Topological Feature Extraction for Comparison of Terascale Combustion Simulation Data

  • Ajith Mascarenhas
  • Ray W. Grout
  • Peer-Timo Bremer
  • Evatt R. Hawkes
  • Valerio Pascucci
  • Jacqueline H. Chen
Part of the Mathematics and Visualization book series (MATHVISUAL)


We describe a combinatorial streaming algorithm to extract features which identify regions of local intense rates of mixing in twoterascale turbulent combustion simulations. Our algorithm allows simulation data comprised of scalar fields represented on 728x896x512 or 2025x1600x400 grids to be processed on a single relatively lightweight machine. The turbulence-induced mixing governs the rate of reaction and hence is of principal interest in these combustion simulations. We use our feature extraction algorithm to compare two very different simulations and find that in both the thickness of the extracted features grows with decreasing turbulence intensity. Simultaneous consideration of results of applying the algorithm to the HO2 mass fraction field indicates that autoignition kernels near the base of a lifted flame tend not to overlap with the high mixing rate regions.


Direct Numerical Simulation Mixture Fraction Medial Axis Turbulent Combustion Scalar Dissipation Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    H. Carr, J. Snoeyink, and U. Axen. Computing contour trees in all dimensions. Comput. Geom. Theory Appl., 24(3):75–94, 2003.zbMATHMathSciNetGoogle Scholar
  2. 2.
    H. Carr, J. Snoeyink, and M. van de Panne. Simplifying flexible isosurfaces using local geometric measures. In IEEE Vis. ’04, 497–504. IEEE Comp. Society, 2004.Google Scholar
  3. 3.
    T. K. Dey and S. Goswami. Tight Cocone: A water-tight surface reconstructor. J. of Comp. Infor. Sci. Eng., 3:302–307, 2003.CrossRefGoogle Scholar
  4. 4.
    H. Edelsbrunner, J. Harer, and A. Zomorodian. Hierarchical Morse-Smale complexes forpiecewise linear 2-manifolds. Discrete Comput. Geom., 30:87–107, 2003.zbMATHMathSciNetGoogle Scholar
  5. 5.
    H. Edelsbrunner, J. Harer, V. Natarajan, and V. Pascucci. Morse-Smale complexes forpiecewise linear 3-manifolds. In Proc. 19th Sympos. Comput. Geom., 361–370, 2003.Google Scholar
  6. 6.
    I. Fujishiro, T. Azuma, and Y. Takeshima. Automating transfer function design for comprehensible volume rendering based on 3D field topology analysis. In Proc. IEEE Vis. ’99, 467–470, Oct. 25–29, 1999.Google Scholar
  7. 7.
    I. Fujishiro, Y. Takeshima, T. Azuma, and S. Takahashi. Volume data mining using 3d field topology analysis. IEEE Comp. Graphics and Applications, 20:46–51, 200.Google Scholar
  8. 8.
    R. W. Grout, E. R. Hawkes, J. H. Chen, A. Mascarenhas, P.-T. Bremer and V. Pascucci. 32 nd Int. Symp. on Combustion, WiPP05-23, 2008.Google Scholar
  9. 9.
    A. Gyulassy, M. Duchaineau, V. Natarajan, V. Pascucci, E.Bringa, A. Higginbotham, and B. Hamann. Topologically clean distance fields. IEEE Trans. on Comp. Graphics and Vis. (TVCG), 13(6):1432–1439, 2007.Google Scholar
  10. 10.
    A. Gyulassy, V. Natarajan, V. Pascucci, P.-T. Bremer, and B. Hamann. Topology-based simplification for feature extraction from 3D scalar fields. IEEE Trans. on Comp. Graphics and Vis. (TVCG), 12(4):474–484, 2006.Google Scholar
  11. 11.
    A. Gyulassy, V. Natarajan, V. Pascucci, and B. Hamann. Efficient computation of Morse-Smale complexes for three-dimensional scalar functions. IEEE Trans. on Comp. Graphics and Vis. (TVCG), 13(6):1440–1447, 2007.Google Scholar
  12. 12.
    E. R. Hawkes, R. Sankaran, J. C. Sutherland, and J. H. Chen. Proc. Comb. Inst., 31:1633–1640, 2007.CrossRefGoogle Scholar
  13. 13.
    M. Isenburg and P. Lindstrom. Streaming meshes. In Proceedings of the IEEE Vis. 2005 (VIS’05), 231–238. IEEE Comp. Society, 2005.Google Scholar
  14. 14.
    S. A. Kaiser and J. H. Frank. Proc. Combust. Inst., 31:1515–1523, 2007.CrossRefGoogle Scholar
  15. 15.
    A. Klimenko and R. W. Bilger. Prog. Energy Combust. Sci., 25(6):595–687, 1999.CrossRefGoogle Scholar
  16. 16.
    D. Laney, P.-T. Bremer, A. Mascarenhas, P. Miller, and V. Pascucci. Understanding the structure of the turbulent mixing layer in hydrodynamic instabilities. IEEE Trans. Vis. and Comp. Graphics (TVCG) / Proc.of IEEE Vis., 12(5):1052–1060, 2006.Google Scholar
  17. 17.
    N. Peters. Prog. Energy Combust. Sci., 10:319–339, 1984.CrossRefGoogle Scholar
  18. 18.
    S. B. Pope. Prog. Energy Combust. Sci., 11(2):119–192, 1985.CrossRefGoogle Scholar
  19. 19.
    G. Weber, G. Scheuermann, H. Hagen, and B. Hamann. Exploring scalar fields using critical isovalues. In M. Gross, K. I. Joy, and R. J. Moorhead, editors, Proc. IEEE Vis. ’02, 171–178, IEEE Comp. Society Press, 2002.Google Scholar
  20. 20.
    P. Vaishnavi, A. Kronenburg, and C. Pantano. J. Fluid Mech., 596:103–132, 2008.zbMATHCrossRefGoogle Scholar
  21. 21.
    M. J. van Kreveld, R. van Oostrum, C. L. Bajaj, V. Pascucci, and D. Schikore. Contour trees and small seed sets for isosurface traversal. In Symp. on Computational Geometry, 212–220, 1997.Google Scholar

Copyright information

© Springer Berlin Heidelberg 2011

Authors and Affiliations

  • Ajith Mascarenhas
    • 1
  • Ray W. Grout
    • 1
  • Peer-Timo Bremer
    • 2
  • Evatt R. Hawkes
    • 4
  • Valerio Pascucci
    • 3
  • Jacqueline H. Chen
    • 1
  1. 1.Sandia National LaboratoriesLivermoreUSA
  2. 2.Lawrence Livermore National LaboratoriesLivermoreUSA
  3. 3.University of UtahSalt Lake CityUSA
  4. 4.University of New South WalesSidneyAustralia

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