https://doi.org/10.1051/epjn/2025062
Regular Article
Convergence of Monte Carlo algorithms for power reactor 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, Institut Polytechnique de Paris, 77455 Champs-sur-Marne, France
* e-mail: axel.fauvel@cea.fr
Received:
30
July
2025
Received in final form:
31
August
2025
Accepted:
27
August
2025
Published online: 6 November 2025
Power reactor noise refers to the small periodical variations of the neutron flux in nuclear reactor cores, induced by various perturbations. A prominent example is the vibration of fuel assemblies due to fluid-structure interactions. Although noise is generally an unwanted phenomenon, its analysis is useful for core monitoring. In this respect, several state-of-the-art deterministic or Monte Carlo solvers have been developed to solve the equations describing neutron noise. In this paper, we investigate the convergence properties of Monte Carlo methods for neutron noise analysis, within the orthodox linearization approximation. For this purpose, we establish a theoretical framework for complex-weighted Monte Carlo games and we show that convergence of such algorithms can be assessed using two sets of eigenvalues. In order to substantiate our findings, we examine a few relevant benchmark configurations for noise problems.
© A. Fauvel et al., Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
