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Automated variance reduction for MCNP using deterministic methods
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
Summary
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.
- 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.
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