Hybrid Weight Window Method for Global Time-Dependent Monte Carlo Particle Transport Calculations

TL;DR

This paper introduces a hybrid Monte Carlo algorithm with global variance reduction for time-dependent particle transport, utilizing auxiliary hybrid MC/deterministic solutions to define weight windows for improved efficiency.

Contribution

The paper develops a novel hybrid MC/deterministic method that automatically defines weight windows using second-moment equations for enhanced variance reduction in time-dependent problems.

Findings

The hybrid method achieves more uniform particle distributions.

Performance depends on weight window parameters, with demonstrated efficiency gains.

Quantitative results show improved figure of merit and reduced errors.

Abstract

This paper presents a new Monte Carlo (MC) algorithm for time-dependent particle transport problems with global variance reduction based on automatic weight windows (WWs). The centers of WWs at a time step are defined by the solution of an auxiliary hybrid MC / deterministic problem formed by the low-order second-moment (LOSM) equations. The closures for the hybrid LOSM equations are calculated by the MC method. The LOSM equations are discretized by a scheme of the second-order accuracy in time and space. Filtering techniques are applied to reduce noise effects in the LOSM closures. The WWs defined with the auxiliary solution give rise to sufficiently uniform MC particle distribution in space on each time step. The algorithm is analyzed by means of an analytic transport benchmark. We study performance of the MC algorithm depending on a set parameters of WWs. Figure of merit and relative error results are presented, demonstrating the performance of the hybrid MC method and quantifying its computational efficiency.