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This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces 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.
Dual Streamline Seeding
(2009)
The analysis of data that captures volcanic eruptions and their atmospheric aftermath plays an important role for domain experts to gain a deeper understanding of the volcanic eruption and their consequences for atmosphere, climate and air traffic. Thereby, one major challenge is to extract and combine the essential information, which is spread over various, mostly sparse data sources. This requires a careful integration of each data set with its strength and limitations. The sparse, but more reliable measurement data is mainly used to calibrate the more dense simulation data. This work combines a collection of visualization approaches into an exploitative framework. The goal is to support the domain experts to build a complete picture of the situation. But it is also important to understand the individual data sources, the wealth of their information and the quality of the simulation results. All presented methods are designed for direct interaction with the data from different perspectives rather than the sole generation of some final images.
A framework is proposed for extracting features in 2D transient flows, based on the acceleration field to ensure Galilean invariance. The minima of the acceleration magnitude, i.e. a superset of the acceleration zeros, are extracted and discriminated into vortices and saddle points --- based on the spectral properties of the velocity Jacobian. The extraction of topological features is performed with purely combinatorial algorithms from discrete computational topology. The feature points are prioritized with persistence, as a physically meaningful importance measure. These features are tracked in time with a robust algorithm for tracking features. Thus a space-time hierarchy of the minima is built and vortex merging events are detected. The acceleration feature extraction strategy is applied to three two-dimensional shear flows:
(1) an incompressible periodic cylinder wake,
(2) an incompressible planar mixing layer and
(3) a weakly compressible planar jet.
The vortex-like acceleration feature points are shown to be well aligned with acceleration zeros, maxima of the vorticity magnitude, minima of pressure field and minima of λ2.
A framework is proposed for extracting features in 2D transient flows, based on the acceleration field to ensure Galilean invariance. The minima of the acceleration magnitude, i.e. a superset of the acceleration zeros, are extracted and discriminated into vortices and saddle points --- based on the spectral properties of the velocity Jacobian. The extraction of topological features is performed with purely combinatorial algorithms from discrete computational topology. The feature points are prioritized with persistence, as a physically meaningful importance measure. These features are tracked in time with a robust algorithm for tracking features. Thus a space-time hierarchy of the minima is built and vortex merging events are detected. The acceleration feature extraction strategy is applied to three two-dimensional shear flows: (1) an incompressible periodic cylinder wake, (2) an incompressible planar mixing layer and (3) a weakly compressible planar jet. The vortex-like acceleration feature points are shown to be well aligned with acceleration zeros, maxima of the vorticity magnitude, minima of pressure field and minima of λ2.