Vector field visualization with streamlines

We have recently developed an algorithm for vector field visualization with oriented streamlines, able to depict the flow directions everywhere in a dense vector field and the sense of the local orientations. The algorithm has useful applications in the visualization of the director field in nematic liquid crystals. Here we propose an improvement of the algorithm able to enhance the visualization of the local magnitude of the field. This new approach of the algorithm is compared with the same procedure applied to the Line Integral Convolution (LIC) visualization.Comment: 9 pges, 7 figure

Comparison of CFD visualization techniques in virtual reality

Many analysis tools are available that can help provide students with a detailed understanding of engineering systems and equipment. This is particularly true when the output from these tools is displayed in a virtual environment (VE) enabling the students to see the results in a natural user-centered environment. To obtain the greatest teaching advantage, educators need to know which of the available formats students best understand. This thesis compares different techniques for visualizing computational fluid dynamics (CFD) data in a VE for the education of technicians and engineers. CFD data has both vector and scalar components, and two standards of visualization are compared for each component. The ability of students to analyze and interpret these types of three-dimensional data sets within a VE is measured. Vector fields are compared to streamlines for the visualization of vector data, and contour plots are compared to isosurfaces for the visualization of scalar data. These comparisons are made for two different groups of student volunteers, one composed of typical community college technical students and the other composed of juniors/seniors in mechanical engineering. The mechanical engineering group showed no preference between the vector field and streamlines methods when analyzing vector data, but the community college technical students showed a strong preference for the streamlines method. This indicates that students who have a formal fluid mechanics education understand each method equally, while those without one are able to understand CFD data better using streamlines. Both groups showed a strong preference for the contours method over the isosurfaces method when analyzing scalar data

EXTRACTING FLOW FEATURES USING BAG-OF-FEATURES AND SUPERVISED LEARNING TECHNIQUES

Measuring the similarity between two streamlines is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying and streamline clustering. This dissertation presents a novel streamline similarity measure inspired by the bag-of-features concept from computer vision. Different from other streamline similarity measures, the proposed one considers both the distribution of and the distances among features along a streamline. The proposed measure is tested in two common tasks in vector field exploration: streamline similarity query and streamline clustering. Compared with a recent streamline similarity measure, the proposed measure allows users to see the interesting features more clearly in a complicated vector field. In addition to focusing on similar streamlines through streamline similarity query or clustering, users sometimes want to group and see similar features from different streamlines. For example, it is useful to find all the spirals contained in different streamlines and present them to users. To this end, this dissertation proposes to segment each streamline into different features. This problem has not been studied extensively in flow visualization. For instance, many flow feature extraction techniques segment streamline based on simple heuristics such as accumulative curvature or arc length, and, as a result, the segments they found usually do not directly correspond to complete flow features. This dissertation proposes a machine learning-based streamline segmentation algorithm to segment each streamline into distinct features. It is shown that the proposed method can locate interesting features (e.g., a spiral in a streamline) more accurately than some other flow feature extraction methods. Since streamlines are space curves, the proposed method also serves as a general curve segmentation method and may be applied in other fields such as computer vision. Besides flow visualization, a pedagogical visualization tool DTEvisual for teaching access control is also discussed in this dissertation. Domain Type Enforcement (DTE) is a powerful abstraction for teaching students about modern models of access control in operating systems. With DTEvisual, students have an environment for visualizing a DTE-based policy using graphs, visually modifying the policy, and animating the common DTE queries in real time. A user study of DTEvisual suggests that the tool is helpful for students to understand DTE

Interactive 3D Flow Visualization Using a Streamrunner

Flow visualization in 3D is challenging due to perceptual problems such as occlusion, lack of directional cues, lack of depth cues, and visual complexity. In this paper we present an interaction technique that addresses these special problems for 3D flow visualization. The feature we present, a streamrunner, gives the user interactive control over the evolution of streamlines from the time they are seeds until they reach their full length. The interactive streamrunner control minimizes occlusion and visual complexity and maximizes directional and depth cues for 3D flow visualization. Combined with our other interactive 3D flow visualization tools, the streamrunner gives a brand new level of control to the user investigating the vector field

Visualizing Magnitude and Direction in Flow Fields

In weather visualizations, it is common to see vector data represented by glyphs placed on grids. The glyphs either do not encode magnitude in readable steps, or have designs that interfere with the data. The grids form strong but irrelevant patterns. Directional, quantitative glyphs bent along streamlines are more effective for visualizing flow patterns. With the goal of improving the perception of flow patterns in weather forecasts, we designed and evaluated two variations on a glyph commonly used to encode wind speed and direction in weather visualizations. We tested the ability of subjects to determine wind direction and speed: the results show the new designs are superior to the traditional. In a second study we designed and evaluated new methods for representing modeled wave data using similar streamline-based designs. We asked subjects to rate the marine weather visualizations: the results revealed a preference for some of the new designs

A Phase Field Model for Continuous Clustering on Vector Fields

A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hilliard model, which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns-the actual clustering-during which the underlying simulation data specifies preferable pattern boundaries. We introduce specific physical quantities in the simulation to control the shape, orientation and distribution of the clusters as a function of the underlying flow field. In addition, the model is expanded, involving elastic effects. In the early stages of the evolution shear layer type representation of the flow field can thereby be generated, whereas, for later stages, the distribution of clusters can be influenced. Furthermore, we incorporate upwind ideas to give the clusters an oriented drop-shaped appearance. Here, we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries. However, the method also carries provisions for other fields as well. The clusters can be displayed directly as a flow texture. Alternatively, the clusters can be visualized by iconic representations, which are positioned by using a skeletonization algorithm.