Open Access
Issue
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
Article Number 15009
Number of page(s) 8
Section Sensitivity & Uncertainty Methods
DOI https://doi.org/10.1051/epjconf/202124715009
Published online 22 February 2021
  1. S. Choi, C. Lee, D. Lee, “Resonance treatment using pin-based pointwise energy slowing-down method,” Journal of Computational Physics, 330, pp. 134–155 (2017). [Google Scholar]
  2. B. Ebiwonjumi, S. Choi, et al., “Validation of Lattice Physics Code STREAM for Predicting Pressurized Water Reactor Spent Nuclear Fuel Isotopic Inventory,” Annals of Nuclear Energy, 120, pp. 431–449 (2018). [Google Scholar]
  3. B. Ebiwonjumi, S. Choi, et al., “Verification and validation of radiation source term capabilities in STREAM,” Annals of Nuclear Energy, 124, pp. 80–87 (2019). [Google Scholar]
  4. M. Williams, G. Ilas, et al., “A statistical sampling method for uncertainty analysis with SCALE and XSUSA,” Nucl. Technol. 183 (3), pp. 515–526 (2013). [Google Scholar]
  5. D. A. Rochman, A. Vasilev, et al., “Uncertainties for Swiss LWR spent nuclear fuels due to nuclear data,” EPJ Nucl. Sci. Tech., 4 (6), pp. 1–13 (2018). [Google Scholar]
  6. G. Ilas, G., and H. Liljenfeldt, “Decay heat uncertainty for BWR used fuel due to modeling and nuclear data uncertainties,” Nucl. Eng. Des., 319, pp. 176–184 (2017). [Google Scholar]
  7. D. Rochman, A. Vasiliev, H. Ferroukhi et al., “Best Estimate Plus Uncertainty Analysis for the 244Cm Prediction in Spent Fuel Characterization,” Proceedings of ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), Real Collegio, Lucca, Italy, May 13 –19, 2018. [Google Scholar]
  8. K. Sargsyan, Handbook of uncertainty quantification, Chapter 19, Springer International Publishing Switzerland (2017). [Google Scholar]
  9. L. Gilli, D. Lathouwers, et al., “Uncertainty quantification for criticality problems using non-intrusive and adaptive Polynomial Chaos techniques,” Ann. Nucl. Energy. 56, pp. 71–80 (2013). [Google Scholar]
  10. Z. Perkó, D. Lathouwers, et al., “Large scale applicability of a Fully Adaptive Non-Intrusive Spectral Projection technique: Sensitivity and uncertainty analysis of a transient,” Ann. Nucl. Energy. 71, pp. 272–292 (2014). [Google Scholar]
  11. S. Marelli, and B. Surety, UQLab: A framework for uncertainty quantification in Matlab, Proc. 2nd Int. Conf. on Vulnerability, Risk Analysis and Management (ICVRAM2014), Liverpool, United Kingdom, pp. 2554–2563 (2014). [Google Scholar]
  12. J. Feinberg, H.P. Langtangen, “Chaospy: An open source tool for designing methods of uncertainty quantification,” Journal of Computational Science, 11, pp. 45–57 (2015). [Google Scholar]
  13. SKB, Measurements of decay heat in spent nuclear fuel at Swedish interim storage facility, CLAB. Svensk Kärnbränslehantering AB (SKB). Swedish Nuclear Fuel and Waste Management Co. (R-05-62) (2006). [Google Scholar]
  14. S. Choi, K. Smith, H.C. Lee, D. Lee, “Impact of inflow transport approximation on light water reactor analysis,” Journal of Computational Physics, 299, pp. 352–373 (2015). [Google Scholar]
  15. C. Prieur, Handbook of uncertainty quantification, Chapter 35, Springer International Publishing Switzerland (2017). [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.