Some Considerations in Devising Effective SCALE6/MAVRIC Models for Large Shielding Applications

*Analog Monte Carlo simulations for deep-penetration shielding problems are not viable; instead, aggressive use of variance reduction methods is used aiming to achieve acceptable accuracy for the result of interest in acceptable time. This requires developing effective variance reduction parameters, which in turn requires a systematic approach and an automated method. Frequently, a hybrid deterministic-stochastic methodology is deployed, where a deterministic transport theory method is used to generate adjoint function distribution. In the FW-CADIS method implemented in MAVRIC sequence of the SCALE6 package, deterministic forward calculation is performed as well, to enable generating variance reduction parameters for Monte Carlo simulation resulting in nearly-uniform statistical uncertainty over a large region. The choice of the deterministic model is user-controlled. However, the accuracy of the forward and adjoint deterministic solutions impacts the quality of Monte Carlo variance reduction parameters. We investigate this problem on a complex real-life shielding problem. The objective is to determine the radiation field (dose rate) throughout a nuclear power plant building. The large problem size (about 50x50x50 m 3 ), is further complicated by attenuation of more than 15 orders of magnitude. The results will serve as the initial step toward developing a general methodology for performance optimization of hybrid shielding calculations.