Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
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|
---|---|---|
Article Number | 15002 | |
Number of page(s) | 11 | |
Section | Monte Carlo Simulation: Applications / Radiation Shielding | |
DOI | https://doi.org/10.1051/epjconf/202430215002 | |
Published online | 15 October 2024 |
https://doi.org/10.1051/epjconf/202430215002
Efficiency of variance-reduction techniques using TRIPOLI-4®: Application to equivalent dose rate calculations in a spent-fuel cask
Université Paris-Saclay, CEA, Service d’études des réacteurs et de mathématiques appliquées, 91191 Gif-sur-Yvette, France
* Corresponding author: alice.bonin@cea.fr
Published online: 15 October 2024
This paper presents the comparison of different variance-reduction methods implemented in the Monte Carlo code TRIPOLI-4® in the case of a deep penetration problem. In this complex radiation transport situation, where the shielding is very thick to ensure protection, variance-reduction methods are necessary to obtain results with a good accuracy in a reasonable calculation time. The configuration investigated in this work is a spent-fuel transport cask. The various variance-reduction methods used here consist either of Importance Sampling with the Exponential Transform method, or of sophisticated population-control methods. These methods are detailed, then the results are presented and compared and the efficiency of these methods is investigated. While all methods implemented in TRIPOLI-4® are efficient versus analog simulations, the pre-calculation of importance maps with deterministic methods enables a faster convergence of the Monte Carlo simulations, especially when combined with the Exponential Transform.
© 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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