Turbulent flows are intrinsic to many processes in science and engineering, and efforts to elucidate the physics of turbulence are of critical importance to many fields. However, ongoing efforts to achieve a fundamental understanding of the mechanisms of turbulent flow are hindered by the difficulty of quantifying the complex, nonlinear interactions between individual eddies in these flows. The difficulty of this task is compounded by the lack of robust methods for accurately identifying individual eddy structures and characterizing their dynamic evolution and organization across multiple scales. In this paper we address this problem by proposing several novel approaches for more accurately segmenting individual eddy structures in turbulent flows.

The study of fluid flow turbulence has been an active area of research for over 100 years, mainly because of its technological importance to a vast number of applications. In recent times with the advent of supercomputers and new experimental imaging techniques, terabyte scale data sets are being generated, and hence storage as well as analysis of this data has become a major issue. In this chapter we outline a new approach to tackling these data sets which relies on selective data storage based on real-time feature extraction and utilizing data mining tools to aid in the discovery and analysis of the data. Visualization results are presented which highlight the type and number of spatially and temporally evolving coherent features that can be extracted from the data sets as well as other high level features.

The detection and tracking of coherent vortical structures in a turbulent compressible boundary layer is considered. Several vortex identification methods are evaluated. It is found that for this flow, the discriminant of the characteristic polynomial of the velocity gradient tensor is the most useful. It tends to highlight vortical structures that are located farther from the wall, and the feature detection is less sensitive to the level than the other methods evaluated. The flow fields are visualized using advanced volume rendering methods that allow large datasets to be analyzed efficiently. These methods have also been adapted to volume fill specific structures so that their evolution can be tracked in time. These approaches promise to make the analysis of large DNS datasets more efficient and quantitative.

This paper discusses techniques for extracting feature lines from three-dimensional unstructured grids. Our twin objectives are to facilitate the interactive manipulation of these typically very large and dense meshes, and to clarify the visualization of the solution data that accompanies them. We describe the perceptual importance of specific viewpoint-dependent and view-independent features, discuss the relative advantages and disadvantages of several alternative algorithms for identifying these features (taking into consideration both local and global criteria), and demonstrate the results of these methods on a variety of different data sets.

There are many applications that can benefit from the simultaneous display of multiple layers of data. The objective in these cases is to render the layered surfaces in a such way that the outer structures can be seen and seen through at the same time. This paper focuses on the particular application of radiation therapy treatment planning, in which physicians need to understand the three-dimensional distribution of radiation dose in the context of patient anatomy.
     We describe a promising technique for communicating the shape and position of the transparent skin surface while at the same time minimally occluding underlying isointensity dose surfaces and anatomical objects: adding a sparse, opaque texture comprised of a small set of carefully-chosen lines. We explain the perceptual motivation for explicitly drawing ridge and valley curves on a transparent surface, describe straightforward mathematical techniques for detecting and rendering these lines, and propose a small number of reasonably effective methods for selectively emphasizing the most perceptually relevant lines in the display.

A means is described of analyzing two- and three-dimensional images into a directed acyclic graph of visually sensible, coherent regions and of using this DAG as the basis for interactive object definition. The image analysis is in terms of the geometry of the intensity surface via a multiscale approach with a focus on symmetry properties about ridges. The image analysis method, a system for interactive object definition, and results of their use on two-dimensional images are reported.