Visualisation of complex flows using texture-based techniques

Authors

  • David James Warne The Queensland University of Technology
  • J. Young High Performance Computing and Research Support, Queensland University of Technology, Queensland 4001
  • N. A. Kelson High Performance Computing and Research Support, Queensland University of Technology, Queensland 4001

DOI:

https://doi.org/10.21914/anziamj.v54i0.6315

Keywords:

Vector Visualisation, Texture-based Techniques, Line integral Convolution, Image Based Flow Visualisation, Computational Fluid Dynamics

Abstract

Detailed representations of complex flow datasets are often difficult to generate using traditional vector visualisation techniques such as arrow plots and streamlines. This is particularly true when the flow regime changes in time. Texture based techniques, which are based on the advection of dense textures, are novel techniques for visualising such flows. We review two popular texture based techniques and their application to flow datasets sourced from active research projects. The techniques investigated were Line integral convolution [Cabral and Leedom, SIGGRAPH'93, pp.263--270, 1993], and Image based flow visualisation [van Wijk, SIGGRAPH'02, pp.745--754, 2002]. We evaluate these and report on their effectiveness from a visualisation perspective. We also report on their ease of implementation and computational overheads. References
  • B. Cabral and L. C. Leedom. Imaging vector fields using line integral convolution. In SIGGRAPH'93, pages 263--270, 1993. doi:10.1145/166117.166151
  • B. Cumming, T. Moroney, and I. Turner. A mass-conservative control volume-finite element method for solving Richards' equation in heterogeneous porous media. BIT Numer. Math., 51(2):845--864, 2011. doi:10.1007/s10543-011-0335-3
  • H.-J. G. Diersch and O. Kolditz. Variable-density flow and transport in porous media: approaches and challenges. Advances in Water Resources, 25:899--944, 2002. doi:10.1016/S0309-1708(02)00063-5
  • C. D. Hansen and C. R. Johnson. The visualization handbook. Elsevier Butterworth-Heinemann, Burlington, MA, 2005.
  • B. Jobard, G. Erlebacher, and M. Yousuff Hussaini. Lagrangian-Eulerian advection of noise and dye textures for unsteady flow visualization. IEEE Trans. Visualization and Computer Graphics, 8(3):211--222, 2002. doi:10.1109/TVCG.2002.1021575
  • P. R. Keller and M. M. Keller. Visual cues: practical data visualization. IEEE Computer Society Press, Los Alamitos, CA, 1993.
  • D. H. Laidlaw, R. M. Kirby, J. S. Davidson, T. S. Miller, M. da Silva, W. H. Warren, and M. Tarr. Quantitative comparative evaluation of 2D vector field visualization methods. In Proc. IEEE Conf. Visualization '01, pages 143--150, 2001. doi:10.1109/VISUAL.2001.964505
  • R. S. Laramee, G. Erlebacher, C. Garth, T. Schafhitzel, H. Theisel, X. Tricoche, T. Weinkauf, and D. Weiskopf. Applications of texture-based flow visualization. Engineering Applications of Computational Fluid Mechanics, 2(3):264--274, 2008. http://www.mpi-inf.mpg.de/ weinkauf/publications/abslaramee08.html
  • R. S. Laramee, H. Hauser, H. Doleisch, B. Vrolijk, F. H. Post, and D. Weiskopf. The state of the art in flow visualisation: dense and texture-based techniques. Computer Graphics Forum, 23(2):203--221, 2004. doi:10.1111/j.1467-8659.2004.00753.x
  • G. Larsen. Modelling hydrodynamic processes within Pumicestone Passage, northern Moreton Bay, Queensland. Master's thesis, Queensland University of Technology, School of Natural Resource Sciences, 2007. http://eprints.qut.edu.au/16634/
  • J. T. Madhani, J. Young, and R. J. Brown. Image based flow visualisation of experimental flow fields inside a gross pollutant trap. In AFMC 2012, 2012. http://www.afms.org.au/conference/18/174 - Brown.pdf
  • J. T. Madhani, J. Young, N. A. Kelson, and R. J. Brown. A novel method to capture and analyze flow in a gross pollutant trap using image-based vector visualization. Water Air Soil Pollut.: Focus, 9:357--369, 2009. doi:10.1007/s11267-009-9225-y
  • F. H. Post, B. Vrolijk, H. Hauser, R. S. Laramee, and H. Doleisch. Feature extraction and visualisation of flow fields. In Eurographics 2002 State of the Art Reports, pages 69--100, 2002. http://diglib.eg.org/EG/DL/Conf/EG2002/STARs/S4_FlowVis_Post.pdf
  • F. H. Post, B. Vrolijk, H. Hauser, R. S. Laramee, and H. Doleisch. The state of the art in flow visualisation: feature extraction and tracking. Computer Graphics Forum, 22(4):775--792, 2003. doi:10.1111/j.1467-8659.2003.00723.x
  • A. Telea. Data visualization: principles and practice. A K Peters, Ltd, Wellesley, MA, 2008.
  • T. Theoharis, G. Papaioannou, N. Platis, and N. M. Patrikalakis. Graphics and visualization: principles and algorithms. A K Peters, Ltd, Wellesley, MA, 2008.
  • E. R. Tufte. The visual display of quantitative information. Graphics Press, Cheshire, Conn, 2001.
  • J. J. van Wijk. Image based flow visualization. In SIGGRAPH'02, pages 745--754, 2002. doi:10.1145/566570.566646
  • J. J. van Wijk. Views on visualization. IEEE Trans. Visualization and Computer Graphics, 12(4):421--432, 2006. doi:10.1109/TVCG.2006.80
  • D. Weiskopf. GPU-based interactive visualization techniques. Springer-Verlag, Berlin, Heidelberg, 2007.

Author Biography

David James Warne, The Queensland University of Technology

High Performance Computing and Research Support

Published

2013-03-07

Issue

Section

Proceedings Computational Techniques and Applications Conference