (Helgeland and Elbroth. ‘06)‏ Hybrid Geometric- and Texture-based Seeding based upon evenly spaced streamlines [JL97a]. Seed positions pre-computed and stored in a 3D texture. Particles added at inflow boundaries and removed from dense clusters. Texture-based approach used to generate field lines. Volume rendering to draw and animate.

Tony McLoughlin 22 End of Part II Thanks for listening Questions? Next up: Ronald Peikert and Part III.