Unbiasedness and Optimization of Regional Weight Cancellation

TL;DR

This paper extends the exact regional weight cancellation method for Monte Carlo neutron transport simulations, removing previous limitations to improve efficiency in realistic continuous-energy problems, demonstrated on a reactor physics benchmark.

Contribution

It introduces an extended framework for regional weight cancellation that overcomes prior limitations, enabling more realistic and efficient neutron transport simulations.

Findings

Enhanced efficiency of weight cancellation in neutron transport

Successful application to a reactor physics benchmark

Overcoming previous methodological limitations

Abstract

The Monte Carlo method is often used to simulate systems which can be modeled by random walks. In order to calculate observables, in many implementations the "walkers" carry a statistical weight which is generally assumed to be positive. Some random walk simulations, however, may require walkers to have positive or negative weights: it has been shown that the presence of a mixture of positive and negative weights can impede the statistical convergence, and special weight-cancellation techniques must be adopted in order to overcome these issues. In a recent work we demonstrated the usefulness of one such method, exact regional weight cancellation, to solve eigenvalue problems in nuclear reactor physics in three spatial dimensions. The method previously exhibited had several limitations (including multi-group transport and isotropic scattering) and needed homogeneous cuboid cancellation regions. In this paper we lift the previous limitations, in view of applying exact regional cancellation to more realistic continuous-energy neutron transport problems. This extended regional cancellation framework is used to optimize the efficiency of the weight cancellation. Our findings are illustrated on a benchmark configuration for reactor physics.