Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

  • D. SiefmanEmail author
  • M. Hursin
  • D. Rochman
  • S. Pelloni
  • A. Pautz
Regular Article


Developments in data assimilation theory allow to adjust integral parameters and cross sections with stochastic sampling. This work investigates how two stochastic methods, MOCABA and BMC, perform relative to a sensitivity-based methodology called GLLS. Stochastic data assimilation can treat integral parameters that behave non-linearly with respect to nuclear data perturbations, which would be an advantage over GLLS. Additionally, BMC is compatible with integral parameters and nuclear data that have non-Gaussian distributions. In this work, MOCABA and BMC are compared to GLLS for a simple test case: JEZEBEL-Pu239 simulated with Serpent2. The three methods show good agreement between the mean values and uncertainties of their posterior calculated values and nuclear data. The observed discrepancies are not statistically significant with a sample size of 10000. BMC posterior calculated values and nuclear data have larger uncertainties than MOCABA’s at equivalent sample sizes.


  1. 1.
    M. Salvatores et al., Nucl. Data Sheets 118, 38 (2014)ADSCrossRefGoogle Scholar
  2. 2.
    A. Hoefer, O. Buss, J.C. Neuber, How confident can we be in confidence intervals for the computational bias obtained with the generalized linear least squares methodology? - A toy model analysis, in Proceedings of the International Conference on Nuclear Criticality (2011)Google Scholar
  3. 3.
    D. Siefman, Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign, in Proceedings of the International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, M&C 2017, Jeju, South Korea (2017)Google Scholar
  4. 4.
    T. Watanabe et al., J. Nucl. Sci. Technol. 51, 590 (2014)CrossRefGoogle Scholar
  5. 5.
    O. Buss et al., Ann. Nucl. Energy 77, 514 (2015)CrossRefGoogle Scholar
  6. 6.
    E. Castro et al., Ann. Nucl. Energy 95, 148 (2016)CrossRefGoogle Scholar
  7. 7.
    D. Rochman et al., Eur. Phys. J. A 52, 182 (2015)ADSCrossRefGoogle Scholar
  8. 8.
    E. Alhassan et al., Prog. Nucl. Energy 88, 43 (2016)CrossRefGoogle Scholar
  9. 9.
    H. Mitani, H. Kuroi, J. Nucl. Sci. Technol. 9, 383 (1972)CrossRefGoogle Scholar
  10. 10.
    A. Pazy et al., Nucl. Sci. Eng. 55, 280 (1974)CrossRefGoogle Scholar
  11. 11.
    J. Dragt et al., Nucl. Sci. Eng. 62, 119 (1977)CrossRefGoogle Scholar
  12. 12.
    D. Rochman et al., Ann. Nucl. Energy 112, 236 (2018)CrossRefGoogle Scholar
  13. 13.
    G. Palmiotti et al., Nucl. Sci. Eng. 178, 295 (2014)CrossRefGoogle Scholar
  14. 14.
    A. Koning et al., Ann. Nucl. Energy 35, 2024 (2008)CrossRefGoogle Scholar
  15. 15.
    D. Rochman et al., EPJ Web of Conferences 8, 4003 (2010)CrossRefGoogle Scholar
  16. 16.
    O. Leray et al., Ann. Nucl. Energy 110, 547 (2017)CrossRefGoogle Scholar
  17. 17.
    J. Leppänen et al., Ann. Nucl. Energy 82, 142 (2015)CrossRefGoogle Scholar
  18. 18.
    M. Aufiero et al., Ann. Nucl. Energy 85, 245 (2015)CrossRefGoogle Scholar
  19. 19.
    T. Zhu et al., Ann. Nucl. Energy 75, 713 (2015)CrossRefGoogle Scholar
  20. 20.
    International handbook of evaluated reactor physics benchmark experiments NEA/NSC/DOC(2006)1 (2017)Google Scholar
  21. 21.
    D. Siefman, Convergence Analysis and Criterion for Parameters Estimated with Sensitivities from Monte Carlo Neutron Transport Codes, in Proceedings of the International Conference on Reactor Physics paving the way towards more efficient systems, PHYSOR2018, Cancun, Mexico (2018)Google Scholar
  22. 22.
    R. MacFarlane, A. Kahler, Nucl. Data Sheets 111, 2739 (2010)ADSCrossRefGoogle Scholar
  23. 23.
    B. Efron, R. Tibshirani, Stat. Sci. 1, 54 (1986)CrossRefGoogle Scholar
  24. 24.
    E. Lehmann, G. Casella, Theory of Point Estimation, Vol. 2 (Springer-Verlag New York, 1998)Google Scholar
  25. 25.
    A. Doucet, A. Johansen, in Handbook of Nonlinear Filtering (Oxford University Press, 2011)Google Scholar
  26. 26.
    S. Surace, A. Kutschireiter, J. Pfister, arXiv:1703.07879 (2017)Google Scholar
  27. 27.
    G. Palmiotti, M. Salvatores, G. Aliberti, Nucl. Data Sheets 123, 41 (2015)ADSCrossRefGoogle Scholar

Copyright information

© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • D. Siefman
    • 1
    Email author
  • M. Hursin
    • 2
  • D. Rochman
    • 2
  • S. Pelloni
    • 2
  • A. Pautz
    • 1
  1. 1.Laboratory for Reactor Physics and Systems BehaviorÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Nuclear Energy and Safety Research DivisionPaul Scherrer Institut (PSI)Villigen PSISwitzerland

Personalised recommendations