Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Particle tracking acceleration via signed distance fields in direct-accelerated geometry Monte Carlo

  • Shriwise, Patrick C. (CNERG Research Group, University of Wisconsin - Madison) ;
  • Davis, Andrew (CNERG Research Group, University of Wisconsin - Madison) ;
  • Jacobson, Lucas J. (CNERG Research Group, University of Wisconsin - Madison) ;
  • Wilson, Paul P.H. (CNERG Research Group, University of Wisconsin - Madison)
  • Received : 2017.06.03
  • Accepted : 2017.08.07
  • Published : 2017.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

Computer-aided design (CAD)-based Monte Carlo radiation transport is of value to the nuclear engineering community for its ability to conduct transport on high-fidelity models of nuclear systems, but it is more computationally expensive than native geometry representations. This work describes the adaptation of a rendering data structure, the signed distance field, as a geometric query tool for accelerating CAD-based transport in the direct-accelerated geometry Monte Carlo toolkit. Demonstrations of its effectiveness are shown for several problems. The beginnings of a predictive model for the data structure's utilization based on various problem parameters is also introduced.

Keywords

References

  1. T.J. Tautges, P.P.H. Wilson, J. Kraftcheck, B.M. Smith, D.L. Henderson, Acceleration Techniques for Direct Use of CAD-based Geometries in Monte Carlo Radiation Transport, in International Conference on Mathematics, Computational Methods & Reactor Physics (M&C 2009), American Nuclear Society, Saratoga Springs, NY, May 2009.
  2. T.J. Tautges, R.Meyers, K. Merkley, C. Stimpson, C. Ernst, MOAB: a Mesh-oriented Database, SAND2004-1592, Sandia National Laboratories, Apr. 2004. report.
  3. B.M. Smith, Robust Tracking and Advanced Geometry for Monte Carlo Radiation Transport, PhD Nuclear Engineering and Engineering Physics, University of Wisconsin-Madison, Madison, WI, United States, 2011.
  4. P. Shriwise, A. Davis, P.P. Wilson, Leveraging Intel's Embree ray tracing in the DAGMC toolkit, in: Am. Nuc. Soc. Winter Meeting 2015, vol. 113, Nov 2015. Washington, DC, USA.
  5. H. Weghorst, G. Hooper, D.P. Greenberg, Improved computational methods for ray tracing, ACM Trans. Graph. 3 (1) (Jan. 1984) 52-69. https://doi.org/10.1145/357332.357335
  6. S. Gottschalk, M.C. Lin, D. Manocha, OBBTree: a hierarchical structure for rapid interference detection, in: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, ACM, 1996, pp. 171-180.
  7. J. O'rourke, Finding minimal enclosing boxes, Int. J. Comput. Inform. Sci. 14 (3) (1985) 183-199. https://doi.org/10.1007/BF00991005
  8. S. Osher, R.P. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Applied Mathematical Science, Springer, New York, N.Y., 2003.
  9. L.J. Tomczak, GPU Ray Marching of Distance Fields, Technical University of Denmark, 2012.
  10. X-5 MONTE CARLO TEAM, MCNP - a General Monte Carlo N-particle Transport Code, Version 5 - Volume III: Developers Guide, Tech. Rep. LA-CP-03-0284, Los Alamos National Laboratory, Jun. 2004.
  11. J.T. Goorley, M.R. James, et al., Initial MCNP6 Release Overview - MCNP6 Version 1.0, Tech. rep., jun 2013.
  12. A.S. Glassner (Ed.), An Introduction to Ray Tracing, Academic Press Ltd., London, UK, 1989.
  13. R. Setaluri, M. Aanjaneya, S. Bauer, E. Sifakis, SPGrid: a sparse paged grid structure applied to adaptive smoke simulation, ACM Trans. Graph. 33 (6) (Nov. 2014) 205:1-205:12.