Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Collapsing flow topology using area metrics
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Continuous topology simplification of planar vector fields
Proceedings of the conference on Visualization '01
Geometric verification of swirling features in flow fields
Proceedings of the conference on Visualization '02
Discrete multiscale vector field decomposition
ACM SIGGRAPH 2003 Papers
Combining Topological Simplification and Topology Preserving Compression for 2D Vector Fields
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities
IEEE Transactions on Visualization and Computer Graphics
Computation of Localized Flow for Steady and Unsteady Vector Fields and Its Applications
IEEE Transactions on Visualization and Computer Graphics
Efficient Visualization of Lagrangian Coherent Structures by Filtered AMR Ridge Extraction
IEEE Transactions on Visualization and Computer Graphics
Efficient Computation and Visualization of Coherent Structures in Fluid Flow Applications
IEEE Transactions on Visualization and Computer Graphics
A Delaunay Simplification Algorithm for Vector Fields
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
Variational multiscale turbulence modelling in a high order spectral element method
Journal of Computational Physics
Meshless Helmholtz-Hodge Decomposition
IEEE Transactions on Visualization and Computer Graphics
Fast Combinatorial Vector Field Topology
IEEE Transactions on Visualization and Computer Graphics
Vortex and Strain Skeletons in Eulerian and Lagrangian Frames
IEEE Transactions on Visualization and Computer Graphics
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In the visualization of flow simulation data, feature detectors often tend to result in overly rich response, making some sort of filtering or simplification necessary to convey meaningful images. In this paper we present an approach that builds upon a decomposition of the flow field according to dynamical importance of different scales of motion energy. Focusing on the high-energy scales leads to a reduction of the flow field while retaining the underlying physical process. The presented method acknowledges the intrinsic structures of the flow according to its energy and therefore allows to focus on the energetically most interesting aspects of the flow. Our analysis shows that this approach can be used for methods based on both local feature extraction and particle integration and we provide a discussion of the error caused by the approximation. Finally, we illustrate the use of the proposed approach for both a local and a global feature detector and in the context of numerical flow simulations.