filename     : Sch09.pdf
entry        : inproceedings
conference   : Visualization 2009
pages        : 1243-1250
year         : 2009
month        : November-December
title        : Predictor-Corrector Schemes for Visualization of Smoothed Particle Hydrodynamics Data
subtitle     :
author       : Benjamin Schindler, Raphael Fuchs, John Biddiscombe, Ronald Peikert
booktitle    : IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization 2009)
ISSN/ISBN    :
editor       : 
publisher    : IEEE Computer Society Press
publ.place   :
volume       : 15
issue        : 6
language     : english
keywords     : smoothed particle hydrodynamics, flow visualization, unsteady flow, feature extraction, vortex core lines.
abstract     :In this paper we present a method for vortex core line extraction which operates directly on the smoothed particle hydrodynamics
(SPH) representation and, by this, generates smoother and more (spatially and temporally) coherent results in an efficient
way. The underlying predictor-corrector scheme is general enough to be applied to other line-type features and it is extendable to
the extraction of surfaces such as isosurfaces or Lagrangian coherent structures. The proposed method exploits temporal coherence
to speed up computation for subsequent time steps. We show how the predictor-corrector formulation can be specialized for several
variants of vortex core line definitions including two recent unsteady extensions, and we contribute a theoretical and practical comparison
of these. In particular, we reveal a close relation between unsteady extensions of Fuchs et al. and Weinkauf et al. and we
give a proof of the Galilean invariance of the latter.
When visualizing SPH data, there is the possibility to use the same interpolation method for visualization as has been used for the
simulation. This is different from the case of finite volume simulation results, where it is not possible to recover from the results the
spatial interpolation that was used during the simulation. Such data are typically interpolated using the basic trilinear interpolant, and
if smoothness is required, some artificial processing is added. In SPH data, however, the smoothing kernels are specified from the
simulation, and they provide an exact and smooth interpolation of data or gradients at arbitrary points in the domain.