Issue
EPJ Nuclear Sci. Technol.
Volume 4, 2018
Special Issue on 4th International Workshop on Nuclear Data Covariances, October 2–6, 2017, Aix en Provence, France – CW2017
Article Number 14
Number of page(s) 8
Section Applied Covariances
DOI https://doi.org/10.1051/epjn/2018016
Published online 29 June 2018
  1. A.J. Koning, D. Rochman, Modern nuclear data evaluation with the TALYS code system, Nucl. Data Sheets 113, 2841 (2012) [CrossRef] [Google Scholar]
  2. D. Rochman, A.J. Koning, Living without covariance files, Workshop on Neutron Cross Section Covariances, Port Jefferson, NY, USA, 2008 [Google Scholar]
  3. R. Capote, D.L. Smith, An investigation of the performance of the unified Monte Carlo method of neutron cross section data evaluation, Nucl. Data Sheets, 109, 2768 (2008) [CrossRef] [Google Scholar]
  4. I. Hill, J. Dyrda, The half Monte Carlo method: combining total Monte Carlo with nuclear data sensitivity profiles, Trans. Am. Nucl. Soc. 116, 712 (2017) [Google Scholar]
  5. I. Hill, J. Gulliford, N. Soppera, M. Bossant, DICE 2013: New Capabilities and Data, Proc. PHYSOR , October 2014 (2014) [Google Scholar]
  6. International handbook of evaluated criticality safety benchmark experiments, September 2016 Edition. OECD/NEA Nuclear Science Committee, NEA/NSC/DOC(95)03 [Google Scholar]
  7. D. Rochman, W. Zwermann, S.C. van der Marck, A.J. Koning, H. Sjöstrand, P. Helgesson, B. Krzykacz-Hausmann, Efficient use of Monte Carlo: uncertainty propagation, Nucl. Sci. Eng. 177, 337 (2014) [CrossRef] [Google Scholar]
  8. P. Helgesson, H. Sjöstrand, A.J. Koning, J. Rydén, D. Rochman, E. Alhassan, S. Pomp, Combining total Monte Carlo and unified Monte Carlo: Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances, Prog. Nucl. Energy 96, 76 (2017) [CrossRef] [Google Scholar]
  9. O. Leray, H. Ferroukhi, M. Hursin, A. Vasiliev, D. Rochman, Methodology for core analyses with nuclear data uncertainty quantification and application to swiss PWR operated cycles, Ann. Nucl. Energy 110, 547 (2017) [CrossRef] [Google Scholar]
  10. D. Rochman, A.J. Koning, S.C. van der Marck, A. Hogenbirk, C.M. Sciolla, Nuclear data uncertainty propagation: perturbation vs. Monte Carlo, Ann. Nucl. Energy 38, 942 (2011) [CrossRef] [Google Scholar]
  11. M.E. Rising, M.C. White, P. Talou, A.K. Prinja, Unified Monte Carlo: Evaluation, Uncertainty Quantification and Propagation of the Prompt Fission Neutron Spectrum (EDP Sciences, France, 2013) [Google Scholar]
  12. R.E. MacFarlane, The NJOY Nuclear Data Processing System Version 2012, LA-UR-12-27079 Rev (2012) [Google Scholar]
  13. D.G. Cacuci, in Sensitivity and Uncertainty Analysis Theory (Chapman & Hall/CRC, 2003), Vol. I [Google Scholar]
  14. L. Fiorito, M. Griseri, A. Stankovskiy, Nuclear Data Uncertainty Propagation in Reactor Studies Using the SANDY Monte Carlo Sampling Code, in M&C 2017, Jeju, Korea, April 16–20, 2017 [Google Scholar]
  15. M. Herman, A. Trkov, ENDF-6 Formats Manual Data Formats and Procedures for the Evaluated Nuclear Data File ENDF/B-VI and ENDF/B-VII, BNL-90365-2009 (2009) [Google Scholar]
  16. J. Leppanen, PSG2/Serpent-A Continuous-Energy Monte Carlo Reactor Physics Bum up Calculation Code, VTT Technical Research Centre of Finland (2010): http://montecarlo.vtt.fi (current as of May 8, 2013) [Google Scholar]

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