Hybrid Monte Carlo-Deterministic Methods for Reactor Analysis

Abstract

A new variant of a hybrid Monte Carlo-deterministic approach for simulating particle transport problems is presented and compared to the SCALE FW-CADIS approach. The new approach, denoted as the SUBSPACE approach, improves the selection of the importance maps in order to reduce the computational overhead required to achieve global variance reduction - that is, the uniform reduction of variance everywhere in the phase-space. The intended applications are reactor analysis problems where detailed responses for all fuel assemblies are required everywhere in the reactor core. Like FW-CADIS, the SUBSPACE approach utilizes importance maps obtained from deterministic adjoint models to derive automatic weight-window biasing. Unlike FW-CADIS, the SUBSPACE approach does not employ flux-based weighting of the adjoint source term. Instead, it utilizes pseudoresponses generated with random weights to help identify the correlations between the importance maps that could be used to reduce the computational time required for global variance reduction. Numerical experiments, serving as proof of principle, are presented to compare the SUBSPACE and FW-CADIS approaches in terms of the global reduction in standard deviation and the associated figures of merit for representative nuclear reactor assembly and core models.