| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 08004 | |
| Number of page(s) | 11 | |
| Section | Particle Transport in Random Media & Neutron Noise | |
| DOI | https://doi.org/10.1051/epjconf/202430208004 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430208004
Convergence of Monte Carlo methods for neutron noise
1 Université Paris-Saclay, CEA, Service d’étude des réacteurs et de mathématiques appliquées, 91191 Gif-sur-Yvette, France
2 École nationale des ponts et chaussées, 77455 Champs-sur-Marne, France
* Corresponding author: axel.fauvel@cea.fr
** Corresponding author: amelie.rouchon@cea.fr
*** Corresponding author: davide.mancusi@cea.fr
**** Corresponding author: andrea.zoia@cea.fr
Published online: 15 October 2024
The neutron noise δφ describes the small variations of the neutron flux around the stationary state φ0, and is typically due to vibrations or oscillations of the core components, induced by fluid-structure interactions and other generally unwanted phenomena. Knowledge of δφ is useful for core monitoring: for this purpose, in recent years several new computational methods have been proposed in order to solve the neutron noise equations with state-of-theart deterministic or Monte Carlo solvers. In this paper we present a preliminary investigation of the convergence properties of Monte Carlo methods for neutron noise analysis, in view of improving their reliability. We carry out our investigation on an infinite-medium benchmark configuration: despite the introduced simplifications, our findings suggest some universal features that represent a stepping stone towards a broader theoretical framework.
Publisher note: This article has been corrected due to problems displaying special characters on October 16, 2024.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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