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.