1 / 49

Scientific Visualization in the Geosciences

Scientific Visualization in the Geosciences. Gordon Erlebacher Florida State University. Minnesota Supercomputer Institute October 8, 2001. We will …. Discuss general visualization principles Some features of Amira ($$) Visualization within Amira. We will not ….

rehan
Download Presentation

Scientific Visualization in the Geosciences

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Scientific Visualization in the Geosciences Gordon Erlebacher Florida State University Minnesota Supercomputer Institute October 8, 2001

  2. We will … • Discuss general visualization principles • Some features of Amira ($$) • Visualization within Amira We will not … • Discuss specific algorithms • Compare different visualization packages Minnesota Supercomputer Institute

  3. Personal background • I have conducted research in Fluid Dynamics and scientific visualization • Simulations of compressible transition and turbulence • Turbulence modeling • Numerical algorithms • Work in Scientific Visualization • Vector fields • Interactivity • Distributed visualization Minnesota Supercomputer Institute

  4. Possible Research • Visualization of time-dependent motion • Change of topology • Interactive feature extraction • Interactive exploration • Use of force feedback in visualization • Handling of Multi-Gigabyte datasets • Exploration of high-dimensional spaces Minnesota Supercomputer Institute

  5. Why Visualize Data • Numerical simulations and experiments produce extremely large datasets • The size of these datasets are increasing exponentially fast • Numerical output (e.g., tables) does not lend itself to easy comprehension Minnesota Supercomputer Institute

  6. Scientific VisualizationGeneral Principles • Maximize comprehension • Maximize information • Maximize accuracy • Minimize clutter • Maximize interactivity • Independence of underlying meshing • Minimize program response time Minnesota Supercomputer Institute

  7. Scientific Visualization • Extract from large datasets more meaningful components (called data extracts) • Isosurface, streamlines, streaklines, vector field topology, vortex tubes, cracks, fault lines, etc. • Sedimentation layers, free-surfaces, edge and surface extraction • Render this data with comprehension in mind, as opposed to visual realism Minnesota Supercomputer Institute

  8. Computer Graphics Modeling input Camera Modeling input input Light Modeling Geometric Models Rendering output input Image Storage and Display Animation Parameters Textures input Minnesota Supercomputer Institute

  9. Taxonomy • Dimensionality of domain • 1D (x)  4D (x,y,z,t) • N-D (e.g., phylogeny) • Dimensionality of range • Scalar, vector, tensor fields • Domain connectivity • (Un)Structured, points, graphs Minnesota Supercomputer Institute

  10. Domain Connectivity (2D) Unstructured Curvilinear Cartesian Tree Graph Minnesota Supercomputer Institute

  11. Auxiliary Vertex Data • Coordinates (2,3,or 4) • Color • Normals (for lighting) • Temperature, conductivity, viscosity, etc. Auxiliary Edge Data • Flux Minnesota Supercomputer Institute

  12. Some Requirements of Geological Visualization • Vector fields, gradient fields • Multiple scales (time and space) • Multi-domain, curvilinear and tetrahedral grids • Time dependent structures • Interactivity in time • Interactive exploration of large datasets • More … Minnesota Supercomputer Institute

  13. HardwareDesired Features • Large framebuffer memory • Double buffering (smooth animation) • Stereo (left/right buffers) • Z-buffering (hidden line removal) • 1600x1200 frame with 32 bit color: 7.7 Mbytes • Double buffering: 15 Mbytes • Stereo: 30 Mbytes • Even higher with alpha, stencil, z-buffers • Large texture memory • Used by many modern visualization algorithms Minnesota Supercomputer Institute

  14. For $4,000 … • Dell Precision Workstation 530 • Dual pentium: 1.7 Ghz cpu • 1 Gbyte memory (400 Mhz) • 21 inch screen • 80 Gbyte disk • Read/Write CD-rom (read/write DVD is better) • Quadro2-Pro graphics card (can not handle dual monitors and stereo) • Linux/Windows X Minnesota Supercomputer Institute

  15. Immersive Environments • Powerwalls and other large-scale displays • (PICTURE) 16’x8’ and larger • Rear or front projection • Enables 5-20 people to view and interact with the data simultaneously. • Only one person controls the interaction • Caves • Project in stereo onto 5 or 6 walls • Provides realistic display of data • Users interact using wands and other devices (one at a time) Minnesota Supercomputer Institute

  16. Visualization AlgorithmsRange Types • Scalar • Isocontours (2D), isosurface (3D) • Volume rendering • Vector • Streamlines, pathlines, streaklinesm • Line integral convolution (steady state) • LEA (Lagrangian-Eulerian Advection (Jobard, Erlebacher, Hussaini) (time-dependent) • Critical points, vector field topology • Tensor • Tensor field topology (symmetric and antisymmetric tensors) (see work of Hesselink) • Hyperstreamlines: streamlines along dominant eigenvector, ellipsoidal cross-section normal to the streamline, determined by other two eigenvalues Minnesota Supercomputer Institute

  17. Visualization Algorithm Challenges • Strike balance between • High- resolution versus interactive speed • How to • Time-dependent visualization • Describe and view change of data topology • Vector and scalar fields • Tensor fields (i.e., rate of strain tensor) • How to navigate a Terabyte dataset? Minnesota Supercomputer Institute

  18. Volume Rendering • It is often difficult to choose isosurface values that produce meaningful surfaces • More often, it is a collection of isosurfaces that is required • Examples: x-rays, translucent medium Minnesota Supercomputer Institute

  19. Screen Screen Volume Rendering Ray casting Texture compositing Minnesota Supercomputer Institute

  20. APIs, Packages, Toolkits • Low Level Graphic APIs (OpenGL, Direct8X) • Visualization APIs (Open Inventor) • Visual Interfaces (Ensight, LightView) • Flowcharting (OpenDX, Iris Explorer, Amira) • Visualization Toolkits (VTK, NCAR) • Free specialized Solutions (Rasmol, MolView) • Commercial specialized solutions (AmiraMol, …) Minnesota Supercomputer Institute

  21. Amira Minnesota Supercomputer Institute

  22. Amira (from TGS) • Flowcharts are created interactively by the user • Each component has an associated user interface • Software capitalizes on graphic hardware (SGI, Onyx, Nvidia, ATI) to achieve good performance • Flowcharts, called networks, can be saved for later use. • Developer version allows users to create their own modules for specialized visualization. • The user interface is based on Qt (free for academic use); portable on wide array of architectures (including PDA) Minnesota Supercomputer Institute

  23. Amira • Amira is a commercial package • I don’t necessarily recommend this package • However, • It has nice features, perhaps useful to the visualization of static and time-dependent fluid structures Minnesota Supercomputer Institute

  24. Amira • Read in 3D file • Generate several planar cross-sections • Generate an iso-surface • Generate a volumetric plot • Combine techniques • Demonstrate data querying (line cut, pointwise, etc.) Minnesota Supercomputer Institute

  25. Amira Features • Very Interactive • Manipulators • Interact with the data • Extensible • Users can write own extension modules • API is very sophisticated • Highly advanced algorithms to do • Isosurface, volume rendering, vector visualization • Combinations of the above Minnesota Supercomputer Institute

  26. What to take from this talk • Interactivity is very important • Data should be seen in 3D • Interact with the data in a “natural” manner Minnesota Supercomputer Institute

  27. Computational Steering • Couple numerical simulations with scientific visualization • Drill down of image for data querying (i.e., visualization metadata or underlying raw data) • Raw data is often not on client: need robust client/server communication • Would like to query a running simulation and change its parameters (e.g., PV3, Cumulus, SciRun, etc.) Minnesota Supercomputer Institute

  28. Future of Visualization • Visualization is/has become multidisciplinary • Successful visualization system must address • I/O • Maintainability • Flexibility (via plugins for example) • Accessibility (low cost and easy to use/install) • Robust • Standardization • The above features are not consistent with each other Minnesota Supercomputer Institute

  29. Visualization Ubiquity • Collaboration through visualization • Office walls become visualization displays (E-Ink: thin, pliable medium capable of electronic encoding) • Exchange of visual data becomes as ubiquitous as exchange of text documents in 2001 Minnesota Supercomputer Institute

  30. An Ideal Visualization System • Reusable modules • Flexible • Ease of use • Low memory footprint • Extensible • Scriptable • Good debugging • Portable • Intelligent defaults • Changeable defaults • Interpreted and compiled modes • Novice and expert modes • Mathematical text editor Minnesota Supercomputer Institute

  31. Future trends in Visualization • Use of Object-Oriented design patterns for reusability • Plugin technology on distributed systems • Extensive use of visualization across the network • Increased intelligence in software • Insertion of new algorithms without recompilation Minnesota Supercomputer Institute

  32. Examples Minnesota Supercomputer Institute

  33. pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x Opaque isosurfaces Minnesota Supercomputer Institute

  34. pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x Transparent isosurfaces Minnesota Supercomputer Institute

  35. Minnesota Supercomputer Institute

  36. FEM and 3D data visualization Visualized with Amira (courtesy TGS) • Vector Fields: • illuminated field lines Minnesota Supercomputer Institute

  37. Heat Convection between Two Plates (Amira) Data, courtesy David Yeun 2573 dataset 643 subsampling Heat flow between two plates at constant temperature Minnesota Supercomputer Institute

  38. Minnesota Supercomputer Institute

  39. Minnesota Supercomputer Institute

  40. Minnesota Supercomputer Institute

  41. Minnesota Supercomputer Institute

  42. Minnesota Supercomputer Institute

  43. Minnesota Supercomputer Institute

  44. Minnesota Supercomputer Institute

  45. Minnesota Supercomputer Institute

  46. Minnesota Supercomputer Institute

  47. Minnesota Supercomputer Institute

  48. Minnesota Supercomputer Institute

  49. Minnesota Supercomputer Institute

More Related