Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Automated variance reduction for MCNP using deterministic methods.

J Sweezy1, F Brown, T Booth

  • 1X-5, Applied Physics Division, Los Alamos National Laboratory, MS F663, Los Alamos, NM 87545, USA. jsweezy@lanl.gov

Radiation Protection Dosimetry
|April 11, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Author Correction: Tree diversity is changing across tropical Andean and Amazonian forests in response to global change.

Nature ecology & evolution·2026
Same author

Tree diversity is changing across tropical Andean and Amazonian forests in response to global change.

Nature ecology & evolution·2026
Same author

Positive reinforcement for the dentally anxious child.

British dental journal·2023
Same author

Transcriptomic analysis reveals pronounced changes in gene expression due to sub-lethal pyrethroid exposure and ageing in insecticide resistance Anopheles coluzzii.

BMC genomics·2021
Same author

Achievements in genetic engineering and their influence on the control and prevention of animal diseases.

Revue scientifique et technique (International Office of Epizootics)·2020
Same author

Thirty years of biochemical work on foot and mouth disease virus at the Pirbright Institute.

Revue scientifique et technique (International Office of Epizootics)·2020
Same journal

Development of CaSO4: Dy-based ring badge for extremity dose monitoring of radiation workers in India.

Radiation protection dosimetry·2026
Same journal

A proposal for a differentiated radiation protection program for the decommissioning of nuclear power plants compared to the operation of nuclear power plants.

Radiation protection dosimetry·2026
Same journal

A three-dimensional neutron localization method based on double-scattering imaging and reconstruction algorithm.

Radiation protection dosimetry·2026
Same journal

Effect of 131I biodistribution on measurements using a scanning whole-body counter.

Radiation protection dosimetry·2026
Same journal

Activity concentration of 137Cs and natural radionuclides in soil around the Belarusian nuclear power plant in the pre-commissioning period.

Radiation protection dosimetry·2026
Same journal

Novel passive-adaptive exoskeleton-supported radiation protection equipment with enhanced shielding and reduced perceived weight.

Radiation protection dosimetry·2026
See all related articles

This study introduces automated variance reduction for MCNP Monte Carlo simulations, significantly cutting computation time for deep penetration problems by using deterministic methods to create weight windows.

Area of Science:

  • Nuclear Engineering
  • Computational Physics
  • Radiation Transport

Background:

  • Deep penetration problems in radiation transport simulations require significant computational resources.
  • Existing Monte Carlo methods, like MCNP, can be inefficient for deep penetration scenarios.
  • Deterministic methods offer an alternative for generating efficiency maps.

Purpose of the Study:

  • To develop an automated variance reduction capability for the MCNP Monte Carlo code.
  • To reduce user and computer time for solving deep penetration problems.
  • To integrate deterministic and Monte Carlo methods for enhanced efficiency.

Main Methods:

  • Developed an automated variance reduction capability for MCNP5.
  • Employed the PARTISN discrete ordinates code to generate mesh-based weight windows.

Related Experiment Videos

  • Translated MCNP geometry for PARTISN and utilized adjoint flux to create weight windows.
  • Biased MCNP source energy spectrum based on adjoint energy spectrum and incorporated angle-dependent weight windows.

    • Main Results:

    • Successfully implemented automated variance reduction for MCNP.
    • Demonstrated increased efficiency in solving deep penetration problems.
    • Enabled the use of deterministic adjoint flux for Monte Carlo weight window generation.
    • Facilitated source energy spectrum biasing and angle-dependent weight windows.

    Conclusions:

    • The developed automated variance reduction capability significantly enhances MCNP efficiency for deep penetration problems.
    • The integration of deterministic (PARTISN) and Monte Carlo (MCNP) methods provides a powerful approach for radiation transport simulations.
    • This method offers flexibility through source energy biasing and angle-dependent weight windows, reducing computational time.