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NASA Vector Field Data
Kim Son Nguyen
Introduction:
The “Bluntfin” dataset was collected by NASA researchers C.M. Hung and P.G. Buning in 1984.
The set is a vector field, contains 40960 data entries and 37479 (39 x 31 x 31) cells. It takes the
form of a structured grid that displays “supersonic flow over a flat rate with a blunt fin rising
from the plate”. It is considered small by the modern advances in this field of research. Despite
its modest size, Bluntfin has been thoroughly analyzed throughout the years and is collectively
described in the visualization circles as “the Utah Teapot” of CFD Visualization.
Figure 1- The Utah Teapot of CFD Visualization
Related works
Because it is a simple vector field data set, vector field visualizations techniques are appropriate
to explore its underpinnings. A vector field is a function that allocates vector at any given point.
A vector has two components, direction and magnitude. The function is usually given by a
differential equation. The resulting vectors are called a “flow”, in which these vectors line up
densely and form particle trajectories.
Figure 2- Indian Monsoon
This has been an active field of research. Visualizing vector field poses some major hurdles.
Data sets are usually large and time-dependent. The most significant problem however, is to
convey clearly and effectively both the direction and magnitude, often simultaneously.
Vector fields are often used to visualize data in two or three dimensions. Some examples of their
applications are velocities and wind of the ocean, magnetic field, blood flow, components of
stress and strain, and just any data type that has to do with directions. They are also used in
computer graphics, such as texture synthesis, fluid simulation, smoke simulation,
parameterization, shape deformation, among other topics.
The Bluntfin belongs to a sub-discipline of vector field visualization called Computational Fluid
Dynamics. CFD predicts flow behaviors. CFD data is often derived from flow simulation either
through or around an object. CFD datasets are usually large, gigabytes in size, unsteady (timedependent), unstructured, adaptive solution grid and smooth field. This can be done in 2D, 2.5D
or 3D. This Bluntfin data set will focus on 3D.
There are four general methods of vector field visualization: direct, texture-based, geometric,
feature based. This paper will focus on geometric, more specifically, streamlines. Streamlines
are curves that are tangent to the velocity vector of the flow data. In other words, streamline is
integration over time. Streamlines are also called trajectories, or solution curves. They are used
to display the flow direction by seeding particles and calculating their positions in the vector
field. There are two integration methods, Euler and Runge-Kutta. Euler is simple and
inexpensive, but imprecise with the more minute time interval, RK is ideal in higher order,
complex flows. Streamline has high computational costs. Streamline has two sub-routines,
streaklines and pathlines. The distinction only exists in unsteady (time-dependent) flow, as three
lines coincide in a steady flow.
Approach
We start with an outline of the fin dataset. Then two subsets are extracted, the planes cut through
the very bottom of the fin and the point before the descent of the data points, as shown in the
image.
Then color map it to velocity, changing the display data set to wireframe. Here we can see the
trilinearly interpolated velocities in the two cutting planes.
Then I played around with the streamlines. At first it was very convoluted, but I reduced the
number of seed points to 10 and the data becomes much more manageable. Then I mapped it to
colors. I also displayed glyphs to show the directions of the vectors.
Result
Conclusion
The flow field direction ascends from the base of the fin to the tip and the velocity decreases as it
goes on. The data is densely populated and sampled at the base, then sparse out as it reaches the
top. Since the data set is small, most of these visualizations were not computationally expensive,
except the display of the entire fin in glyph.
References
"NASA Advanced Supercomputing Division." Sample Curvilinear Mesh, CFD Datasets from NAS. N.p.,
n.d. Web. 24 Jan. 2014.
"Oliver Kreylos' Research and Development Homepage - Point-based Volume Visualization." Oliver
Kreylos' Research and Development Homepage - Point-based Volume Visualization. N.p., n.d. Web. 24
Jan. 2014.
"Tutorials for ParaView 3.8.1." ParaView 3.8.1 Tutorial. N.p., n.d. Web. 24 Jan. 2014.
"Using ParaView to Visualize Scientific Data (online Tutorial)." TechWeb RSS. N.p., n.d. Web. 24 Jan.
2014.
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