Post on 22-Dec-2015
Scientific Visualization in the Geosciences
Gordon ErlebacherFlorida State University
Minnesota Supercomputer InstituteOctober 8, 2001
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We will … Discuss general visualization principles Some features of Amira ($$) Visualization within Amira
Discuss specific algorithms Compare different visualization packages
We will not …
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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
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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
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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
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Scientific VisualizationGeneral Principles
Maximize comprehension Maximize information Maximize accuracy Minimize clutter Maximize interactivity Independence of underlying meshing Minimize program response time
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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
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Computer Graphics Modeling
GeometricModels
AnimationParameters
Rendering
Textures
CameraModeling
LightModeling
Image Storage and Display
input
outputinput
input
input
input
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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
nR
nR
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Domain Connectivity (2D)
Cartesian Curvilinear Unstructured
Tree Graph
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Auxiliary Vertex Data Coordinates (2,3,or 4) Color Normals (for lighting) Temperature, conductivity, viscosity, etc.
Auxiliary Edge Data Flux
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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 …
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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
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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
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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)
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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
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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?
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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
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Volume Rendering
ScreenScreen
Ray casting Texture compositing
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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,
…)
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Amira
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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)
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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
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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.)
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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
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What to take from this talk
Interactivity is very important Data should be seen in 3D Interact with the data in a “natural”
manner
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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.)
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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
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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
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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
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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
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Examples
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pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x
Opaque isosurfaces
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pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x
Transparent isosurfaces
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FEM and 3D data visualization
Vector Fields:• illuminated field lines
Visualized with Amira(courtesy TGS)
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Heat Convection between Two Plates (Amira)
Data, courtesy David Yeun
643
subsampling
2573 dataset
Heat flow between two plates at constant temperature
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