Open Access
Issue
EPJ Nuclear Sci. Technol.
Volume 8, 2022
Article Number 3
Number of page(s) 7
DOI https://doi.org/10.1051/epjn/2021028
Published online 17 January 2022
  1. A.J.M. Plompen, et al., The joint evaluated fission and fusion nuclear data library, JEFF-3.3, Eur. Phys. J. A 56, 181 (2020) [Google Scholar]
  2. D.A. 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) [Google Scholar]
  3. K. Shibata et al., JENDL-4.0: a new library for nuclear science and engineering, J. Nucl. Sci. Technol. 48, 1 (2011) [Google Scholar]
  4. A. Koning, D. Rochman, J. Sublet, N. Dzysiuk, M. Fleming, S. van der Marck, TENDL: complete nuclear data library for innovative nuclear science and technology, Nucl. Data Sheets 155, 1 (2019) [Google Scholar]
  5. A. Aures, W. Bernnat, F. Bostelmann, J. Bousquet, B. Krzykacz-Hausmann, A. Pautz, K. Velkov, W. Zwermann, Reactor simulations with nuclear data uncertainties, proceedings of the ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), BEPU2018-KN-E7, Real Collegio, Lucca, Italy, May 13-19, 2018 [Google Scholar]
  6. D. Rochman, A. Dokhane, A. Vasiliev, H. Ferroukhi, M. Hursin, Nuclear data uncertainties for core parameters based on Swiss BWR operated cycles, Ann. Nucl. Energy 148, 107727 (2020) [CrossRef] [Google Scholar]
  7. O. Cabellos, E. Castro, C. Ahnet, C. Holgado, Propagation of nuclear data uncertainties for PWR core analysis, Nucl. Eng. Technol. 46, 299 (2014) [CrossRef] [Google Scholar]
  8. I.C. Gauld, M.L. Williams, F. Michel-Sendis, J.S. Martinez, Integral nuclear data validation using experimental spent nuclear fuel compositions, Nucl. Eng. Technol. 49, 1226 (2017) [CrossRef] [Google Scholar]
  9. M. Frankl, M. Hursin, D. Rochman, A. Vasiliev, H. Ferroukhi, Nuclear data uncertainty quantification in criticality safety evaluations for spent nuclear fuel geological disposal, Appl. Sci. 11, 6499 (2021) [CrossRef] [Google Scholar]
  10. D. Rochman, A. Vasiliev, A. Dokhane, H. Ferroukhi, Uncertainties for Swiss LWR spent nuclear fuels due to nuclear data, Eur. Phys. J. Nuclear Sciences & Technologies 4, 6 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  11. G. Ilas, H. Liljenfeldt, Decay heat uncertainty for BWR used fuel due to modeling and nuclear data uncertainties, Nucl. Eng. Des. 319, 176 (2017) [CrossRef] [Google Scholar]
  12. A. Rintala, Evaluating the Effect of Decay and Fission Yield Data Uncertainty on BWR Spent Nuclear Fuel Source Term, in Proceedings of the 29th international conference on nuclear energy for the new Europe, NENE 2020, Sept. 7–10, Portoroz, Slovenia (2020) [Google Scholar]
  13. I. Trivedi, J. Hou, G. Grasso, K. Ivanov, F. Franceschini, Nuclear data uncertainty quantification and propagation for safety analysis of lead-cooled fast reactors, Sci. Technol. Nucl. Instal. 3961095 (2020) [Google Scholar]
  14. L.A. Bernstein, D.A. Brown, A.J. Koning, B.T. Rearden, C.E. Romano, A.A. Sonzogni, A.S. Voyles, W. Younes, Our future nuclear data needs, Annu. Rev. Nucl. Part. Sci. 69, 109 (2019) [CrossRef] [Google Scholar]
  15. F. Bostelmann, G. Ilas, W.A. Wieselquist, Key Nuclear Data Impacting Reactivity in Advanced Reactors, Oak Ridge National Laboratory, Report ORNL/TM-2020/1557 (2020) [Google Scholar]
  16. W.A. Wieselquist, K.S. Kim, G. Ilas, I.C. Gault, Comparison of burnup credit uncertainty quantification methods, in Proceedings of the ANS NCSD 2013 - Criticality Safety in the Modern Era: Raising the Bar, Wilmington, NC, September 29-October 3, 2013, on CD-ROM, American Nuclear Society, LaGrange Park, IL (2013) [Google Scholar]
  17. A. Vasiliev, J. Herrero, M. Pecchia, D. Rochman, H. Ferroukhi, S. Caruso, Preliminary assessment of criticality safety constraints for Swiss spent nuclear fuel loading in disposal canisters, Materials 12, 494 (2019) [CrossRef] [Google Scholar]
  18. I.C. Gauld, U. Mertyurek, Validation of BWR spent nuclear fuel isotopic predictions with applications to burnup credit, Nucl. Eng. Des. 345, 110 (2019) [CrossRef] [Google Scholar]
  19. Safety assessment and verification for nuclear power plants, IAEA Safety Standards Series NS-G-1.2 (International Atomic Energy Agency, Vienna, Austria, 2009) [Google Scholar]
  20. Best estimate safety analysis for nuclear power plants: Uncertainty evaluation, IAEA Safety Report Series No. 52 (International Atomic Energy Agency, Vienna, Austria, 2008) [Google Scholar]
  21. E.J. Bonano, J.E. Meacham, G.J. Appel, Radioactive waste management: it’s not all science and engineering, in Proceedings of the conference on Management of Spent Fuel from Nuclear Power Reactors: Learning from the Past, Enabling the Future, International Atomic Energy Agency, Vienna, Austria, 2020, on CD-ROM, IAEA-CN-272/65, ID65 (2020) [Google Scholar]
  22. J.B. Briggs Ed., International Handbook of evaluated Criticality Safety, Benchmark Experiments, NEA/NSC/DOC(95)03/I (Organization for Economic Co-operation and Development, Nuclear Energy Agency, 2004) [Google Scholar]
  23. J.I. Marquez Damian, J.R. Granada, D.C. Malaspina, CAB models for water: a new evaluation of the thermal neutron scattering laws for light and heavy water in ENDF-6 format, Ann. Nucl. Energy 65, 280 (2014) [CrossRef] [Google Scholar]
  24. G. Noguere, J.P. Scotta, C. De Saint Jean, P. Archier, Covariance matrices of the hydrogen neutron cross sections bound in light water for the JEFF-3.1.1 neutron library, Ann. Nucl. Energy 104, 132 (2017) [CrossRef] [Google Scholar]
  25. A.J. Koning, D. Rochman, Towards sustainable nuclear energy: putting nuclear physics to work, Ann. Nucl. Energy 35, 2024 (2008) [CrossRef] [Google Scholar]
  26. L. Maul, Thermal Scattering Law Uncertainties and Propagation into Small Thermal Fission Reactors, PhD Thesis, University of New South Wales, Sydney, Australia (2018) [Google Scholar]
  27. J.P. Scotta, G. Noguère, J.I. Marquez Damian, Generation of the 1H in H2O neutron thermal scattering law covariance matrix of the CAB model, Eur. Phys. J. Nuclear Sciences & Technologies 4, 32 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  28. D. Rochman, A. Koning, Improving the H in H2O thermal scattering data using the Petten method, Nucl. Sci. Eng. 172, 287 (2012) [CrossRef] [Google Scholar]
  29. D. Rochman, A. Koning, Evaluation and adjustement of the neutron-induced reactions of 63,65Cu, Nucl. Sci. Eng. 170, 265 (2012) [CrossRef] [Google Scholar]
  30. D. Rochman, A. Koning, How to randomly evaluate nuclear data: a new method applied to 239Pu, Nucl. Sci. Eng. 169, 68 (2011) [CrossRef] [Google Scholar]
  31. E. Bauge, M. Dupuis, S. Hilaire, S. Peru, A.J. Koning, D. Rochman, S. Goriely, Connecting the dots, or nuclear data in the age of supercomputing, Nucl. Data Sheets 118, 32 (2014) [CrossRef] [Google Scholar]
  32. D. Rochman, E. Bauge, A. Vasiliev, H. Ferroukhi, Correlation nu-sigma-chi in the fast neutron range via integral information, Eur. J. Phys. Nuclear Sciences & Technologies 3, 14 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  33. D. Rochman, E. Bauge, A. Vasiliev, H. Ferroukhi, S. Pelloni, J.Ch. Koning, Sublet, Monte Carlo nuclear data adjustment via integral information, Eur. Phys. J. Plus 133, 537 (2018) [CrossRef] [Google Scholar]
  34. D. Rochman, A. Vasiliev, H. Ferroukhi, S. Pelloni, E. Bauge, A.J. Koning, Correlation nu-sigma for U-Pu in the thermal and resonance neutron range via integral information, Eur. Phys. J. Plus 134, 453 (2019) [Google Scholar]
  35. D. Kumar, S.B. Alam, H. Sjöstrand, J.M. Palau, C. De Saint Jean, Nuclear data adjustment using Bayesian inference, diagnostics for model fit and influence of model parameters, Eur. Phys. J. Web Conf. 239, 13003 (2020) [CrossRef] [EDP Sciences] [Google Scholar]
  36. C. De Saint Jean, P. Archier, E. Privas, G. Noguere, B. Habert, P. Tamagno, Evaluation of neutron-induced cross sections and their related covariances with physical constraints, Nucl. Data Sheets 148, 383 (2018) [CrossRef] [Google Scholar]
  37. C.W. Chapman, Thermal neutron scattering evaluation framework, PhD Thesis, Georgia Institute of Technology, USA, August 2017 [Google Scholar]
  38. D. Siefman, M. Hursin, D. Rochman, S. Pelloni, A. Pautz, Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments, Eur. Phys. J. Plus 133, 429 (2018) [Google Scholar]
  39. E. Ivanov, C. de Saint Jean, V. Sobes, Nuclear data assimilation, scientific basis and current status, Eur. J. Phys. NuclearSciences & Technologies 7, 9 (2021) [Google Scholar]
  40. D. Rochman, M. Hursin, A. Vasiliev, H. Ferroukhi, Impact of H in H2O thermalscattering data on depletion calculation: keff, nuclide inventory and decay heat, submitted to Eur. Phys. J. Nuclear Sciences & Technologies (2021) [Google Scholar]
  41. tsl-HinH2O, J.I. Marquez Damian, J.R. Granada, D. Roubtsov, https://github.com/marquezj/tsl-HinH2O, September 2020 [Google Scholar]
  42. J.P. Scotta, G. Noguere, D. Bernard, J.I. Marquez Damian, A. Santamarina, Impact of the thermal scattering law of H in H2O on the isothermal temperature reactivity coefficients for UOX and MOX fuel lattices in cold operating conditions, Eur. Phys. J. Nuclear Sciences & Technologies 2, 28 (2016) [CrossRef] [EDP Sciences] [Google Scholar]
  43. R.E. MacFarlane, A.C. Kahler, Methods for processing ENDF/B-VII with NJOY, Nucl. Data Sheets 111, 2739 (2010) [CrossRef] [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.