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.

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

The State of the Art in Flow Visualization: Dense and Texture-Based Techniques

Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio-temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods. Categories and Subject Descriptors (according to ACM CCS): I.3 [Computer Graphics]: visualization, flow visualization, computational flow visualizatio