Hybrid Weight Window Method for Global Time-Dependent Monte Carlo Particle Transport Calculations
This work addresses variance reduction in global time-dependent particle transport calculations, which is an incremental advancement for computational physics and nuclear engineering applications.
The paper tackles the challenge of achieving uniform particle distribution in time-dependent Monte Carlo particle transport simulations by introducing a hybrid weight window method based on low-order second-moment equations, resulting in improved computational efficiency as demonstrated by figure of merit and relative error metrics.
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.