250
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Generation of Optimal Weight Values Based on the Recursive Monte Carlo Method for Use in Monte Carlo Deep Penetration Calculations

, , &
Pages 497-507 | Received 29 Nov 2022, Accepted 03 May 2023, Published online: 28 Jun 2023

References

  • D. B. PELOWITZ, A. J. FALLGREN, and G. E. MCMATH, “MCNP6 User’s Manual, Code Version 6.1.1beta,” LA-CP-14-00745, Los Alamos National Laboratory (2014).
  • X-5 MONTE CARLO TEAM, “MCNP—A General N-Particle Transport Code, Version 5, Volume I: Overview and Theory,” LA-UR-03-1987, Los Alamos National Laboratory (2003).
  • J. LI et al., “An Auto Importance Sampling Method for Deep Penetration Problems,” Prog. Nucl. Sci. Technol., 2, 732 (2011); http://dx.doi.org/10.15669/pnst.2.732.
  • T. E. BOOTH, “Sample Problem for Variance Reduction in MCNP,” LA-10363-MS, Los Alamos National Laboratory (1985).
  • J. S. HENDRICKS and C. N. CULBERTSON, “An Assessment of MCNP Weight Windows,” LA-UR-00-55, Los Alamos National Laboratory (2000).
  • J. E. HOOGENBOOM and D. LÉGRÁDY, “A Critical Review of the Weight Window Generator in MCNP,” Proc. Monte Carlo 2005 Topl. Mtg: The Monte Carlo Method—Versatility Unbounded in a Dynamic Computing World, Chattanooga, Tennessee, April 17–21, 2005.
  • S. W. MOSHER et al., “ADVANTG—An Automated Variance Reduction Parameter Generator,” ORNL/TM-2013/416, Rev. 1, Oak Ridge National Laboratory (2013).
  • J. SWEEZY et al., “Automated Variance Reduction for MCNP Using Deterministic Methods,” Radiat. Prot. Dosim., 116, 1–4, 508 (2005); http://dx.doi.org/10.1093/rpd/nci257.
  • A. HAGHIGHAT and J. C. WAGNER, “Monte Carlo Variance Reduction with Deterministic Importance Functions,” Prog. Nucl. Energy, 42, 1, 25 (2003); http://dx.doi.org/10.1016/S0149-1970(02)00002-1.
  • A. DAVIS and A. TURNER, “Comparison of Global Variance Reduction Techniques for Monte Carlo Radiation Transport Simulations of ITER,” Fusion Eng. Des., 86, 9–11, 2698 (2011); http://dx.doi.org/10.1016/j.fusengdes.2011.01.059.
  • Q. PAN and K. WANG, “An Adaptive Variance Reduction Algorithm Based on RMC Code for Solving Deep Penetration Problems,” Ann. Nucl. Energy, 128, 171 (2019); http://dx.doi.org/10.1016/j.anucene.2019.01.009.
  • J. KONHEISER et al., “Application of Different Nuclides in Retrospective Dosimetry,” J. ASTM Int., 9, 3 (2012); http://dx.doi.org/10.1520/JAI103925.
  • P. SAIDI, M. SADEGHI, and C. TENREIRO, “Variance Reduction of Monte Carlo Simulation in Nuclear Engineering Field,” Theory and Applications of Monte Carlo Simulations, pp. 153–172 (2013).
  • T. E. BOOTH, “Genesis of the Weight Window and the Weight Window Generator in MCNP—A Personal History,” LA-UR-06-5807, Los Alamos National Laboratory (2006).
  • M. GOLDSTEIN and E. GREENSPAN, “Recursive Monte Carlo Method for Estimating Importance Function Distributions in Deep-Penetration Problems,” Nucl. Sci. Eng., 76, 3, 308 (1980); http://dx.doi.org/10.13182/NSE80-A21321.
  • B. V. RAMANA, “Higher Engineering Mathematics,” Tata McGraw-Hill Education (2006).

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.