Issue
EPJ Nuclear Sci. Technol.
Volume 11, 2025
Status and advances of Monte Carlo codes for particle transport simulation
Article Number 5
Number of page(s) 8
DOI https://doi.org/10.1051/epjn/2024034
Published online 31 January 2025
  1. T.M. Pandya, S.R. Johnson, T.M. Evans, G.G. Davidson, S.P. Hamilton, A.T. Godfrey, Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code, J. Comput. Phys. 308, 239 (2016) [CrossRef] [Google Scholar]
  2. W.A. Wieselquist, R.A. Lefebvre, SCALE 6.3.2 User Manual (Oak Ridge National Laboratory, Oak Ridge, TN, USA, 2024) ORNL/TM-2024/3386 [Google Scholar]
  3. S.P. Hamilton, T.M. Evans, Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code, Ann. Nucl. Energy 128, 236 (2019) [CrossRef] [Google Scholar]
  4. E. Merzari, S. Hamilton, T. Evans, M. Min, P. Fischer, S. Kerkemeier et al., Exascale multiphysics nuclear reactor simulations for advanced designs, in International Conference for High Performance Computing, Networking, Storage and Analysis (Denver, CO, USA, 2023), p. 1 [Google Scholar]
  5. NuScale Power, LLC, About Us; 2023, https://www.nuscalepower.com/en/about [Google Scholar]
  6. K. Smith, NuScale Small Modular Reactor (SMR) Progression Problems for the ExaSMR Project, Exascale Computing Project; 2017, WBS 1.2.1.08 ECP-SE-08-43 [Google Scholar]
  7. Oak Ridge Leadership Computing Facility, Summit: Oak Ridge National Laboratory’s 200 petaflop supercomputer; 2023, https://www.olcf.ornl.gov/olcf-resources/compute-systems/summit [Google Scholar]
  8. Oak Ridge Leadership Computing Facility, Frontier; 2023, https://www.olcf.ornl.gov/olcf-resources/compute-systems/frontier [Google Scholar]
  9. S.R. Johnson, T.M. Evans, G.G. Davidson, S.P. Hamilton, T.M. Pandya, K.E. Royston et al. Omnibus User Manual (Oak Ridge National Laboratory, Oak Ridge, TN, USA, 2020), ORNL/TM-2018/1073 [CrossRef] [Google Scholar]
  10. J.A. Ellis, T.M. Evans, S.P. Hamilton, C.T. Kelley, T.M. Pandya, Optimization of processor allocation for domain decomposed Monte Carlo calculations, Parallel Comput. 87, 77 (2019) [CrossRef] [Google Scholar]
  11. S.P. Hamilton, T.M. Evans, K.E. Royston, E.D. Biondo, Domain decomposition in the GPU-accelerated Shift Monte Carlo code, Ann. Nucl. Energy 166, 108687 (2022) [CrossRef] [Google Scholar]
  12. C. Josey, P. Ducru, B. Forget, K. Smith, Windowed multipole for cross section Doppler broadening, J. Comput. Phys. 307, 715 (2016) [CrossRef] [Google Scholar]
  13. R.N. Hwang, A rigorous pole representation of multilevel cross sections and its practical applications, Nucl. Sci. Eng. 96, 192 (1987) [CrossRef] [Google Scholar]
  14. E.P. Wigner, L. Eisenbud, Higher angular momenta and long range interaction in resonance reactions, Phys. Rev. 72, 29 (1947) [CrossRef] [Google Scholar]
  15. B. Forget, J. Yu, G. Ridley, Performance improvements of the windowed multipole formalism using a rational fraction approximation of the Faddeeva function, in International Conference on Physics of Reactors (Pittsburgh, PA, USA, 2022), p. 1963 [Google Scholar]
  16. J. Yu, wmp-endfbvii.1, 2022, Commit: 7887b3144cc579c4a449c2d82ba781ae93617da3, https://www.github.com/jiankai-yu/wmp-endfbvii.1 [Google Scholar]
  17. B. Becker, R. Dagan, G. Lohnert, Proof and implementation of the stochastic formula for ideal gas, energy dependent scattering kernel, Ann. Nucl. Energy 36, 470(2009) [CrossRef] [Google Scholar]
  18. N. Choi, H.G. Joo, Relative speed tabulation method for efficient treatment of resonance scattering in GPU-based Monte Carlo neutron transport calculation, Nucl. Sci. Eng. 195, 954 (2021) [CrossRef] [Google Scholar]
  19. S.R. Johnson, R. Lefebvre, K. Bekar, ORANGE: Oak Ridge Advanced Nested Geometry Engine (Oak Ridge National Laboratory, 2023), ORNL/TM-2023/3190 [Google Scholar]
  20. E. Biondo, T. Evans, S. Johnson, S. Hamilton, Comparison of nested geometry treatments within GPU-based Monte Carlo neutron transport simulations of fission reactors, International Journal of High Performance Computing Applications, 2024, Submitted March, 2024 [Google Scholar]
  21. C. Wächter, A. Keller, Instant ray tracing: The bounding interval hierarchy, in Symposium on Rendering, edited by T. Akenine-Moeller, W. Heidrich, The Eurographics Association, (2006), pp. 139–49 [Google Scholar]
  22. C. Lee, Y.S. Jung, Z. Zhong, J. Ortensi, V. Laboure, Y. Wang et al. Assessment of the Griffin Reactor Multiphysics Application Using the Empire Micro Reactor Design Concept (Argonne National Laboratory and Idaho National Laboratory, 2020), ANL/NSE-20/23 and INL/LTD-20-59263 [CrossRef] [Google Scholar]
  23. C. Matthews, V. Laboure, M. DeHart, J. Hansel, D. Andrs, Y. Wang et al., Coupled multiphysics simulations of heat pipe microreactors using DireWolf, Nucl. Technol. 207, 1142 (2021) [CrossRef] [Google Scholar]
  24. A. Haghighat, J.C. Wagner, Monte Carlo variance reduction with deterministic importance functions, Prog. Nucl. Energy 42, 25 (2003) [CrossRef] [Google Scholar]
  25. J. Wagner, D. Peplow, S. Mosher, FW-CADIS method for global and regional variance reduction of Monte Carlo radiation transport calculations, Nucl. Sci. Eng. 176, 37 (2014) [CrossRef] [Google Scholar]
  26. T.M. Evans, A.S. Stafford, R.N. Slaybaugh, K.T. Clarno, Denovo: A new three-dimensional parallel discrete ordinates code in SCALE, Nucl. Technol. 171, 171 (2010) [CrossRef] [Google Scholar]
  27. T.E. Booth, A Sample Problem for Variance Reduction in MCNP (Los Alamos National Laboratory, 1985), LA-10363-MS [Google Scholar]
  28. K. Royston, T. Evans, S.P. Hamilton, G. Davidson, Weight window variance reduction on GPUs in the Shift Monte Carlo Code, in International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (Niagara Falls, ON, Canada, 2023), p. 1 [Google Scholar]
  29. E.S. Gonzalez, G.G. Davidson, Choosing transport events for initiating splitting and rouletting, J. Nucl. Eng. 2, 97 (2021) [CrossRef] [Google Scholar]
  30. G.G. Davidson et al., Nuclide depletion capabilities in the Shift Monte Carlo code, Ann. Nucl. Energy 114, 259 (2018) [CrossRef] [Google Scholar]
  31. J.L. Salcedo-Pérez, B. Forget, K. Smith, P. Romano, Hybrid tallies to improve performance in depletion Monte Carlo simulations, in International Conference on Mathematics & Computational Methods Applied to Nuclear Science and Engineering (Portland, OR, USA, 2019), p. 927 [Google Scholar]
  32. E.E. Lewis, M.A. Smith, N. Tsoulfanidis, G. Palmiotti, T.A. Taiwo, R.N. Blomquist, Benchmark specification for Deterministic 2-D/3-D MOX fuel assembly transport calculations without spatial homogenization (C5G7 MOX) (Nuclear Energy Agency and Nuclear Science Committee, 2001), NEA/NSC/DOC(2001)4 [Google Scholar]
  33. J. Brown, C. Celik, T. Evans, B. Jeon, R. Lefebvre, J. McDonnell et al., Photonuclear Physics in SCALE (Oak Ridge National Laboratory, Oak Ridge, TN, USA, 2023), ORNL/TM-2023/3201 [Google Scholar]
  34. D.B. Pelowitz, MCNP6 User’s Manual Version 1.0 (Los Alamos National Laboratory, 2013), LA-CFP-13-00634 Rev 0 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.