Countering Neutron Clustering in Monte Carlo With a Neutron Source Injection

Abstract

Neutron clustering is a recently identified problem with Monte Carlo eigenvalue calculations which can produce significantly erroneous results. Previous work by Sutton & Mittal (2017) considered neutron clustering as a problem of maintaining ‘genetic diversity’ within the neutron population. This paper proposes reducing the extent of neutron clustering by replacing fission neutrons in the source bank with uncorrelated neutrons, sampled from a uniform source distribution – effectively adding new neutron genealogies to the population. The efficacy of the method is demonstrated on a number of simple problems, showing improved behaviour of the Shannon entropy and neutron centre-of-mass. Although currently limited in scope, this paper intends to provide a route to reducing clustering effects in more general problems.