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Flow Vis

Visualization and Feature Extraction for Flow Fields

Flow fields play an important role in science and engineering. We have been developing new methods for the visual analysis of flow fields. Some of those methods are presented here.

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Hierachical Vortex Regions in Swirling Flow

A vortex region that is bounded by a line of constant intersection angle to the vector field.
We developed a new criterion to characterize hierarchical two-dimensional vortex regions induced by swirling motion. Central to the definition are closed loops that intersect the flow field at a constant angle. The union of loops belonging to the same area of swirling motion defines a vortex region. These regions are disjunct but may be nested, thus introducing a spatial hierarchy of vortex regions.

Dual Streamline Seeding

Streamlines of a vetor field (black) and the dual streamlines (blue).
Dual Streamline Seeding is a novel streamline seeding technique based on dual streamlines that are orthogonal to the flow field, instead of tangential. A greedy algorithm is applied to produce a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces.

Probabilistic Local Features in Uncertain Vector Fields with Spatial Correlation

Color coded probabilities for singularities in the wall shear stress vector field from a simulated cerebral aneurysm blood flow at a single simulation time step. The mean wall shear stress vector field is indicated by a low-contrast LIC visualization. Probabilities for the different critical point types are encoded by different colors: sinks in violet, sources in green and saddles in blue. Intensities are scaled by the probabilities. Colors are blended additively. Depicted are: (a) All critical points of the 9 ensemble members and (b) probabilities considering spatial correlations
We extended methods for the extraction of local features from crisp vector fields to uncertain fields. While in a crisp field local features are either present or absent at some location, in an uncertain field they are present with some probability. We model sampled uncertain vector fields by discrete Gaussian random fields with empirically estimated spatial correlations. The variability of the random fields in a spatial neighborhood is characterized by marginal distributions. Probabilities for the presence of local features are formulated in terms of low-dimensional integrals over such marginal distributions. Specifically, we define probabilistic equivalents for critical points and vortex cores. The probabilities are computed by Monte Carlo integration. For identification of critical points and cores of swirling motion we employ the Poincaré index and the criterion by Sujudi and Haimes. In contrast to previous global methods we take a local perspective and directly extract features in divergence-free fields as well. The method is able to detect saddle points in a straight forward way and works on various grid types. It is demonstrated by applying it to simulated unsteady flows of biofluid and climate dynamics.

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Duration

01/2008–