scientific visualization in the geosciences gordon erlebacher florida state university minnesota...

Scientific Visualization in the Geosciences

Gordon ErlebacherFlorida State University

Minnesota Supercomputer InstituteOctober 8, 2001

October 8, 2001 Minnesota Supercomputer Institute 2

We will … Discuss general visualization principles Some features of Amira ($$) Visualization within Amira

Discuss specific algorithms Compare different visualization packages

We will not …


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


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


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


Scientific VisualizationGeneral Principles

Maximize comprehension Maximize information Maximize accuracy Minimize clutter Maximize interactivity Independence of underlying meshing Minimize program response time


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


Computer Graphics Modeling

GeometricModels

AnimationParameters

Rendering

Textures

CameraModeling

LightModeling

Image Storage and Display

input

outputinput

input

input

input


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


Domain Connectivity (2D)

Cartesian Curvilinear Unstructured

Tree Graph


Auxiliary Vertex Data Coordinates (2,3,or 4) Color Normals (for lighting) Temperature, conductivity, viscosity, etc.

Auxiliary Edge Data Flux


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 …


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


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


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)


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


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?


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


Volume Rendering

ScreenScreen

Ray casting Texture compositing


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,

…)


Amira


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)


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


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.)


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


What to take from this talk

Interactivity is very important Data should be seen in 3D Interact with the data in a “natural”

manner


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.)


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


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


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


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


Examples


pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x

Opaque isosurfaces


pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x

Transparent isosurfaces


FEM and 3D data visualization

Vector Fields:• illuminated field lines

Visualized with Amira(courtesy TGS)


Heat Convection between Two Plates (Amira)

Data, courtesy David Yeun

643

subsampling

2573 dataset

Heat flow between two plates at constant temperature

scientific visualization in the geosciences gordon erlebacher florida state university minnesota...

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