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http://dx.doi.org/10.1016/j.net.2022.06.012

McCARD/MIG stochastic sampling calculations for nuclear cross section sensitivity and uncertainty analysis  

Ho Jin Park (Kyung Hee University)
Publication Information
Nuclear Engineering and Technology / v.54, no.11, 2022 , pp. 4272-4279 More about this Journal
Abstract
In this study, a cross section stochastic sampling (S.S.) capability is implemented into both the McCARD continuous energy Monte Carlo code and MIG multiple-correlated data sampling code. The ENDF/B-VII.1 covariance data based 30 group cross section sets and the SCALE6 covariance data based 44 group cross section sets are sampled by the MIG code. Through various uncertainty quantification (UQ) benchmark calculations, the McCARD/MIG results are verified to be consistent with the McCARD stand-alone sensitivity/uncertainty (S/U) results and the XSUSA S.S. results. UQ analyses for Three Mile Island Unit 1, Peach Bottom Unit 2, and Kozloduy-6 fuel pin problems are conducted to provide the uncertainties of keff and microscopic and macroscopic cross sections by the McCARD/MIG code system. Moreover, the SNU S/U formulations for uncertainty propagation in a MC depletion analysis are validated through a comparison with the McCARD/MIG S.S. results for the UAM Exercise I-1b burnup benchmark. It is therefore concluded that the SNU formulation based on the S/U method has the capability to accurately estimate the uncertainty propagation in a MC depletion analysis.
Keywords
Stochastic sampling; Uncertainty quantification; Monte Carlo; McCARD; MIG; UAM;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 M. Pusa, Incorporating sensitivity and uncertainty analysis to a lattice physics code with application to CASMO-4, Ann. Nucl. Energy 40 (2012) 153-162.   DOI
2 M.L. Williams, et al., A statistical sampling method for uncertainty analysis with SCALE and XSUSA, Nucl. Technol. 183 (2013) 515-526.   DOI
3 A.J. Koning, D. Rochman, Towards sustainable nuclear energy; putting nuclear physics to work, Ann. Nucl. Energy 35 (2008) 2024-2030.   DOI
4 SCALE, A Modular Code System for Performing Standardized Computer Analyses for Licensing Evaluation Version 6, 2009. ORNL/TM-2005/39.
5 H.J. Shim, C.H. Kim, Adjoint sensitivity and uncertainty analyses in Monte Carlo forward calculations, J. Nucl. Sci. Technol. 48 (2011) 1453-1461.   DOI
6 H.J. Shim, et al., McCARD: an Monte Carlo code for advanced reactor design and analysis, Nucl. Eng. Tech. 44 (2) (2012) 161-176.   DOI
7 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.
8 H.J. Park, H.J. Shim, C.H. Kim, Uncertainty propagation in Monte Carlo depletion analysis, Nucl. Sci. Eng. 167 (2011) 196-208.   DOI
9 G.E.P. Box, Mervin E. Muller, A note on the generation of random number deviates, Ann. Math. Statist. 29 (2) (1958) 610-611.   DOI
10 H.J. Park, et al., Generation of few-group diffusion theory constants by Monte Carlo code McCARD, Nucl. Sci. Eng. 172 (2012) 66-77.   DOI
11 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.
12 R. Macfarlane, et al., The NJOY Nuclear Data Processing System Version," LAUR-17-20093, Los Alamos National Laboratory, NW, USA, 2016.
13 International Handbook of Evaluated Criticality Safety Benchmark Experiments," September 2010 Edition, available on DVD-ROM, NEA/NSC/DOC(95) 03.
14 International Handbook of Evaluated Reactor Physics Benchmark Experiments," March 2010 Edition, available on DVD-ROM, NEA/NSC/DOC(2006)1.
15 W. Zwermann, et al., Status of XSUSA for sampling based nuclear data uncertainty and sensitivity analysis, EPJ Web Conf. 42 (2013), 03003.
16 T. Takeda, et al., Estimation of error propagation in Monte-Carlo burnup calculations," J, Nucl. Sci. Technol. 36 (1999) 738.
17 H.J. Shim, C.H. Kim, Error propagation module implemented in the MC-CARD Monte Carlo code, Trans. Am. Nucl. Soc. 86 (2002) 325.
18 N. Garcia-Herranz, et al., Propagation of statistical and nuclear data uncertainties in Monte Carlo burnup calculations, Ann. Nucl. Energy 35 (2008) 185.
19 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).
20 R.P. Martin, A. Petruzzi, Progress in international best estimate Plus uncertainty analysis methodologies, Nucl. Eng. Des. 374 (2021), 111033.
21 A. Aures, et al., Reactor simulations with nuclear data uncertainties, Nucl. Eng. Deg. 355 (2019), 110313.