TY - JOUR
T1 - Generation of Optimal Weight Values Based on the Recursive Monte Carlo Method for Use in Monte Carlo Deep Penetration Calculations
AU - Yadav, Pratibha
AU - Rachamin, Reuven
AU - Konheiser, Jörg
AU - Baier, Silvio
N1 - Publisher Copyright:
© 2023 American Nuclear Society.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - In nuclear engineering, Monte Carlo (MC) methods are commonly used for reactor analysis and radiation shielding problems. These methods are capable of dealing with both simple and complex system models with accuracy. The application of MC methods experiences challenges when dealing with the deep penetration problems that are typically encountered in radiation shielding cases. It is difficult to produce statistically reliable results due to poor particle sampling in the region of interest. Therefore, such calculations are performed by the Monte Carlo N-Particle Transport (MCNP) code in association with the weight window (WW) variance reduction technique, which increases the particle statistics in the desired tally region. However, for large problems, MCNP’s built-in weight window generator (WWG) produces zero WW parameters for tally regions located far from the source. To address this issue, the recursive Monte Carlo (RMC) method was proposed. This paper focuses on the RMC methodology and its implementation in the Helmholtz-Zentrum Dresden-Rossendorf’s (HZDR’s) in-house code TRAWEI, which is responsible for producing optimal zone weight parameters used for optimizing deep penetration MC calculations. In addition, this paper discusses the verification of the TRAWEI weight generator program to that of an existing MCNP WWG. The performance of TRAWEI-generated weight values is assessed using a handful of test cases involving two shield materials. Globally, the TRAWEI-generated weight values improved not only the statistical variance and computational efficiency of the MC run compared to the analog MCNP simulation but also those of the simulation with WW values generated by the standard MCNP WWG.
AB - In nuclear engineering, Monte Carlo (MC) methods are commonly used for reactor analysis and radiation shielding problems. These methods are capable of dealing with both simple and complex system models with accuracy. The application of MC methods experiences challenges when dealing with the deep penetration problems that are typically encountered in radiation shielding cases. It is difficult to produce statistically reliable results due to poor particle sampling in the region of interest. Therefore, such calculations are performed by the Monte Carlo N-Particle Transport (MCNP) code in association with the weight window (WW) variance reduction technique, which increases the particle statistics in the desired tally region. However, for large problems, MCNP’s built-in weight window generator (WWG) produces zero WW parameters for tally regions located far from the source. To address this issue, the recursive Monte Carlo (RMC) method was proposed. This paper focuses on the RMC methodology and its implementation in the Helmholtz-Zentrum Dresden-Rossendorf’s (HZDR’s) in-house code TRAWEI, which is responsible for producing optimal zone weight parameters used for optimizing deep penetration MC calculations. In addition, this paper discusses the verification of the TRAWEI weight generator program to that of an existing MCNP WWG. The performance of TRAWEI-generated weight values is assessed using a handful of test cases involving two shield materials. Globally, the TRAWEI-generated weight values improved not only the statistical variance and computational efficiency of the MC run compared to the analog MCNP simulation but also those of the simulation with WW values generated by the standard MCNP WWG.
KW - Monte Carlo
KW - deep penetration problem
KW - recursive Monte Carlo
KW - weight window
UR - http://www.scopus.com/inward/record.url?scp=85163606581&partnerID=8YFLogxK
U2 - 10.1080/00295639.2023.2211199
DO - 10.1080/00295639.2023.2211199
M3 - Article
AN - SCOPUS:85163606581
SN - 0029-5639
VL - 198
SP - 497
EP - 507
JO - Nuclear Science and Engineering
JF - Nuclear Science and Engineering
IS - 2
ER -