The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2011 vol.17)
pp: 1862-1871
Patrick Moran , NASA Ames Research Center
David Ellsworth , Computer Sciences Corporation, NASA Ames Research Center
We present a new technique for providing interpolation within cell-centered Adaptive Mesh Refinement (AMR) data that achieves C^0 continuity throughout the 3D domain. Our technique improves on earlier work in that it does not require that adjacent patches differ by at most one refinement level. Our approach takes the dual of each mesh patch and generates "stitching cells" on the fly to fill the gaps between dual meshes. We demonstrate applications of our technique with data from Enzo, an AMR cosmological structure formation simulation code. We show ray-cast visualizations that include contributions from particle data (dark matter and stars, also output by Enzo) and gridded hydrodynamic data. We also show results from isosurface studies, including surfaces in regions where adjacent patches differ by more than one refinement level.
Adaptive mesh refinement, AMR, Enzo, interpolation, ray casting, isosurfaces, dual meshes, stitching cells.
Patrick Moran, David Ellsworth, "Visualization of AMR Data With Multi-Level Dual-Mesh Interpolation", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 1862-1871, Dec. 2011, doi:10.1109/TVCG.2011.252
[1] M. Berger and P. Colella, Local adaptive mesh refinement for shock hydrodynamics. Journal of Computational Physics, 82: 64–84, 1989.
[2] M. Berger and J. Oliger, Adaptive mesh refinement for hyperbolic partial differential equations. Journal of Computational Physics, 53: 484–512, 1984.
[3] G. L. Bryan, Fluids in the universe: adaptive mesh refinement in cosmology. Comput. Sci. Eng., 1 (2): 46–53, Apr. 1999.
[4] C. P. Gribble, T. Ize, A. Kensler, I. Wald, and S. G. Parker, A coherent grid traversal approach to visualizing particle-based simulation data. IEEE Transactions on Visualization and Computer Graphics, 13: 758– 768, 2007.
[5] M. R. Joung, R. Cen, and G. L. Bryan, Galaxy size problem at z = 3: Simulated galaxies are too small. The Astrophysical Journal Letters, 692 (1):L1, 2009.
[6] R. Kähler, Accelerated Volume Rendering on Structured Adaptive Meshes. PhD thesis, Zuse Institute Berlin (ZIB), 2005.
[7] R. Kähler, T. Abel, and H.-C. Hege, Simultaneous GPU-assisted ray-casting of unstructured point sets and volumetric grid data. In Proc. of IEEE/EG Symposium on Volume Graphics 2007, pages 49 – 56, 2007.
[8] R. Kähler, J. Wise, T. Abel, and H.-C. Hege, GPU-assisted raycasting for cosmological adaptive mesh refinement simulations. In Volume Graphics 2006. A K Peters Ltd., 2006.
[9] O. Kreylos, G. H. Weber, E. W. Bethel, J. M. Shalf, B. Hamann, and K. I. Joy, Remote interactive direct volume rendering of AMR data. Technical Report LBNL 49954, Lawrence Berkeley National Laboratory, 2002.
[10] M. Levoy, A hybrid ray tracer for rendering polygon and volume data. Computer Graphics and Applications, IEEE, 10 (2): 33 –40, Mar. 1990.
[11] W. E. Lorensen and H. E. Cline, Marching cubes: A high resolution 3D surface construction algorithm. In Computer Graphics (Proceedings of SIGGRAPH 87), volume 21, pages 163–169, July 1987.
[12] S. Marchesin and C. de Verdière, High-quality, semi-analytical volume rendering for AMR data. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1611 –1618, Nov.-Dec. 2009.
[13] N. Max, Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1 (2): 99–108, June 1995.
[14] J. J. Monaghan, Smoothed particle hydrodynamics. Annual Review of Astronomy and Astrophysics, 30 (1): 543–574, 1992.
[15] G. M. Nielson and B. Hamann, The asymptotic decider: Removing the ambiguity in marching cubes. In Visualization '91, pages 83–91, 1991.
[16] M. L. Norman, G. L. Bryan, R. Harkness, J. Bordner, D. Reynolds, B. O'Shea, and R. Wagner, Simulating Cosmological Evolution with Enzo. In D. Bader editor, , Petascale Computing: Algorithms and Applications. Chapman and Hall, 2007.
[17] M. L. Norman, J. M. Shalf, S. Levy, and G. Daues, Diving deep: Data management and visualization strategies for adaptive mesh refinement simulations. Computing in Science and Engineering, 1 (4): 36–47, July/August 1999.
[18] S. Park, C. L. Bajaj, and V. Siddavanahalli, Case study: Interactive rendering of adaptive mesh refinement data. In IEEE Visualization 2002, pages 521–524. IEEE Computer Society, 2002.
[19] M. J. Turk, B. D. Smith, J. S. Oishi, S. Skory, S. W. Skillman, T. Abel, and M. L. Norman, yt: A Multi-Code Analysis Toolkit for Astrophysical Simulation Data. The Astrophysical Journal Supplement, 192 (1), Jan. 2011.
[20] G. H. Weber, V. E. Beckner, H. Childs, T. J. Ligocki, M. C. Miller, B. V. Straalen, and E. W. Bethel, Visualization tools for adaptive mesh refinement data. In Proceedings of the 4th High-End Visualization Workshop, pages 18–22, 2007.
[21] G. H. Weber, O. Kreylos, T. J. Ligocki, J. M. Shalf, H. Hagen, B. Hamann, and K. I. Joy, Extraction of crack-free isosurfaces from adaptive mesh refinement data. In Data Visualization 2001: Proceedings of the Joint Eurographics - IEEE TVCG, pages 25–34, 335. Springer Verlag, May 2001.
[22] G. H. Weber, O. Kreylos, T. J. Ligocki, J. M. Shalf, H. Hagen, B. Hamann, K. I. Joy, and K.-L. Ma, High-quality volume rendering of adaptive mesh refinement data. In Vision, Modeling & Visualization, pages 121–128, 522, Nov. 2001.
22 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool