Open Access
Issue
EPJ Nuclear Sci. Technol.
Volume 7, 2021
Article Number 5
Number of page(s) 9
DOI https://doi.org/10.1051/epjn/2021005
Published online 17 March 2021
  1. 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]
  2. A.J. Plompen et al., The joint evaluated fission and fusion nuclear data library, JEFF-3.3, Eur. Phys. J. A 56, 181 (2020) [CrossRef] [EDP Sciences] [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.D. Carlson et al., Evaluation of the neutron data standards, Nucl. Data Sheets 148, 143 (2018) [Google Scholar]
  6. E. Bauge, P. Dossantos-Uzarralde, Evaluation of the covariance matrix of 239Pu neutronic cross sections in the continuum using the backward-forward Monte-Carlo method, J. Korean Phys. Soc. 59, 1218 (2011) [Google Scholar]
  7. A.J. Koning, Bayesian Monte Carlo method for nuclear data evaluation, Eur. Phys. J. A 51, 184 (2015) [CrossRef] [EDP Sciences] [Google Scholar]
  8. A. Hoefer, O. Buss, M. Hennebach, M. Schmid, D. Porsch, MOCABA: a general Monte Carlo-Bayes procedure for improved predictions of integral functions of nuclear data, Ann. Nucl. Energy 77, 514 (2015) [Google Scholar]
  9. E. Bauge, Full model nuclear data and covariance evaluation process using TALYS, Total Monte Carlo and backward-forward Monte Carlo, Nucl. Data Sheets 123, 201 (2015) [Google Scholar]
  10. S. Pelloni, D. Rochman, Resonance parameter adjustment in the resolved region based upon an Asymptotic Generalized Linear Least-Squares methodology in conjunction with the Monte Carlo method, Ann. Nucl. Eng. 145, 107509 (2020) [Google Scholar]
  11. C. de Saint Jean, P. Archier, E. Privas, G. Noguère, On the use of Bayesian Monte-Carlo in evaluation of nuclear data, EPJ Web Conf. 146, 02007 (2017) [Google Scholar]
  12. S. Pelloni, Comparison of progressive incremental adjustment sequences for cross-section and variance/covariance data adjustment by analyzing fast-spectrum systems, Ann. Nucl. Energy 106, 33 (2017) [Google Scholar]
  13. S. Pelloni, D. Rochman, Cross-section adjustment in the fast energy range on the basis of an Asymptotic Progressing nuclear data Incremental Adjustment (APIA) methodology, Ann. Nucl. Energy 115, 323 (2018) [Google Scholar]
  14. M. Salvatores et al., Methods and issues for the combined use of integral experiments and covariance data: results of a NEA International Collaborative Study, Nucl. Data Sheets 118, 38 (2014) [Google Scholar]
  15. C. de Saint Jean, P. Archier, E. Privas, G. Noguère, O. Litaize, P. Leconte, Evaluation of cross section uncertainties using physical constraints: focus on integral experiments, Nucl. Data Sheets 123, 178 (2015) [Google Scholar]
  16. R. Capote, D.L. Smith, A. Trkov, M. Meghzifene, A new formulation of the Unified Monte Carlo Approach (UMC-B) and cross-section evaluation for the dosimetry reaction 55 Mn(n, γ)56Mn, J. ASTM Int. 9, 1 (2012) [Google Scholar]
  17. D.L. Smith, A unified Monte Carlo approach to fast neutron cross section data evaluation, in Proceedings of the 8th International Topical Meeting on Nuclear Applications and Utilization of Accelerators, Pocatello, July 29–August 2 (2007), p. 736 [Google Scholar]
  18. P. Helgesson, H. Sjöstrand, A.J. Koning, J. Ryden, D. Rochman, E. Alhassan, S. Pomp, Combining total Monte Carlo and Unified Monte Carlo: Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances, Progr. Nuclear Energy 96, 76 (2017) [Google Scholar]
  19. T. Watanabe, T. Endo, A. Yamamoto, Y. Kodama, Y. Ohoka, T. Ushio, Cross section adjustment method based on random sampling technique, J. Nucl. Sci. Technol. 51, 590 (2014) [Google Scholar]
  20. T. Kawano, K.M. Hanson, S. Frankle, P. Talou, M.B. Chadwick, R.C. Little, Evaluation and propagation of the 239Pu fission cross-section uncertainties using a Monte Carlo technique, Nucl. Sci. Eng. 153, 1 (2006) [Google Scholar]
  21. C. De Saint Jean, P. Archier, E. Privas, G. Noguère, B. Habert, P. Tamagno, Evaluation of neutron-induced cross sections and their related covariances with physical constraints, Nucl. Data Sheets 148, 383 (2018) [Google Scholar]
  22. D. Siefman, M. Hursin, H. Sjostrand, G. Schnabel, D. Rochman, A. Pautz, Data assimilation of post-irradiation examination data for fission yields from GEF, Eur. Phys. J. N 6, 52 (2020) [Google Scholar]
  23. E. Alhassan, D. Rochman, H. Sjostrand, A. Vasiliev, A.J. Koning, H. Ferroukhi, Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo, Ann. Nucl. Eng. 139, 107239 (2020) [Google Scholar]
  24. 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]
  25. 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]
  26. D. Rochman, E. Bauge, A. Vasiliev, H. Ferroukhi, S. Pelloni, A.J. Koning, J.Ch. Sublet, Monte Carlo nuclear data adjustment via integral information, Eur. Phys. J. Plus 133, 537 (2018) [Google Scholar]
  27. E. Bauge, D. Rochman, Cross-observables and cross-isotopes correlations in nuclear data from integral constraints, Eur. Phys. J. N 4, 35 (2018) [Google Scholar]
  28. D. Rochman, E. Bauge, A. Vasiliev, H. Ferroukhi, G. Perret, Nuclear data correlation between different isotopes via integral information, Eur. Phys. J. N 4, 7 (2018) [Google Scholar]
  29. D. Rochman, E. Bauge, A. Vasiliev, H. Ferroukhi, Correlation nu-sigma-chi in the fast neutron range via integral information, Eur. Phys. J. N 3, 14 (2017) [Google Scholar]
  30. J.Ch. Sublet et al., Neutron-induced damage simulations: beyond defect production cross-section, displacement per atom and iron-based metrics, Eur. Phys. J. Plus 134, 350 (2019) [Google Scholar]
  31. O. Leray, L. Fiorito, D. Rochman, H. Ferroukhi, A. Stankovskiy, G. Van den Eynde, Uncertainty propagation of fission product yields to nuclide composition and decay heat for a PWR UO2 fuel assembly, Progr. Nucl. Energy 101, 486 (2017) [Google Scholar]
  32. D. Rochman et al., Nuclear data uncertainties for typical LWR fuel assemblies and a simple reactor core, Nucl. Data Sheets 139, 1 (2017) [Google Scholar]
  33. R.W. Mills, Uncertainty propagation of fission product yield data in spent fuel inventory calculations, Nucl. Data Sheets 118, 484 (2014) [Google Scholar]
  34. T. Frosio, T. Bonaccorsi, P. Blaise, Fission yields and cross section uncertainty propagation in Boltzmann/Bateman coupled problems: Global and local parameters analysis with a focus on MTR, Ann. Nucl. Eng. 98, 43 (2016) [Google Scholar]
  35. J.B. Briggs, J.D. Bess, J. Gulliford, Integral benchmark data for nuclear data testing through the ICSBEP & IRPhEP, Nucl. Data Sheets 118, 396 (2014) [Google Scholar]
  36. J.B. Briggs, J. Gulliford, An overview of the international reactor physics experiment evaluation project, Nucl. Sci. Eng. 178, 269 (2014) [Google Scholar]
  37. I. Kodeli, E. Sartori, B. Kirk, SINBAD shielding benchmark experiments status and planned activities, in Proceedings of the American Nuclear Society, 14th Biennial Topical Meeting of the Radiation Protection and Shielding Division, Carlsbad New Mexico, USA, April 3–6, 2006 [Google Scholar]
  38. M. Tardy, S. Kitsos, G. Grassi, A. Santamarina, L. San Felice, C. Riffard, First burnup credit application including actinides and fission products for transport and storage cask by using French experiments, J. Nucl. Sci. Technol. 52, 1008 (2015) [Google Scholar]
  39. D. Rochman, A. Vasiliev, H. Ferroukhi, M. Seidl, J. Basualdo, Improvement of PIE analysis with a full core simulation: the U1 case, Ann. Nucl. Eng. 148, 107706 (2020) [Google Scholar]
  40. D. Rochman, A. Vasiliev, H. Ferroukhi, M. Pecchia, Consistent criticality and radiation studies of Swiss spent nuclear fuel: the CS2M approach, J. Hazard. Mater. 357, 384 (2018) [Google Scholar]
  41. ARIANE International Programme Final Report, Belgonucléaire, AR2000/15 BN Ref. 0000253/221, Revision B, December 2000 [Google Scholar]
  42. F. Michel-Sendis et al., SFCOMPO-2.0: an OECD NEA database of spent nuclear fuel isotopic assays, reactor design specifications, and operating data, Ann. Nucl. Eng. 110, 779 (2017) [Google Scholar]
  43. D. Rochman, A. Vasiliev, H. Ferroukhi, M. Hursin, Analysis for the ARIANE GU1 sample: isotopic compositions and decay heat, Ann. Nucl. Eng. (2021), in press [Google Scholar]
  44. J. Rhodes, K. Smith, D. Lee, CASMO-5 development and applications, in Proceedings of the PHYSOR-2006 conference, ANS Topical Meeting on Reactor Physics, Vancouver, BC, Canada, September 10–14, Vancouver, BC, Canada, September 10–14 (2006), p. B144. [Google Scholar]
  45. O. Leray, D. Rochman, P. Grimm, H. Ferroukhi, A. Vasiliev, M. Hursin, G. Perret, A. Pautz, Nuclear data uncertainty propagation on spent fuel nuclide compositions, Ann. Nucl. Eng. 94, 603 (2016) [Google Scholar]
  46. M. Herman, D.A. Brown, M.B. Chadwick, W. Haeck, T. Kawano, D. Neudecker, P. Talou, A. Trkov, M.C. White, New paradigm for nuclear data evaluation, in Proceedings of the International Conference on Nuclear Data for Science and Technology, Beijing, China, May 19–24, 2019, EPJ Web Conf. 239, 11001 (2020) [Google Scholar]
  47. E. Bauge et al., Coherent investigation of nuclear data at CEA DAM: Theoretical models, experiments and evaluated data, Eur. Phys. J. A 48, 113 (2012) [CrossRef] [EDP Sciences] [Google Scholar]

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