Investigation of novel weight window methods in Serpent 2 for fusion neutronics applications

A. Valentine (Corresponding Author), R. Worrall, J. Leppänen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Released in 2009, the Serpent Monte Carlo code has established itself as a highly efficient and powerful simulation code for nuclear systems analysis. Originally developed for reactor physics applications, the scope of the code now extends to coupled multi-physics simulations and photon transport. The latter has allowed adoption of the code by the fusion neutronics community following developments of a coupled neutron-photon capability in 2014 and the ability to handle complex geometry types in 2016. The code is well validated for the energy regimes and geometry types one can expect in fission reactor analysis. Over the course of recent years a benchmarking effort has been undertaken for application of the code to nuclear fusion. Compared to nuclear fission, or accelerator based applications, the underlying particle interaction phenomena differ greatly at the energies expected in a fusion reactor as well as the specific responses that are of interest. In this paper, a novel weight window generation implementation in Serpent is investigated. The applicability of this method is demonstrated for the Frascati Neutron Generator (FNG) bulk blanket and shield experiment, part of the SINBAD database, and a DEMO helium cooled pebble bed (HCPB) computational model. A comparison is performed against MCNP using weight windows generated with ADVANTG. Excellent agreement is found for the specified tallies and the significant efficiency gain using weight windows generated using both methods is comparable. A robust variance reduction method implementation is fundamental to applications to fusion neutronics and as such, this work is an important step in deployment of Serpent for this type of analysis.

Original languageEnglish
Article number113090
JournalFusion Engineering and Design
Volume178
DOIs
Publication statusE-pub ahead of print - 9 Mar 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • DEMO
  • MCNP
  • Neutronics
  • Serpent
  • SINBAD
  • Variance reduction

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