Development and Verification of Sampling Code NECP-SOUL for Evaluated Nuclear Data Files in ENDF-6 Format

References

• A. BUCALOSSI et al., “Comparison Between Best-Estimate–Plus–Uncertainty Methods and Conservative Tools for Nuclear Power Plant Licensing,” Nucl. Technol., 172, 1, 29 (2010); https://doi.org/10.13182/NT172-29.
   Web of Science ®Google Scholar
• A. BUCALOSSI and A. PETRUZZI, “Role of Best Estimate Plus Uncertainty Methods in Major Nuclear Power Plant Modifications,” J. Nucl. Sci. Technol., 47, 8, 671 (2010); https://doi.org/10.1080/18811248.2010.9711643.
   Web of Science ®Google Scholar
• C. J. WERNER et al., “MCNP User’s Manual Code Version 6.2,” p. 310, Los Alamos National Laboratory (Nov. 2017); http://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-17-29981.
   Google Scholar
• B. L. BROADHEAD et al., “Sensitivity- and Uncertainty-Based Criticality Safety Validation Techniques,” Nucl. Sci. Eng., 146, 3, 340 (2004); https://doi.org/10.13182/NSE03-2.
   Web of Science ®Google Scholar
• A. HARA, T. TAKEDA and Y. KIKUCHI, “SAGEP: Two-dimensional Sensitivity Analysis Code Based on Generalized Perturbation Theory,” p. 1, Japan Atomic Energy Agency (Feb. 1984); https://doi.org/10.11484/jaeri-m-84-027.
   Google Scholar
• J. MA et al., “Generalized Perturbation Theory Based Total Sensitivity and Uncertainty Analysis for High-Fidelity Neutronics Calculation,” Front. Energy Res., 9, (2021); https://doi.org/10.3389/fenrg.2021.743642.
   Web of Science ®Google Scholar
• J. E. SWEEZY et al., “MCNP — A General Monte Carlo N-Particle Transport Code, Version 5−Volume 3: Developer’s Guide,” p. 115, Los Alamos National Laboratory (Apr. 2003).
   Google Scholar
• M. WILLIAMS et al., “Development of a Statistical Sampling Method for Uncertainty Analysis with SCALE,” presented at the Int. Conf. on the Physics of Reactors 2012 (PHYSOR 2012), Apr 15−20, 2012.
   Google Scholar
• W. ZWERMANN et al., “Status of XSUSA for Sampling Based Nuclear Data Uncertainty and Sensitivity Analysis,” EPJ Web Conf., 42, 03003 (2013); https://doi.org/10.1051/epjconf/20134203003.
   Google Scholar
• T. ZHU et al., “NUSS: A Tool for Propagating Multigroup Nuclear Data Covariances in Pointwise Ace-Formatted Nuclear Data Using Stochastic Sampling Method,” Ann. Nucl. Energy, 75, 713 (2015); https://doi.org/10.1016/j.anucene.2014.09.013.
   Web of Science ®Google Scholar
• T. ZU et al., “Total Uncertainty Analysis for PWR Assembly Based on the Statistical Sampling Method,” Nucl. Sci. Eng., 183, 3, 371 (2016); https://doi.org/10.13182/NSE15-96.
   Web of Science ®Google Scholar
• R. E. MACFARLANE and A. C. KAHLER, “Methods for Processing ENDF/B-VII with NJOY,” Nucl. Data Sheets, 111, 12, 2739 (2010); https://doi.org/10.1016/j.nds.2010.11.001.
   Google Scholar
• X. ZOU et al., “Uncertainty Propagation of Prompt Fission Neutron Spectrum for Physics Analysis of Fast and Thermal Reactors,” Prog. Nucl. Energy, 144, 104077 (2022); https://doi.org/10.1016/j.pnucene.2021.104077.
   Web of Science ®Google Scholar
• T. ZU et al., “NECP-Atlas: A New Nuclear Data Processing Code,” Ann. Nucl. Energy, 123, 153 (2019); https://doi.org/10.1016/j.anucene.2018.09.016.
   Web of Science ®Google Scholar
• A. J. KONING and D. ROCHMAN, “Towards Sustainable Nuclear Energy: Putting Nuclear Physics to Work,” Ann. Nucl. Energy, 35, 11, 2024 (2008); https://doi.org/10.1016/j.anucene.2008.06.004.
   Web of Science ®Google Scholar
• A. J. KONING, S. HILAIRE, and M. C. DUIJVESTIJN, “TALYS-1.0,” EDP Sciences, ND 2007, 058 (2007); https://doi.org/10.1051/ndata:07767.
   Google Scholar
• O. BUSS, A. HOEFER, and J. C. NEUBER, “NUDUNA-Nuclear Data Uncertainty Analysis,” Proc. Int. Conf. on Nuclear Criticality (ICNC 2011), Edinburgh, Scotland (2011).
   Google Scholar
• L. FIORITO et al., “Nuclear Data Uncertainty Propagation to Integral Responses Using SANDY,” Ann. Nucl. Energy, 101, 359 (2017); https://doi.org/10.1016/j.anucene.2016.11.026.
   Web of Science ®Google Scholar
• D. A. BROWN, “ENDF-6 Formats Manual—Data Formats and Procedures for the Evaluated Nuclear Data Files ENDF/B-VI, ENDF/B-VII and ENDF/B-VIII,” p. 73, Brookhaven National Laboratory (2023); https://doi.org/10.2172/2007538.
   Google Scholar
• C. J. DÍEZ et al., “Comparison of Nuclear Data Uncertainty Propagation Methodologies for PWR Burn-up Simulations,” Ann. Nucl. Energy, 77, 101 (2015); https://doi.org/10.1016/j.anucene.2014.10.022.
   Web of Science ®Google Scholar
• L. FIORITO, J. DYRDA, and M. FLEMING, “JEFF-3.3 Covariance Application to ICSBEP Using SANDY and NDAST,” EPJ Web Conf., 211, 07003 (2019); https://doi.org/10.1051/epjconf/201921107003.
   Google Scholar
• 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); https://doi.org/10.1016/j.nds.2018.02.001.
   Google Scholar
• N. YAMANO et al., “Crucial Importance of Correlation Between Cross Sections and Angular Distributions in Nuclear Data of 28Si on Estimation of Uncertainty of Neutron Dose Penetrating a Thick Concrete,” J. Nucl. Sci. Technol., 59, 5, 641 (2022); https://doi.org/10.1080/00223131.2021.1997665.
   Web of Science ®Google Scholar
• N. YAMANO et al., “Effects of Correlations in Uncertainties of Total Cross Section and Elastic Angular Distribution for a Deep Penetration of 14-MeV Neutrons in Cu,” J. Nucl. Sci. Technol., 61, 1, 74 (2024); https://doi.org/10.1080/00223131.2023.2272759.
   Web of Science ®Google Scholar
• G. CHIBA, “ERRORJ: A Code to Process Neutron-Nuclide Reaction Cross Section Covariance, Version 2.3,” p. 1, Japan Atomic Energy Agency (2007); https://doi.org/10.11484/jaea-data-code-2007-007.
   Google Scholar
• J. C. HELTON and F. J. DAVIS, “Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems,” Reliab. Eng. Syst. Saf., 81, 1, 23 (2003); https://doi.org/10.1016/s0951-8320(03)00058-9.
   Web of Science ®Google Scholar
• A. P. DEMPSTER, M. SCHATZOFF, and N. WERMUTH, “A Simulation Study of Alternatives to Ordinary Least Squares,” J. Am. Stat. Assoc., 72, 357, 77 (1977); https://doi.org/10.1080/01621459.1977.10479910.
   Web of Science ®Google Scholar
• H. A. L. KIERS, “Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms,” Psychometrika, 62, 2, 251 (1997); https://doi.org/10.1007/bf02295279.
   Web of Science ®Google Scholar
• “ICSBEP Handbook 2020,” Nuclear Energy Agency (2020); https://doi.org/10.1787/7e0ebc50-en (accessed Nov. 15, 2023).
   Google Scholar
• K. IVANOV et al., “Benchmarks for Uncertainty Analysis in Modelling (UAM) for the Design, Operation and Safety Analysis of LWRs - Volume I: Specification and Support Data for Neutronics Cases (Phase I),”p. 33, Organization for Economic Co-operation and Development/Nuclear Energy Agency (May. 2013); https://cms.oecd-nea.org/upload/docs/application/pdf/2020-01/nsc-doc2013-7.pdf.
   Google Scholar
• Q. HE et al., “NECP-MCX: A Hybrid Monte-Carlo-Deterministic Particle-Transport Code for the Simulation of Deep-Penetration Problems,” Ann. Nucl. Energy, 151, 107978 (2021); https://doi.org/10.1016/j.anucene.2020.107978.
   Web of Science ®Google Scholar
• K. YOKOYAMA, A. SHONO, and T. SANDA, “Japan’s Experimental Fast Reactor JOYO Mk-I Core: Sodium-Cooled Uranium-Plutonium Mixed Oxide Fueled Fast Core Surrounded by UO2 Blanket,” International Handbook of Evaluated Reactor Physics Benchmark Experiments, 30, 3, 213 (2006).
   Google Scholar