Tracking and Visualizing Turbulent
Deborah E. Silver, Xin Wang · 1997
Visualizing 3D time-varying fluid datasets is difficult because of the immense amount of data to be processed and understood. These datasets contain many evolving amorphous regions, and it is difficult to observe patterns and visually follow regions of interest. In this paper, we present a technique which isolates and tracks full volume representations of regions of interest from 3D regular and curvilinear Computational Fluid Dynamics datasets. Connected voxel regions, features, are extracted from each time step and matched to features in subsequent time steps. Spatial overlap is used to determine matching. The features from each time step are stored in octree forests to speed the matching process. Once features are identified and tracked, properties of the features and their evolutionary history can be computed. This information can be used to enhance isosurface visualization and volume rendering by color coding individual regions. We demonstrate the algorithm on four 3D time-varying simulations from ongoing research in Computational Fluid Dynamics and show how tracking can significantly improve and facilitate the processing of massive datasets. Index Terms—Scientific visualization, multidimensional visualization, feature tracking, computer vision, CFD, isosurfaces, volume rendering. —————————— ✦ —————————— 1I NTRODUCTION IME varying simulations and observations are commonly used to study the evolution of different physical phe- nomena. Once the evolution is captured, the scientist can attempt to understand the underlying cause and build pre- dictive models. For example, meteorologists track cloud for- mations for weather predictions and hurricane warnings, environmentalist study the change in the ozone hole for knowledge about the greenhouse effect, and aeronautic engi- neers look at the movement of air over an airplane for better aircraft design and control. Visualization techniques can help provide the scientist with tools to highlight the evolutionary pattern of these phenomena. To be useful, these tools must allow the user to extract regions, classify and visualize them, abstract them for simplified representations, and track their evolution. Coherent regions are easily recognizable when the datasets are viewed, because they are localized in space and persist over finite intervals of time. However, standard visu- alization techniques do not provide any methods to compute or display the correspondence between datasets from the