Scientific Visualization Motivations
A D V E R T I S E M E N T
Through the availability of increasingly powerful computers with increasing
amounts of internal and external memory, it is possible to investigate
incredibly complex dynamics by means of ever more realistic simulations.
However, this brings with it vast amounts of data . To analyze these data it is
imperative to have software tools which can visualize these multi-dimensional
data sets. Comparing this with experiment and theory it becomes clear that
visualization of scientific data is useful yet difficult. For complicated,
time-dependent simulations, the running of the simulation may involve the
calculation of many time steps, which requires a substantial amount of CPU time
, and memory resources are still limited, one cannot save the results of every
time step. Hence, it will be necessary to visualize and store the results
selectively in `real time' so that we do not have to recompute the dynamics if
we want to see the same scene again. `Real time' means that the selected time
step will be visualized as soon as it has been calculated.
The main reasons for scientific visualization are the following ones : it will
compress a lot of data into one picture (data browsing), it can reveal
correlations between different quantities both in space and time, it can furnish
new space-like structures beside the ones which are already known from previous
calculations, and it opens up the possibility to view the data selectively and
interactively in `real time'. By following the formation and the deformation as
well as the motions of these structures in time, one will gain insight into the
complicated dynamics. As was mentioned before, we also want to integrate our
simulation codes into a visualization environment in order to analyze the data
'real time' and to by-pass the need to store every intermediate result for later
analysis. This is possible by means of processing in which the
simulation is distributed over a set of high-performance computers and the
actual visualization is done on a graphical distributive workstation.
It is also very useful to have the possibility to interactively change the
simulation parameters and immediately see the effect of this change through the
new data. This process is called computational steering and it will
increase the effective use of CPU time.
Common Questions and Concerns
The discussion is focussed on the following questions:
- What is the improvement in the understanding of the data as compared to
the situation without visualization?
- Which visualization techniques are suitable for one's data? Are direct
volume rendering techniques to be preferred over surface rendering
techniques?
- Can current techniques, like streamline and particle advection methods,
be used to appropriately outline the known visual phenomena in the system?
The success of visualization not only depends on the results which it
produces, but also depends on the environment in which it has to be done. This
environment is determined by the available hardware, like graphical
workstations, disk space, color printers, video editing hardware, and network
bandwidth, and by the visualization software. For example, the graphical
hardware imposes constraints on interactive speed of visualization and on the
size of the data sets which can be handled. Many different problems encountered
with visualization software must be taken into account. The user interface,
programming model, data input, data output, data manipulation facilities, and
other related items are all important. The way in which these items are
implemented determines the convenience and effectiveness of the use of the
software package as seen by the scientist. Furthermore, whether software
supports distributive processing and computational steering must be taken into
account.
Examples
- Engineering
- Computational Fluid Dynamics
- Finite Element Analysis
- Electronic Design Automation
- Simulation
- Medical Imaging
- Geospatial
- RF Propagation
- Meteorology
- Hydrology
- Data Fusion
- Ground Water Modeling
- Oil and Gas Exploration and Production
- Finance
- Data Mining/OLAP
|
