Generation of optimal weight values based on the Recursive Monte Carlo (RMC) method for using in MC deep penetrations calculations

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 Monte Carlo methods experiences challenges when dealing with the deep penetration problem that is 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, the MCNP’s built-in weight window generator produces zero weight window parameters for the 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 HZDR’s in-house code (TRAWEI), which is responsible for producing optimal zone weight parameters used for optimizing the deep penetration MC calculations. In addition, this paper discusses the verification of the TRAWEI weight generator program to that of an existing MCNP weight window generator. 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 the statistical variance and computational efficiency of the Monte Carlo run compared to the analog MCNP simulation, and that of the simulation with weight window values generated by the standard MCNP weight window generator (WWG).