References
- K. IVANOV et al., “Benchmark for Uncertainty Analysis in Modelling (UAM) for the Design, Operation and Safety Analysis of LWRs,” NEA/NSC/DOC(2013)7, Organisation for Economic Co-operation and Development, Nuclear Energy Agency (2013).
- W. WIESELQUIST et al., “PSI Methodologies for Nuclear Data Uncertainty Propagation with CASMO-5M and MCNPX: Results for OECD/NEA UAM Benchmark Phase I,” Sci. Technol. Nucl. Install., 2013 (2013).
- A. YANKOV et al., “Comparison of XSUSA and Two Step Approaches for Full-Core Uncertainty Quantification,” Proc. Int. Conf. Physics of Reactors (PHYSOR 12), Knoxville, Tennessee, April 15–20, 2012, American Nuclear Society (2012).
- B. FOAD and T. TAKEDA, “Sensitivity and Uncertainty Analysis for UO2 and MOX Fueled PWR Cells,” Ann. Nucl. Energy, 75, 595 (2015); https://doi.org/https://doi.org/10.1016/j.anucene.2014.08.068.
- D. ROCHMAN et al., “Nuclear Data Uncertainties for Typical LWR Fuel Assemblies and a Simple Reactor Core,” Nucl. Data Sheets, 139, 1 (2017); https://doi.org/https://doi.org/10.1016/j.nds.2017.01.001.
- M. D. McKAY, R. J. BECKMAN, and W. J. CONOVER, “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code,” Technometrics, 21, 239 (1979).
- R. L. IMAN and W. CONOVER, “Sensitivity Analysis Techniques: Self-Teaching Curriculum,” NUREG/CR2350, SAND81-1978, Sandia National Laboratories (1982).
- Y. KANDIEV and O. ZATSEPIN, “Importance Sampling Implemented in the Code Prizma for Deep Penetration and Detection Problems in Reactor Physics,” Proc. SNA + MC 2013 Joint Int. Conf. Supercomputing in Nuclear Applications + Monte Carlo, Paris, France, October 27–31, 2013, p. 3301 EDP Sciences (2014).
- C. WAN et al., “Code Development for Eigenvalue Total Sensitivity Analysis and Total Uncertainty Analysis,” Ann. Nucl. Energy, 85, 788 (2015); https://doi.org/https://doi.org/10.1016/j.anucene.2015.06.036.
- 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/https://doi.org/10.13182/NSE15-96.
- H. W. LEWIS et al., “Risk Assessment Review Group Report to the U.S. Nuclear Regulatory Commission,” IEEE Trans. Nucl. Sci., 26, 5, 4686 (1979); https://doi.org/https://doi.org/10.1109/TNS.1979.4330198.
- 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).
- Z. SUI et al., “Covariance-Oriented Sample Transformation: A New Sampling Method for Reactor-Physics Uncertainty Analysis,” Ann. Nucl. Energy, 134, 452 (2019); https://doi.org/https://doi.org/10.1016/j.anucene.2019.07.001.
- W. ZWERMANN et al., “Status of XSUSA for Sampling Based Nuclear Data Uncertainty and Sensitivity Analysis,” EPJ Web of Conferences, 42, 03003 (2013).
- M. WILLIAMS et al., “Development of a Statistical Sampling Method for Uncertainty Analysis with Scale,” Proc. Int. Conf. Physics of Reactors (PHYSOR 12), Knoxville, Tennessee, April 15–20, 2012, American Nuclear Society (2012).
- D. ROCHMAN et al., “Nuclear Data Uncertainty Propagation: Total Monte Carlo vs. Covariances,” J. Korean Phys., 59, 2, 1236 (2011); https://doi.org/https://doi.org/10.3938/jkps.59.1236.
- D. ROCHMAN et al., “Efficient Use of Monte Carlo: Uncertainty Propagation,” Nucl. Sci. Eng., 177, 3, 337 (2014); https://doi.org/https://doi.org/10.13182/NSE13-32.
- J. C. HELTON et al., “Survey of Sampling-Based Methods for Uncertainty and Sensitivity Analysis,” Reliab. Eng. Syst. Saf., 91, 10–11, 1175 (2006); https://doi.org/https://doi.org/10.1016/j.ress.2005.11.017.
- C. J. DIEZ et al., “Comparison of Nuclear Data Uncertainty Propagation Methodologies for PWR Burn-Up Simulations,” Ann. Nucl. Energy, 77, 101 (2015); https://doi.org/https://doi.org/10.1016/j.anucene.2014.10.022.
- P. CHEN and A. QUARTERONI, “A New Algorithm for High-Dimensional Uncertainty Quantification Based on Dimension-Adaptive Sparse Grid Approximation and Reduced Basis Methods,” J. Comput. Phys., 298, 176 (2015); https://doi.org/https://doi.org/10.1016/j.jcp.2015.06.006.
- M. L. WILLIAMS et al., “Scale Nuclear Data Covariance Library,” ORNL/TM-2005/39, Oak Ridge National Laboratory (2011).
- M. BALL, D. NOVOG, and J. LUXAT, “Analysis of Implicit and Explicit Lattice Sensitivities Using DRAGON,” Nucl. Eng. Des., 265, 1 (2013); https://doi.org/https://doi.org/10.1016/j.nucengdes.2013.07.011.
- D. XU, Z. CHEN, and L. YANG, “Scenario Tree Generation Approaches Using K-Means and LP Moment Matching Methods,” J. Comput. Appl. Math., 236, 17, 4561 (2012); https://doi.org/https://doi.org/10.1016/j.cam.2012.05.020.
- K. HØYLAND and S. W. WALLACE, “Generating Scenario Trees for Multistage Decision Problems,” Manage. Sci., 47, 2, 295 (2001); https://doi.org/https://doi.org/10.1287/mnsc.47.2.295.9834.
- A. SAHLBERG, “Ensemble for Deterministic Sampling with Positive Weights: Uncertainty Quantification with Deterministically Chosen Samples,” UPPSALA University (2016).
- S. OLADYSHKIN and W. NOWAK, “Data-Driven Uncertainty Quantification Using the Arbitrary Polynomial Chaos Expansion,” Reliab. Eng. Syst. Saf., 106, 179 (2012); https://doi.org/https://doi.org/10.1016/j.ress.2012.05.002.
- L. GUO, Y. LIU, and T. ZHOU, “Data-Driven Polynomial Chaos Expansions: A Weighted Least-Square Approximation,” J. Comput. Phys., 381, 129 (2019); https://doi.org/https://doi.org/10.1016/j.jcp.2018.12.020.
- L. GUO, A. NARAYAN, and T. ZHOU, “Constructing Least-Squares Polynomial Approximations,” SIAM Rev., 62, 2, 483 (2020); https://doi.org/https://doi.org/10.1137/18M1234151.
- G. C. PFLUG, “Scenario Tree Generation for Multiperiod Financial Optimization by Optimal Discretization,” Math. Program., 89, 2, 251 (2001); https://doi.org/https://doi.org/10.1007/PL00011398.
- N. GROWE-KUSKA, H. HEITSCH, and W. RÖMISCH, “Scenario Reduction and Scenario Tree Construction for Power Management Problems,” Proc. 2003 IEEE Bologna Power Tech Conf., Bologna, Italy, June 23–26, 2003, Vol. 3, p. 23, IEEE (2003).
- H. HEITSCH and W. RÖMISCH, “Scenario Tree Modeling for Multistage Stochastic Programs,” Math. Program., 118, 2, 371 (2009); https://doi.org/https://doi.org/10.1007/s10107-007-0197-2.
- K. HØYLAND, M. KAUT, and S. W. WALLACE, “A Heuristic for Moment-Matching Scenario Generation,” Comput. Optim. Appl., 24, 2–3, 169 (2003); https://doi.org/https://doi.org/10.1023/A:1021853807313.
- X. JI et al., “A Stochastic Linear Goal Programming Approach to Multistage Portfolio Management Based on Scenario Generation via Linear Programming,” IIE Trans., 37, 10, 957 (2005); https://doi.org/https://doi.org/10.1080/07408170591008082.
- A. YAMAMOTO et al., “Uncertainty Quantification of LWR Core Characteristics Using Random Sampling Method,” Nucl. Sci. Eng., 181, 2, 160 (2015); https://doi.org/https://doi.org/10.13182/NSE14-152.
- R. VANHANEN et al., “Quality Assurance Methods for Uncertainty Analysis in Reactor Physics with Applications,” Aalto University (2016).
- M. CHADWICK et al., “ENDF/B-VII.1 Nuclear Data for Science and Technology: Cross Sections, Covariances, Fission Product Yields and Decay Data,” Nucl. Data Sheets, 112, 12, 2887 (2011); https://doi.org/https://doi.org/10.1016/j.nds.2011.11.002.
- Y. LI et al., “Development and Verification of PWR-Core Fuel Management Calculation Code System NECP-Bamboo: Part I Bamboo-Lattice,” Nucl. Eng. Des., 335, 432 (2018); https://doi.org/https://doi.org/10.1016/j.nucengdes.2018.05.030.