EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 10 (2024) EPJ Nuclear Sci. Technol., 10 (2024) 5 References
Open Access
Issue
EPJ Nuclear Sci. Technol.
Volume 10, 2024
Article Number 5
Number of page(s) 13
DOI https://doi.org/10.1051/epjn/2024008
Published online 10 June 2024
  1. B. Mouginot, B. Leniau, N. Thiolliere, M. Ernoult, S. David, X. Doligez, A. Bidaud, O. Meplan, R. Montesanto, G. Bellot et al., Core library for advanced scenario simulation, CLASS: principle & application, in International Conference The Role of Reactor Physics toward a Sustainable Future (PHYSOR 2014) (2014), pp. 12–p [Google Scholar]
  2. G. Marleau, A. Hébert, R. Roy, A User Guide for DRAGON Version 5, in Institut de génie nucléaire, Département de génie physique, École Polytechnique de Montréal. Montréal QC, Canada, Tech. Rep. IGE-335 (2018) [Google Scholar]
  3. A. Hébert, D. Sekki, R. Chambon, A User Guide for DONJON Version 5, in Institut de génie nucléaire, Département de génie mécanique, École Polytechnique de Montréal. Montréal QC, Canada, Tech. Rep. IGE-344 (2019) [Google Scholar]
  4. M. Guillet, X. Doligez, G. Marleau, M. Paradis, M. Ernoult, N. Thiollière, Coupled CLASS and DONJON5 3D full-core calculations and comparison with the neural network approach for fuel cycles involving MOX fueled PWRs, Ann. Nucl. Energy 152, 107971 (2021), https://www.sciencedirect.com/science/article/pii/S0306454920306678 [CrossRef] [Google Scholar]
  5. M. Guillet, Prise en compte des calculs de coeur via l’utilisation de DONJON5 pour le code de scénario nucléaire CLASS et évaluation de l’impact sur les inventaires isotopiques, Master’s thesis, École Polytechnique de Montréal, 2019 [Google Scholar]
  6. M. Paradis, Prise en compte de la chaîne de calcul DRAGON5-DONJON5 dans la simulation de réacteurs à eau sous pression hybride via CLASS, Master’s thesis, Polytechnique Montréal, juillet, 2021, https://publications.polymtl.ca/6649/ [Google Scholar]
  7. M. Paradis, X. Doligez, G. Marleau, M. Ernoult, N. Thiollière, DONJON5/CLASS coupled simulations of MOX/UO2 heterogeneous PWR core, EPJ Nuclear Sci. Technol. 8, 4 (2022), https://doi.org/10.1051/epjn/2021030 [Google Scholar]
  8. X. Doligez, M. Paradis, S. David, M. Ernoult, N. Thiollière, G. Marleau, Advanced modeling of the historical french nuclear fleet with DONJON5/CLASS coupled simulations, GLOBAL 2022 – International Conference on Nuclear Fuel Cycle, 2022 [Google Scholar]
  9. P. Gallet, Développement d’un module de thermohydraulique simplifiée dans le code DONJON4 pour l’étude multiphysique des réacteurs à eau pressurisée, Master’s thesis, École Polytechnique de Montréal, août, 2014, https://publications.polymtl.ca/1499/ [Google Scholar]
  10. A. Hébert, A nuclear data library production system for advanced lattice codes, in International Conference on Nuclear Data for Science and Technology, (EDP Sciences, 2007), pp. 701–704 [Google Scholar]
  11. J.-F. Vidal, O. Litaize, R. Tran, D. Bernard, A. Santamarina, C. Vaglio-Gaudard, ew modelling of lwr assemblies using the apollo2 code package, C+SNA 2Mathematics and Computation and Supercomputing in Nuclear Applications, 2007 [Google Scholar]
  12. A. Hébert, Revisiting the Ceschino interpolation method, in MATLAB – A Ubiquitous Tool for the Practical Engineer, edited by C. M. Ionescu, (IntechOpen, 2011, ch. 2) [Google Scholar]
  13. V. Salino, A. Hèbert, On steady-state multiphysics stability and related in-core fuel management capabilities in DONJON5, in PHYSOR 2016, Unifying Theory and Experiments in the 21st Century (2016) [Google Scholar]
  14. G. Billiet, Modélisations DONJON-CLASS optimisées avec températures locales du combustible, pour des scénarios français de parcs de réacteurs nucléaires REP/SMR, Master’s thesis, École Polytechnique de Montréal, 2023 [Google Scholar]
  15. F. Courtin, B. Leniau, N. Thiollière, B. Mouginot, X. Doligez, A. Somaini, A.-A. Zakari-Issoufou, S. David, A. Bidaud, J.-B. Clavel, Neutronic predictors for PWR fuelled with multi-recycled plutonium and applications with the fuel cycle simulation tool CLASS, Prog. Nucl. Energy 100, 33 (2017) [CrossRef] [Google Scholar]
  16. A. V. Skarbeli, I. M. Rodríguez, F. Álvarez-Velarde, A. Hernandez-Solis, G. Van den Eynde, Quantification of the differences introduced by nuclear fuel cycle simulators in advanced scenario studies, Ann. Nucl. Energy 137, 107160 (2020) [CrossRef] [Google Scholar]
  17. RTE, Futurs énergétiques 2050, (Réseau de transport d’électricité, 2022, ch. 5: les scénarios de mix production-consommation), https://assets.rte-france.com/prod/public/2022-06/FE2050%20_Rapport%20complet_5.pdf [Google Scholar]
  18. A. Somaini, S. David, X. Doligez, A. Zakari-Issoufou, A. Bidaud, N. Cappelan, O. Méplan, A. Nuttin, P. Prévot, F. Courtin et al., The impact of reactor model simplification for fuel evolution: A bias quantification for fuel cycle dynamic simulations, in 2016 International Congress on Advances in Nuclear Power Plants (ICAPP 2016) (2016), pp. 1045–1053 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.