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

Abstract

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.

Keywords:

• Monte Carlo
• deep penetration problem
• weight window
• recursive Monte Carlo

Acknowledgments

This project is funded by the Federal Ministry of Education and Research under contract number 15S9409A and supported by Preussen Elektra GmbH. The authors would like to thank Rafael Macián Juan for his assistance.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Bundesministerium für Bildung und Forschung [15S9409A].