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All issues Volume 7 (2021) EPJ Nuclear Sci. Technol., 7 (2021) 9 References
Issue
EPJ Nuclear Sci. Technol.
Volume 7, 2021
A tribute to Massimo Salvatores' scientific work
Article Number 9
Number of page(s) 9
DOI https://doi.org/10.1051/epjn/2021008
Published online 06 May 2021
  1. 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, 38–71 (2014) [CrossRef] [Google Scholar]
  2. L.N. Usachev, Y. Bobkov, Planning on optimum set of microscopic experiments and evaluations to obtain a given accuracy in reactor parameter calculations, INDC CCP-19U, IAEA International Nuclear Data Committee (1972) [Google Scholar]
  3. J.L. Rowlands, L.D. Macdougall, The use of integral measurements to adjust cross-sections and predicted reactor properties, Proceedings of the International Conference on Fast Critical Experiments and their Analysis, ANL-7320 (1966) [Google Scholar]
  4. E. Fort, G. Rimpault, J-C. Bosq et al., Improved performances of the fast reactor calculational system ERANOS-ERALIB1 due to improved a priori nuclear data and consideration of additional specific integral data, Ann. Nucl. Energy 30, 1879–1898 (2003) [CrossRef] [Google Scholar]
  5. C. de Saint-Jean (Co-ordinator), Assessment of Existing Nuclear Data Adjustment Methodologies, Report by the Working Party on International Evaluation Co-operation of the NEA Nuclear Science Committee, Vol. 33, NEA/WPEC- 33, OECD/NEA, 2011 [Google Scholar]
  6. G. Palmiotti, M. Salvatores, The role of experiments and of sensitivity analysis in simulation validation strategies with emphasis on reactor physics, Ann. Nucl. Energy 52, 10–21 (2013) [CrossRef] [Google Scholar]
  7. Dan Gabriel Cacuci (ed.) Handbook of nuclear engineering: Vol. 1: nuclear engineering fundamentals, Springer, Boston, MA (Springer 2010) [Google Scholar]
  8. G. Evensen, Data Assimilation: The Ensemble Kalman Filter (Springer, Berlin, 2006) [Google Scholar]
  9. N.M. Larson, ORNL Report ORNL/TM-9179/R8, 2008 [Google Scholar]
  10. M. Moxon et al., UKNSF Report, 2010 [Google Scholar]
  11. P. Archier, C. De Saint Jean, O. Litaize, G. Noguère, L. Berge, E. Privas, P. Tamagno, CONRAD evaluation code: development status and perspectives, Nucl. Data Sheets 118, 488–490 (2014) [CrossRef] [Google Scholar]
  12. A.J. Koning, Bayesian Monte Carlo method for nuclear data evaluation, Nucl. Data Sheets 123, 207–213 (2015) [CrossRef] [Google Scholar]
  13. D. Siefman, M. Hursin, D. Rochman et al., Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments, Eur. Phys. J. Plus 133, 429 (2018) [CrossRef] [Google Scholar]
  14. A. Hoefer et al., MOCABA: A general Monte Carlo–Bayes procedure for improved predictions of integral functions of nuclear data, Ann. Nucl. Energy 77, 514–521 (2015) [CrossRef] [Google Scholar]
  15. C. De Saint Jean, P. Archier, E. Privas, G. Noguere, On the use of Bayesian Monte-Carlo in evaluation of nuclear data, EPJ Web Conf. 146, 02007 (2017) [CrossRef] [Google Scholar]
  16. V. Sobes, L.C. Leal, G. Arbanas, Nuclear data adjustment with SAMMY based on integral experiments, Anaheim, California 111, 843–845 (2014) [Google Scholar]
  17. C. de Saint Jean, P. Archier, E. Privas, G. Nogu‘ere, O. Litaize, P. Leconte, Evaluation of cross section uncertainties using physical constraints: focus on integral experiments, Nucl. Data Sheets 123, 178 (2015) [CrossRef] [Google Scholar]
  18. W. Martienssen (ed.), Low Energy Neutron Physics, Landolt-Bornstein (Springer-Verlag, Berlin, 2000) [Google Scholar]
  19. T. Ivanova, E. Ivanov, I. Hill, Methodology and issues of integral experiments selection for nuclear data validation, EPJ Web Conf. 146 (2017) [Google Scholar]
  20. S. Pelloni, D. Rochman, Performance assessment of adjusted nuclear data along with their covariances on the basis of fast reactor experiments, Ann. Nucl. Energy 121, 361–373 (2018) [CrossRef] [Google Scholar]
  21. M.G. Kendall, A. Stuart, in The Advanced Theory of Statistics. Vol. 2, Inference and Relationship (Hafner, New York, 1961), pp. 474–483 [Google Scholar]
  22. V.F. Turchin et al., The use of mathematical-statistics methods in the solution of incorrectly posed problems, Soviet Physics Uspekhi 13, 681 (1971) [NASA ADS] [CrossRef] [Google Scholar]
  23. K. Beven, Facets of uncertainty: epistemic uncertainty, nonstationarity, likelihood, hypothesis testing, and communication, Hydrol. Sci. J. 61, 1652–1665 (2016) [CrossRef] [Google Scholar]
  24. W. Oberkampf, C. Roy, Verification and Validation in Scientific Computing (Cambridge University Press, UK, 2010) [CrossRef] [Google Scholar]
  25. J.B. Briggs, J.D. Bess, J. Gulliford, Integral benchmark data for nuclear data testing through the ICSBEP & IRPhEP, Nucl. Data Sheets 118 (2014) [Google Scholar]
  26. A. Santamarina et al., Reactivity worth measurement of major fission products in MINERVE LWR lattice experiment, Nucl. Sci. Eng. 178, 562–581 (2014) [CrossRef] [Google Scholar]
  27. L. Leal, A.D. Santos, E. Ivanov, T. Ivanova, Impact of 235U resonance parameter evaluation in the reactivity prediction, Nucl. Sci. Eng. 187, 127–141 (2017) [CrossRef] [Google Scholar]
  28. K.F. Raskach, An Improvement of the Monte Carlo generalized differential operator method by taking into account first- and second-order perturbations of fission source, Nucl. Sci. Eng. 162, 158–166 (2009) [CrossRef] [Google Scholar]
  29. B.C. Kiedrowski, F.B. Brown, P.P.H. Wilson, Adjoint-weighted tallies for k-eigenvalue calculations with continuous-energy Monte Carlo, Nucl. Sci. Eng. 168, 226–241 (2011) [CrossRef] [Google Scholar]
  30. M. Aufiero et al., A collision history-based approach to sensitivity/perturbation calculations in the continuous energy Monte Carlo code SERPENT, Ann. Nucl. Energy, Volume 85, 245–258 (2015) [CrossRef] [Google Scholar]
  31. E. Brun et al., TRIPOLI-4®, CEA, EDF and AREVA reference Monte Carlo code, Ann. Nucl. Energy 82, 151–160 (2015) [CrossRef] [Google Scholar]
  32. A. Jinaphanh, N. Leclaire, Continuous-energy perturbation methods in the MORET 5 code, Ann. Nucl. Energy 114, 395–406 (2018) [CrossRef] [Google Scholar]
  33. C.M. Perfetti, B.T. Rearden, W.R. Martin, SCALE continuous-energy eigenvalue sensitivity coefficient calculations, Nucl. Sci. Eng. 182, 332–353 (2016) [CrossRef] [Google Scholar]
  34. M.T. Pigni, M. Herman, P. Oblozinsky, F.S. Dietrich, Sensitivity analysis of neutron total and absorption cross sections within the optical model, Phys. Rev. C83, 24601 (2011) [Google Scholar]
  35. I. Kodeli, Comments on the status of modern covariance data based on different fission and fusion reactor studies, EPJ Nuclear Sci. Technol. 4, 46 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  36. G. Palmiotti, M. Salvatores, Cross section covariances: a user perspective, EPJ Nuclear Sci. Technol. 4, 40 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  37. E. Bauge, S. Hilaire, P. Dossantos-Uzarralde, Evaluation of the covariance matrix of neutronic cross sections with the Backward-Forward Monte Carlo method, Inter. Conf. Nucl. Sci. Technol. 259–264 (2007) [Google Scholar]
  38. M.B. Chadwick et al., CIELO collaboration summary results: international evaluations of neutron reactions on uranium, plutonium, iron, oxygen and hydrogen, Nucl. Data Sheets 148, 189–213 (2018) [CrossRef] [Google Scholar]
  39. D. Kumar, S.B. Alam, H. Sjöstrand, J.M. Palauand, C. De Saint Jean, Influence of nuclear data parameters on integral experiment assimilation using Cook's distance, EPJ Web Conf. 211, 07001 (2019) [Google Scholar]
  40. C. De Saint Jean et al., Evaluation of neutron-induced cross sections and their related covariances with physical constraints, Nucl. Data Sheets 148, 383–419 (2018) [CrossRef] [Google Scholar]
  41. T.T. Ivanova, M.N. Nikolaev, K.F. Raskach, E.V. Rozhikhin, A.M. Tsiboulia, Use of international criticality safety benchmark evaluation project data for validation of the ABBN cross-section library with the MMK-KENO code, Nucl. Sci. Eng. 145, 247–255 (2003) [CrossRef] [Google Scholar]
  42. The Need for Integral Critical Experiments with Low-moderated MOX Fuels, in Proceedings of the Workshop, Paris, France, 14–15 April 2004, OECD NEA, No. 5668, ISBN 92-64-02078-0 [Google Scholar]
  43. L.C. Leal, G. Noguere, C. de Saint Jean, A.C. Kahler, 239Pu resonance evaluation for thermal benchmark system calculations, Nucl. Data Sheets 118, 122–125 (2014) [CrossRef] [Google Scholar]
  44. J.B. Clarity, W.J. Marshall, B.T. Rearden, I. Duhamel, Selected uses of TSUNAMI in critical experiment design and analysis. In: 2020 ANS Virtual Winter Meeting, Transactions, Volume 123, Number 1, 2020, pp. 804–807 [Google Scholar]
  45. T. Frosio, T. Bonaccorsi, P. Blaise, Extension of Bayesian inference for multi-experimental and coupled problem in neutronics − a revisit of the theoretical approach, EPJ Nuclear Sci. Technol. 4, 19 (2018) [CrossRef] [EDP Sciences] [Google Scholar]

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