Issue
EPJ Nuclear Sci. Technol.
Volume 11, 2025
Special Issue on ‘Overview of recent advances in HPC simulation methods for nuclear applications’, edited by Andrea Zoia, Elie Saikali, Cheikh Diop and Cyrille de Saint Jean
Article Number 69
Number of page(s) 18
DOI https://doi.org/10.1051/epjn/2025065
Published online 05 November 2025
  1. N. Burgio et al., Monte Carlo simulation analysis of integral data measured in the SCK-CEN/ENEA experimental campaign on the TAPIRO fast reactor. Experimental and calculated data comparison, Nucl. Eng. Des. 273, 350 (2014), https://doi.org/10.1016/j.nucengdes.2014.03.040 [Google Scholar]
  2. F. Valentina et al., Neutrons propagation in Lead: A feasibility study for experiments in the RSV TAPIRO fast research reactor, EPJ Web Conf. 284, 08006 (2023), https://doi.org/10.1051/epjconf/202328408006 [Google Scholar]
  3. E. Martelli et al., Study of EU DEMO WCLL breeding blanket and primary heat transfer system integration, Fusion Eng. Des. 136, 828 (2018), https://doi.org/10.1016/j.fusengdes.2018.04.016 [Google Scholar]
  4. P. Arena et al., Design and integration of the EU-DEMO water-cooled lead lithium breeding blanket, Energies 16, 2069 (2023), https://doi.org/10.3390/en16042069 [Google Scholar]
  5. M. Pillon et al., Study of the response of a piezoceramic motor irradiated in a fast reactor up to a neutron fluence of 2.77E+17n/cm2, in Proceedings of the 28th Symposium On Fusion Technology (SOFT-28) (Fusion Engineering and Design, 2015), https://doi.org/10.1016/j.fusengdes.2015.03.025 [Google Scholar]
  6. X. Doligez et al., Effective delayed neutron fraction measurement in the critical VENUS-F reactor using noise techniques, in 2015 4th International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications (ANIMMA) (IEEE, 2015), p. 1 [Google Scholar]
  7. M. Margulis, Zero power reactors in support of current and future nuclear power systems, Nucl. Eng. Des. 425, 113330 (2024), https://doi.org/10.1016/j.nucengdes.2024.113330 [Google Scholar]
  8. F. Zylbersztejn, P. Filliatre, C. Jammes, Analysis of the experimental neutron noise from the PHENIX reactor, Ann. Nucl. Energy 60, 106 (2013), https://doi.org/10.1016/j.anucene.2013.04.009 [Google Scholar]
  9. A. Santagata, Ph.D. thesis, Università degli Studi di Roma “La Sapienza”, 2024 [Google Scholar]
  10. L. Jaakko et al., Status of Serpent Monte Carlo code in 2024, EPJ Nuclear Sci. Technol. 11, 3 (2025), https://doi.org/10.1051/epjn/2024031 [Google Scholar]
  11. G. Truchet et al., Computing adjoint-weighted kinetics parameters in TRIPOLI-4® by the Iterated Fission Probability method, Ann. Nucl. Energy 85, 17 (2015), https://doi.org/10.1016/j.anucene.2015.04.025 [CrossRef] [Google Scholar]
  12. R.K. Meulekamp, S.C. van der Marck, Calculating the effective delayed neutron fraction with Monte Carlo, Nucl. Sci. Eng. 152, 142 (2006), https://doi.org/10.13182/NSE03-107 [Google Scholar]
  13. Y. Nauchi, T. Kameyama, Proposal of direct calculation of kinetic parameters βeff and based on continuous energy Monte Carlo method, J. Nucl. Sci. Technol. 42, 503 (2005), https://doi.org/10.1080/18811248.2004.9726417 [CrossRef] [Google Scholar]
  14. Y. Nauchi, T. Kameyama, Development of calculation technique for iterated fission probability and reactor kinetic parameters using continuous-energy Monte Carlo method, J. Nucl. Sci. Technol. 47, 977 (2010), https://doi.org/10.1080/18811248.2010.9711662 [Google Scholar]
  15. J. Leppänen et al., Calculation of effective point kinetics parameters in the Serpent 2 Monte Carlo code, Ann. Nucl. Energy 65, 272 (2014), https://doi.org/10.1016/j.anucene.2013.10.032 [CrossRef] [Google Scholar]
  16. A. Santamarina et al., Calculation of LWR βeff kinetic parameters: Validation on the MISTRAL experimental program, Ann. Nucl. Energy 48, 51 (2012), https://doi.org/10.1016/j.anucene.2012.05.001 [Google Scholar]
  17. A. dos Santos et al., A proposal of a benchmark for βeff, βeff/λ, and λ of thermal reactors fueled with slightly enriched uranium, Ann. Nucl. Energy 33, 848 (2006), https://doi.org/10.1016/j.anucene.2006.03.006 [Google Scholar]
  18. M. Brovchenko et al., Neutronic benchmark of the molten salt fast reactor in the frame of the EVOL and MARS collaborative projects, EPJ Nuclear Sci. Technol. 5, 2 (2019), https://doi.org/10.1051/epjn/2018052 [Google Scholar]
  19. F. Lodi et al., Characterization of the new ALFRED core configuration, Tech. rep., Technical Report ADPFISS-LP2-085, ENEA, 2015 [Google Scholar]
  20. S. Bortot et al., European benchmark on the ASTRID-like low-void-effect core characterization: Neutronic parameters and safety coefficients, in ICAPP 2015-International Congress on Advances in Nuclear Power Plants (2015), p. 15361 [Google Scholar]
  21. E. Sartori, Standard energy group structures of cross section libraries for reactor shielding, reactor cell and fusion neutronics applications: VITAMIN-J, ECCO-33, ECCO-2000 and XMAS, in JEF/DOC-315, in Revision (1990), p. 3 [Google Scholar]
  22. A. Gandini, M. Salvatores, I.D. Bono, Sensitivity study of fast reactors using generalized perturbation techniques, in Fast Reactor Physics Vol. I. Proceedings of a Symposium on Fast Reactor Physics and Related Safety Problems, (IAEA, International Atomic Energy Agency (IAEA), 1968), p. 241 [Google Scholar]
  23. A. Gandini, M. Salvatores, Effects of Plutonium-239 Alpha uncertainties on some significant integral quantities of fast reactors, Nucl. Sci. Eng. 41, 452 (1970), https://doi.org/10.13182/NSE70-A19105 [Google Scholar]
  24. M. Aufiero et al., A collision history-based approach to sensitivity/perturbation calculations in the continuous energy Monte Carlo code SERPENT, Ann. Nucl. Energy 85, 245 (2015), https://doi.org/10.1016/j.anucene.2015.05.008 [CrossRef] [Google Scholar]
  25. R. Macfarlane et al., The NJOY Nuclear Data Processing System, Version 2016, Tech. rep., Los Alamos National Laboratory (LANL) (1 2017), https://doi.org/10.2172/1338791 [Google Scholar]
  26. Los Alamos National Laboratory, NJOY, https://github.com/njoy (2016) [Google Scholar]
  27. N. Abrate, S. Dulla, P. Ravetto, Generalized perturbation techniques for uncertainty quantification in lead-cooled fast reactors, Ann. Nucl. Energy 164, 108623 (2021), https://doi.org/10.1016/j.anucene.2021.108623 [Google Scholar]
  28. A. Koning, D. Rochman, Towards sustainable nuclear energy: Putting nuclear physics to work, Ann. Nucl. Energy 35, 2024 (2008), https://doi.org/10.1016/j.anucene.2008.06.004 [CrossRef] [Google Scholar]
  29. L. Fiorito et al., Nuclear data uncertainty propagation to integral responses using SANDY, Ann. Nucl. Energy 101, 359 (2017), https://doi.org/10.1016/j.anucene.2016.11.026 [Google Scholar]
  30. D. Rochman et al., Uncertainty propagation with fast Monte Carlo techniques, Nucl. Data Sheets 118, 367 (2014), https://doi.org/10.1016/j.nds.2014.04.082 [Google Scholar]
  31. B. Grégoire et al., Evaluating embedded Monte Carlo vs. total Monte Carlo for nuclear data uncertainty quantification, EPJ Web Conf. 302, 07016 (2024), https://doi.org/10.1051/epjconf/202430207016 [Google Scholar]
  32. A. Aimetta et al., A nonintrusive nuclear data uncertainty propagation study for the ARC fusion reactor design, Nucl. Sci. Eng. 197, 2192 (2023), https://doi.org/10.1080/00295639.2022.2153638 [Google Scholar]
  33. E. Alhassan et al., Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the european lead cooled training reactor, Ann. Nucl. Energy 75, 26 (2015), https://doi.org/10.1016/j.anucene.2014.07.043 [Google Scholar]
  34. S.J. Julier, J.K. Uhlmann, New extension of the Kalman filter to nonlinear systems, in Signal Processing, Sensor Fusion, and Target Recognition VI (International Society for Optics and Photonics, SPIE, Orlando, 1997), p. 182 [Google Scholar]
  35. N. Abrate et al., Nuclear data uncertainty propagation for the molten salt fast reactor design, Nucl. Sci. Eng. 197, 2977 (2023), https://doi.org/10.1080/00295639.2023.2190861 [Google Scholar]
  36. S. Volkwein, Model reduction using proper orthogonal decomposition, Lecture notes, Institute of Mathematics and Scientific Computing, University of Graz. see http://www.uni-graz.at/imawww/volkwein/POD.pdf 1025 (2011), https://www.math.uni-konstanz.de/numerik/personen/volkwein/teaching/POD-Vorlesung.pdf [Google Scholar]
  37. S. Seabold, J. Perktold, statsmodels: Econometric and statistical modeling with python, in 9th Python in Science Conference (2010) [Google Scholar]
  38. R. Borsdorf, N.J. Higham, M. Raydan, Computing a nearest correlation matrix with factor structure, SIAM J. Matrix Anal. Appl. 31, 2603 (2010), https://doi.org/10.1137/090776718 [Google Scholar]
  39. D. Li, Y. Wang, Constrained unscented kalman filter for parameter identification of structural systems, Struct. Control Health Monit. 29, e2908 (2022), https://doi.org/10.1002/stc.2908 [Google Scholar]
  40. G. Federico et al., Application of nuclear data covariance matrices to representativity calculations, EPJ Web Conf. 294, 05005 (2024), https://doi.org/10.1051/epjconf/202429405005 [Google Scholar]
  41. D. Brown et al., ENDF/B-VIII.0: The 8th major release of the nuclear reaction data library with CIELO-project cross sections, new standards and thermal scattering data, Nucl. Data Sheets 148, 1 (2018), https://doi.org/10.1016/j.nds.2018.02.001 [CrossRef] [Google Scholar]
  42. A.J. Plompen et al., The joint evaluated fission and fusion nuclear data library, JEFF-3.3, Eur. Phys. J. A 56, 1 (2020), https://doi.org/10.1140/epja/s10050-020-00141-9 [CrossRef] [Google Scholar]
  43. O. Buss, A. Hoefer, J.C. Neuber, NUDUNA-nuclear data uncertainty analysis, in Proc. International Conference on Nuclear Criticality (ICNC 2011) (Edinburgh, Scotland, 2011) [Google Scholar]
  44. D.G. Chereshkov et al., Nuclear data uncertainty on generation IV fast reactors criticality calculations analysis comparison, Nucl. Energy Technol. 9, 157 (2023) [Google Scholar]
  45. S. Panizo et al., Sensitivity and uncertainty analyses for advanced nuclear systems (ALFRED, ASTRID, ESFR and MYRRHA), Prog. Nucl. Energy 172, 105207 (2024), https://doi.org/10.1016/j.pnucene.2024.105207 [Google Scholar]
  46. L. Qiao, Y. Zheng, C. Wan, Uncertainty quantification of sodium-cooled fast reactor based on the UAM-SFR benchmarks: From pin-cell to full core, Ann. Nucl. Energy 128, 433 (2019), https://doi.org/10.1016/j.anucene.2019.01.033 [Google Scholar]
  47. J. Alexis, Z. Andrea, A variance-reduction strategyfor the sensitivity of βeff, EPJ Web Conf. 302, 10002 (2024), https://doi.org/10.1051/epjconf/202430210002 [Google Scholar]
  48. T. Endo et al., Experimental analysis and uncertainty quantification using random sampling technique for ADS experiments at KUCA, J. Nucl. Sci. Technol. 55, 450 (2018), https://doi.org/10.1080/00223131.2017.1403387 [Google Scholar]
  49. G.K. Delipei et al., Summary of comparative analysis and conclusions from OECD/NEA LWR-UAM benchmark Phase I, Nucl. Eng. Des. 384, 111474 (2021), https://doi.org/10.1016/j.nucengdes.2021.111474 [Google Scholar]
  50. A.A. Ryzhkov, G.V. Tikhomirov, M. Yu. Ternovykh, A review of the current nuclear data performanceassessments in advanced nuclear reactor systems, Ann. Nucl. Energy 212, 110806 (2025), https://doi.org/10.1016/j.anucene.2024.110806 [Google Scholar]

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