| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 10002 | |
| Number of page(s) | 10 | |
| Section | Sensitivity Analysis | |
| DOI | https://doi.org/10.1051/epjconf/202430210002 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430210002
A variance-reduction strategy for the sensitivity of βeff
Université Paris-Saclay, CEA, Service d’Etudes des Réacteurs et de Mathématiques Appliquées, 91191, Gif-sur-Yvette, France
* e-mail: alexis.jinaphanh@cea.fr
** e-mail: andrea.zoia@cea.fr
Published online: 15 October 2024
The Monte Carlo computation of the GPT-based sensitivity of the effective delayed neutron fraction βeff to nuclear data proves to be quite difficult to converge due to the small amount of delayed neutrons that are sampled in k-eigenvalue calculations. This paper describes a variance-reduction method aimed at efficiently computing the sensitivity coefficients of βeff, reducing the associated Monte Carlo uncertainty and increasing the Figure Of Merit. This variance-reduction technique allows also computing the sensitivities of βj eff for a specific precursor family j. Verification and performance evaluation are achieved using simple configurations admitting analytical solutions and several continuousenergy benchmarks.
© The Authors, published by EDP Sciences, 2024
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