Open Access
EPJ Nuclear Sci. Technol.
Volume 5, 2019
Article Number 24
Number of page(s) 19
Published online 23 December 2019
  1. L. San-Felice et al., Experimental validation of the DARWIN2.3 package for fuel cycle applications, Nucl. Technol. 184, 217 (2013) [CrossRef] [Google Scholar]
  2. A. Santamarina et al., APOLLO2.8: a validated code package for PWR calculations, in Proc. Int. Conf. Advances in Nuclear Fuel Management IV, Hilton Head Island, South Carolina USA, 2009 [Google Scholar]
  3. A. Tsilanizara et al., DARWIN: an evolution code system for a large range of applications, in Proc. Int. Conf. ICRS-9, Tsukuba, Ibakari, Japan, 1999 [Google Scholar]
  4. A. Santamarina et al., The JEFF-3.1.1 Nuclear Data library”, JEFF report, 22, OECD-NEA Data Bank, 2009 [Google Scholar]
  5. C. de Saint Jean et al., Status of CONRAD, a nuclear reaction analysis tool, in Proc. Int. Conf. Nuclear Data for Science and Technology, Nice, France, 2007 [Google Scholar]
  6. P. Archier et al., CONRAD evaluation code: development status and perspectives, in Proc. Int. Conf. Nuclear Data for Science and Technology, New-York, USA, 2013 [Google Scholar]
  7. A. Rizzo et al., Work plan for improving the DARWIN2.3 depleted material balance calculation of nuclides of interest for the fuel cycle, EPJ Web Conf. 146, 09030 (2017) [CrossRef] [Google Scholar]
  8. A. Rizzo et al., Nuclear data Adjustment based on the interpretation of post-irradiation experiments with the DARWIN2.3 package, EPJ Nuclear Sci. Technol. 4, 47 (2018) [Google Scholar]
  9. A. Rizzo et al., Assessment of the 153Eu and 154Eu neutron capture cross sections from the Integral Data Assimilation of used nuclear fuel experiments, Ann. Nucl. Energy 124, 524 (2019) [CrossRef] [Google Scholar]
  10. C. Bastian et al., AGS, a computer code for uncertainty propagation in time-of-flight cross-section data, in Proc. Int. Conf. PHYSOR, Vancouver, Canada, 2006 [Google Scholar]
  11. B. Habert et al., Retroactive generation of covariance matrix of nuclear model parameters using marginalization techniques, Nucl. Sci. Eng. 166, 276 (2010) [CrossRef] [Google Scholar]
  12. G. Noguere et al., Zero variance penalty model for the generation of covariance matrices in integral data assimilation problems, Nucl. Sci. Eng. 172, 164 (2012) [CrossRef] [Google Scholar]
  13. E. Privas et al., Generation of 238U covariance matrices by using the integral data assimilation technique of the CONRAD code, EPJ Web Conf. 106, 04015 (2016) [CrossRef] [Google Scholar]
  14. I.C. Gauld et al., Uncertainties in predicted isotopic compositions for high burnup PWR spent nuclear fuel, U.S. NRC report NUREG/CR-7012, ORNL/TM-2010/41, 2010 [Google Scholar]
  15. E. Brun et al., TRIPOLI-4®, CEA, EDF, and AREVA reference Monte Carlo code, Ann. Nucl. Energy 82, 151 (2015) [CrossRef] [Google Scholar]
  16. M. Ouisloumen et al., A model for neutron scattering off heavy isotopes that accounts for thermal agitation effects, Nucl. Sci. Eng. 107, 189 (1991) [CrossRef] [Google Scholar]
  17. V. Vallet et al., Deterministic approach of the decay heat uncertainty due to JEFF-3.1.1 nuclear data uncertainties with the CYRUS tool and the DARW IN2. 3 depletion code, Proc. Int. Conf. PHYSOR, Kyoto, Japan, 2014 [Google Scholar]
  18. P. Archier et al., COMAC − Nuclear data covariance matrices library for reactor applications, Proc. Int. Conf. PHYSOR, Kyoto, Japan, 2014 [Google Scholar]
  19. N. Terranova, Covariance Evaluation for Nuclear Data of Interest to the Reactivity Loss Estimation of the Jules Horowitz Reactor, Ph.D. thesis report, Bologne University, Italy, 2016 [Google Scholar]
  20. D. Bernard et al., Validation of actinides nuclear cross section using pile-oscillation experiments performed at MINERVE facility, J. Korean Phys. Soc. 59, 1119 (2011) [CrossRef] [Google Scholar]
  21. P. Leconte et al., OSMOSE programme: validation of actinides nuclear data for LWR applications, JEFDOC-1502, OECD NEA Data Bank, 2013 [Google Scholar]
  22. P. Leconte et al., Feedback on 239Pu and 240Pu nuclear data and associated covariances through the CERES integral experiments, J. Nucl. Sci. Technol. 52, 1044 (2015) [CrossRef] [Google Scholar]
  23. A. Gruel et al., Interpretation of fission products oscillations in the MINERVE reactor, from thermal to epithermal spectra, Nucl. Sci. Eng. 169, 229 (2011) [CrossRef] [Google Scholar]
  24. P. Leconte et al., MAESTRO: an ambitious experimental programme for the improvement of nuclear data of structural, detection, moderating, and absorbing materials − first results for natV, 55Mn, 59Co, and 103Rh, in Proc. Int. Conf. ANIMMA, Marseille, France, 2013 [Google Scholar]
  25. P. Leconte et al., Thermal neutron activation experiments on Ag, In, Cs, Eu, V, Mo, Zn, Sn and Zr in the MINERVE facility, EPJ Web Conf. 111, 07001 (2016) [CrossRef] [Google Scholar]
  26. P. Leconte et al., Nuclear data feedback on structural, moderating and absorbing materials through the MAESTRO experimental programme, JEFDOC-1849, OECD NEA Data Bank, 2017 [Google Scholar]
  27. D. Bernard et al., Validation of JEFF-3.1.1 thermal and epithermal induced capture cross sections through MELUSINE experiment analysis, Nucl. Sci. Eng. 179, 302 (2015) [CrossRef] [Google Scholar]
  28. J.-F. Lebrat et al., JEFF-3.1.1 nuclear data validation for sodium fast reactors, J. Nucl. Sci. Technol. 48, 620 (2011) [CrossRef] [Google Scholar]
  29. J. Lerendegui-Marco et al., Radiative neutron capture of 242Pu in the resonance region at the CERN n_TOF-EAR1 facility, Phys. Rev. C 97, 024605 (2018) [CrossRef] [Google Scholar]
  30. E. Mendoza et al., Measurement and analysis of the 243Am neutron capture cross section at the n_TOF facility at CERN, Phys. Rev. C 90, 036608 (2014) [CrossRef] [Google Scholar]
  31. G. Leinweber et al., Europium resonance parameters from neutron capture and transmission measurements in the energy range 0.01-200 eV, Ann. Nucl. Energy 69, 74 (2014) [CrossRef] [Google Scholar]
  32. C. Schmitt et al., Fission yield at different fission-product kinetic energies for thermal-neutron-induced fission on 239Pu, Nucl. Phys. A 940, 21 (1984) [CrossRef] [Google Scholar]
  33. A. Bail, Mesures de rendements isobariques et isotopiques des produits de fission lourds sur le spectrumètre de masse Lohengrin, Ph.D thesis, University of Bordeaux, France, 2009 [Google Scholar]
  34. Y. Gupta et al., Fission fragment yield distribution in the heavy-mass region from the 239Pu(nth, f) reaction, Phys. Rev. C 96, 014608 (2017) [CrossRef] [Google Scholar]
  35. L. Leal et al., Nuclear data evaluation work at IRSN, JEFDOC-1832, OECD NEA Data Bank, 2017 [Google Scholar]
  36. A. Santamarina, D. Bernard et al., Re-estimation of nuclear data and reliable covariances using integral experiments. Application to JEFF3 library, in Proc. Int. Conf. on Mathematicals & Computational methods applied to nuclear science and engineering, Jeju, South Korea, 2017 [Google Scholar]
  37. S.E. Skutnik, Proposed re-evaluation of the 154Eu thermal (n, γ) capture cross-section based on spent fuel nuclear benchmarking studies, Ann. Nucl. Energy 99, 80 (2017) [CrossRef] [Google Scholar]
  38. JEFF-3.3 nuclear data library, available at (2018) [Google Scholar]
  39. G. Noguere et al., New resonance parameters shape analysis of the 1st resonance of Pu240 for thermal reactor applications, JEFDOC-1526, OECD NEA Data Bank, 2013 [Google Scholar]
  40. G. Noguere, P.H.L. Doan, Progress report on 154Eu buildup for DARWIN applications, JEFDOC-1592, OECD NEA Data Bank, 2014 [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.