References
- R.P. Martin, A. Petruzzi, Progress in international best estimate Plus uncertainty analysis methodologies, Nucl. Eng. Des. 374 (2021), 111033.
- A. Aures, et al., Reactor simulations with nuclear data uncertainties, Nucl. Eng. Deg. 355 (2019), 110313.
- M. Pusa, Incorporating sensitivity and uncertainty analysis to a lattice physics code with application to CASMO-4, Ann. Nucl. Energy 40 (2012) 153-162. https://doi.org/10.1016/j.anucene.2011.10.013
- M.L. Williams, et al., A statistical sampling method for uncertainty analysis with SCALE and XSUSA, Nucl. Technol. 183 (2013) 515-526. https://doi.org/10.13182/NT12-112
- A.J. Koning, D. Rochman, Towards sustainable nuclear energy; putting nuclear physics to work, Ann. Nucl. Energy 35 (2008) 2024-2030. https://doi.org/10.1016/j.anucene.2008.06.004
- SCALE, A Modular Code System for Performing Standardized Computer Analyses for Licensing Evaluation Version 6, 2009. ORNL/TM-2005/39.
- H.J. Shim, C.H. Kim, Adjoint sensitivity and uncertainty analyses in Monte Carlo forward calculations, J. Nucl. Sci. Technol. 48 (2011) 1453-1461. https://doi.org/10.1080/18811248.2011.9711838
- H.J. Shim, et al., McCARD: an Monte Carlo code for advanced reactor design and analysis, Nucl. Eng. Tech. 44 (2) (2012) 161-176. https://doi.org/10.5516/NET.01.2012.503
- K. Ivanov, et al., Benchmark for Uncertainty Analysis in Modelling (UAM) for Design, Operation and Safety Analysis of LWRs, Volume I: Specification and Support Data for the Neutronics Cases (Phase I)," NEA/NSC/DOC(2012), OECD Nuclear Energy Agency, 2012.
- H.J. Park, H.J. Shim, C.H. Kim, Uncertainty propagation in Monte Carlo depletion analysis, Nucl. Sci. Eng. 167 (2011) 196-208. https://doi.org/10.13182/NSE09-106
- G.E.P. Box, Mervin E. Muller, A note on the generation of random number deviates, Ann. Math. Statist. 29 (2) (1958) 610-611. https://doi.org/10.1214/aoms/1177706645
- H.J. Park, et al., Generation of few-group diffusion theory constants by Monte Carlo code McCARD, Nucl. Sci. Eng. 172 (2012) 66-77. https://doi.org/10.13182/NSE11-22
- H.J. Park, et al., Implementation of cross section random sampling code system for direct sampling method in continuous energy Monte Carlo calculations, Trans. Korean Nucl. Soc. Virtual Meet. (2020). July 9-10, Korea.
- R. Macfarlane, et al., The NJOY Nuclear Data Processing System Version," LAUR-17-20093, Los Alamos National Laboratory, NW, USA, 2016.
- International Handbook of Evaluated Criticality Safety Benchmark Experiments," September 2010 Edition, available on DVD-ROM, NEA/NSC/DOC(95) 03.
- International Handbook of Evaluated Reactor Physics Benchmark Experiments," March 2010 Edition, available on DVD-ROM, NEA/NSC/DOC(2006)1.
- W. Zwermann, et al., Status of XSUSA for sampling based nuclear data uncertainty and sensitivity analysis, EPJ Web Conf. 42 (2013), 03003.
- T. Takeda, et al., Estimation of error propagation in Monte-Carlo burnup calculations," J, Nucl. Sci. Technol. 36 (1999) 738.
- H.J. Shim, C.H. Kim, Error propagation module implemented in the MC-CARD Monte Carlo code, Trans. Am. Nucl. Soc. 86 (2002) 325.
- N. Garcia-Herranz, et al., Propagation of statistical and nuclear data uncertainties in Monte Carlo burnup calculations, Ann. Nucl. Energy 35 (2008) 185.
- H.J. Park, et al., Uncertainty propagation analysis for PWR burnup pin-cell benchmark by Monte Carlo code McCARD, Sci. Technol. Nucl. Install. (2012), 616253, (2012).