Application of Variational Variance Reduction for Source-Detector Problems in Nuclear Non-proliferation

Abstract

ABSTRACTThe VVR (Variational Variance Reduction) method has been tested extensively on criticality problems and has proven to be effective, but its application for shielding problems has been limited. In this work, the VVR method is tested on source-detector type problems that are of interest in nonproliferation applications. A 3D multigroup Monte Carlo code that uses parts of the framework of Attila, a production deterministic code for radiation transport problems, was developed to prototype hybrid (Deterministic/Monte Carlo) variance reduction techniques. The VVR method is applied to the forward Monte Carlo simulations based on a deterministically calculated adjoint solution using Attila. The performance of the VVR method when applied with 3 different configurations of the forward simulation are tested: (1) Monte Carlo calculation with implicit absorption, (2) Monte Carlo calculation with the Consistent Adjoint Driven Importance Sampling (CADIS) based weight windows method and (3) Monte Carlo calculation with the Local Importance Functional Transform (LIFT) method. The simulations were performed on 2 benchmark test problems for nonproliferation applications, the “Mulch Box” photon test problem and the PANDA neutron test problem. The angular domain was discretized for efficient evaluation of the VVR functional using a discrete ordinates based approach and a spherical harmonics based approach. For the mulch test problem, the VVR method provided only marginal improvements in the figure of merit (FOM) when applied to the Monte Carlo calculation with only implicit absorption, but provided up to two times improvement in FOM when applied with the CADIS based weight windows method. For the PANDA neutron problem, VVR method did not provide improvements in FOM due to the highly scattering nature of the problem. It was also observed that the VVR method when combined with the LIFT method is detrimental to the overall performance.