An effective initial particle sampling technique for Monte Carlo reactor transient simulations

TL;DR

This paper introduces a new initial particle sampling method for Monte Carlo reactor transient simulations that produces uniform-weight particles around target sizes, improving upon existing techniques.

Contribution

The paper presents a novel sampling technique that enhances initial particle distribution in Monte Carlo simulations, implemented in an open-source Python code.

Findings

Verified against homogeneous medium problem

Successfully applied to C5G7-TD benchmark

Produces uniform-weight particles effectively

Abstract

We propose a technique to effectively sample initial neutron and delayed neutron precursor particles for Monte Carlo (MC) simulations of typical off-critical reactor transients. The technique can be seen as an improvement, or alternative, to the existing ones. Similar to some existing techniques, the proposed sampling technique uses the standard MC criticality calculation. However, different from the others, the technique effectively produces uniform-weight particles around user-specified target sizes. The technique is implemented into the open-source Python-based MC code MC/DC and verified against an infinite homogeneous 361-group medium problem and the 3D C5G7-TD benchmark model.