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http://dx.doi.org/10.1016/j.net.2022.03.001

Propagation of radiation source uncertainties in spent fuel cask shielding calculations  

Ebiwonjumi, Bamidele (Department of Nuclear Engineering, Ul san National Institute of Science and Technology)
Mai, Nhan Nguyen Trong (Department of Nuclear Engineering, Ul san National Institute of Science and Technology)
Lee, Hyun Chul (Nuclear Engineering Division, School of Mechanical Engineering, Pusan National University)
Lee, Deokjung (Department of Nuclear Engineering, Ul san National Institute of Science and Technology)
Publication Information
Nuclear Engineering and Technology / v.54, no.8, 2022 , pp. 3073-3084 More about this Journal
Abstract
The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively.
Keywords
Monte Carlo shielding; STREAM; Spent nuclear fuel; Uncertainty quantification; Stochastic sampling;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 T. Nguyen, et al., Validation of UNIST Monte Carlo code MCS using VERA progression problems, Nucl. Eng. Technol. 52 (5) (2020) 878, https://doi.org/10.1016/j.net.2019.10.023.   DOI
2 H.H. Grady, An Electron/photon/relaxation Data Library for MCNP6," LA-UR-13-27377, Los Alamos National Laboratory, 2015.
3 J. Choe, et al., Verification and validation of STREAM/RAST-K for PWR analysis, Nucl. Eng. Technol. 51 (2) (2019) 356, https://doi.org/10.1016/j.net.2018.10.004.   DOI
4 B. Ebiwonjumi, et al., Validation of lattice physics code STREAM for predicting pressurized water reactor spent nuclear fuel isotopic inventory, Ann. Nucl. Energy 120 (2018) 431, https://doi.org/10.1016/j.anucene.2018.06.002.   DOI
5 B. Ebiwonjumi, et al., Verification and validation of radiation source term capabilities in STREAM, Ann. Nucl. Energy 124 (2019) 80, https://doi.org/10.1016/j.anucene.2018.09.034.   DOI
6 I.C. Gauld, et al., Isotopic depletion and decay methods and analysis capabilities in SCALE, Nucl. Tech. 174 (2) (2011) 169, https://doi.org/10.13182/NT11-3.   DOI
7 M. Lemaire, et al., Verification of photon transport capability of UNIST Monte Carlo code MCS, Comput. Phys. Commun. 231 (2018) 1, https://doi.org/10.1016/j.cpc.2018.05.008.   DOI
8 H. Yun, et al., ' "an efficient evaluation of depletion uncertainty for a GBC-32 dry storage cask with PLUS7 fuel assemblies using the Monte Carlo uncertainty sampling method, Ann. Nucl. Energy 110 (2017) 679, https://doi.org/10.1016/j.anucene.2017.07.020.   DOI
9 J. Jang, et al., Uncertainties of PWR spent nuclear fuel isotope inventory for back-end cycle analysis with STREAM/RAST-K, Ann. Nucl. Energy 158 (2021) 108267, https://doi.org/10.1016/j.anucene.2021.108267.   DOI
10 B.T. Rearden, et al., TSUNAMI Primer: A Primer for Sensitivity/Uncertainty Calculations with SCALE," ORNL/TM-2009/027, Oak Ridge National Laboratory, 2009.
11 S. Goluoglu, et al., Monte Carlo criticality methods and analysis capabilities in SCALE, Nucl. Tech. 174 (2) (2011) 214, https://doi.org/10.13182/NT10-124.   DOI
12 A. Haghighat, J.C. Wagner, Monte Carlo variance reduction with deterministic importance functions, Prog. Nucl. Energy 42 (1) (2003) 25, https://doi.org/10.1016/S0149-1970(02)00002-1.   DOI
13 G. Ilas, H. Liljenfeldt, Decay heat uncertainty for BWR used fuel due to modeling and nuclear data uncertainties, Nucl. Eng. Des. 319 (2017) 176, https://doi.org/10.1016/j.nucengdes.2017.05.009.   DOI
14 I.C. Gauld, U. Mertyurek, Validation of BWR spent nuclear fuel isotopic predictions with applications to burnup credit, Nucl. Eng. Des. 345 (2019) 110, https://doi.org/10.1016/j.nucengdes.2019.01.026.   DOI
15 H. Lee, et al., MCS - a Monte Carlo particle transport code for large-scale power reactor analysis, Ann. Nucl. Energy 139 (2020) 107276, https://doi.org/10.1016/j.anucene.2019.107276.   DOI
16 D.E. Peplow, Monte Carlo shielding analysis capabilities with MAVRIC, Nucl. Tech. 174 (2) (2011) 289, https://doi.org/10.13182/NT174-289.   DOI
17 N.N.T. Mai, et al., Extension of Monte Carlo code MCS to spent fuel cask shielding analysis, Int. J. Energy Res. 44 (10) (2020) 8089, https://doi.org/10.1002/er.5023.   DOI
18 B. Ebiwonjumi, et al., Uncertainty quantification of PWR spent fuel due to nuclear data and modeling parameters, Nucl. Eng. Technol. 53 (3) (2021) 715, https://doi.org/10.1016/j.net.2020.07.012.   DOI
19 M.I. Radaideh, D. Price, T. Kozlowski, On using computational versus datadriven methods for uncertainty propagation of isotopic uncertainties, Nucl. Eng. Technol. 52 (6) (2020) 1148, https://doi.org/10.1016/j.net.2019.11.029.   DOI
20 M. Williams, et al., A statistical sampling method for uncertainty analysis with SCALE and XSUSA, Nucl. Tech. 183 (3) (2013) 515, https://doi.org/10.13182/NT12-112.   DOI
21 P. Zhang, et al., Development of a Variance Reduction Scheme in the MCS Monte Carlo Code, Transactions of the Korean Nuclear Society Autumn Meeting, Yeosu, Korea, 2018, pp. 24-26. October.
22 International Commission on Radiological Protection, Corrigenda to ICRP publication 116: conversion coefficients for radiological protection quantities for external radiation exposures, Ann. ICRP, Publication 116 (2010) 40, https://doi.org/10.1177/0146645315577925, 2-5.   DOI
23 B. Foad, A. Yamamoto, T. Endo, Uncertainty and regression analysis of the MSLB accident in PWR based on unscented transformation and low rank approximation, Ann. Nucl. Energy 143 (2020) 107493, https://doi.org/10.1016/j.anucene.2020.107493.   DOI
24 T. Goorley, et al., Initial MCNP6 release overview, Nucl. Tech. 180 (3) (2012) 298, https://doi.org/10.13182/NT11-135.   DOI
25 J. Sweezy, et al., Automated variance reduction for MCNP using deterministic methods, Radiat. Protect. Dosim. 116 (2005) 508, https://doi.org/10.1093/rpd/nci257.   DOI
26 Y. Gao, et al., Radiation dose rate distributions of spent fuel dry casks estimated with MAVRIC based on detailed geometry and continuous-energy models, Ann. Nucl. Energy 117 (2018) 84, https://doi.org/10.1016/j.anucene.2018.03.015.   DOI
27 International Commission on Radiological Protection, Conversion coefficients for radiological protection quantities for external radiation exposures, Ann. ICRP, Publication 116 (2-5) (2010) 40, https://doi.org/10.1016/j.icrp.2011.10.001.   DOI
28 M. Lemaire, H. Lee, D. Lee, Implementation of Tally Convergence Tests in UNIST Monte Carlo Code MCS, Transactions of the Korean Nuclear Society Autumn Meeting, Yeosu, Korea, 2018, pp. 24-26. October.
29 T. Endo, T. Watanabe, A. Yamamoto, Confidence interval estimation by bootstrap method for uncertainty quantification using random sampling method, J. Nucl. Sci. Tech. 52 (2015) 993, https://doi.org/10.1080/00223131.2015.1034216.   DOI
30 J. Jang, et al., Validation of UNIST Monte Carlo code MCS for criticality safety analysis of PWR spent fuel pool and storage cask, Ann. Nucl. Energy 114 (2018) 495, https://doi.org/10.1016/j.anucene.2017.12.054.   DOI
31 U.S.NRC, Packaging and transportation of radioactive material, 10 CFR 72," United States nuclear regulatory commission. https://www.nrc.gov/readingrm/doc-collections/cfr/part072/. (Accessed 1 September 2021).
32 U.S.NRC, TN-32 generic technical specification," United States nuclear regulatory commission. https://www.nrc.gov/docs/ML0104/ML010460423.pdf. (Accessed 1 September 2021).
33 S. Choi, et al., Resonance self-shielding methodology of new neutron transport code STREAM, J. Nucl. Sci. Technol. 52 (9) (2015) 1133, https://doi.org/10.1080/00223131.2014.993738.   DOI
34 L. Fiorito, et al., Generation of fission yield covariances to correct discrepancies in the nuclear data libraries, Ann. Nucl. Energy 88 (2016) 12, https://doi.org/10.1016/j.anucene.2015.10.027.   DOI
35 J.H. Ko, et al., Shielding analysis of dual purpose casks for spent nuclear fuel under normal storage conditions, Nucl. Tech. 46 (4) (2014) 547, https://doi.org/10.5516/NET.08.2013.039.   DOI
36 Inc Transnuclear, TN-32 dry storage cask system safety evaluation report," United States nuclear regulatory commission. https://www.nrc.gov/docs/ML0036/ML003696918.pdf. (Accessed 1 September 2021).
37 A.B. Svensk Karnbranslehantering, Measurements of Decay Heat in Spent Nuclear Fuel at the Swedish Interim Storage Facility, CLAB," R-05-62, Swedish Nuclear Fuel and Waste Management Co, 2006.
38 S. Choi, C. Lee, D. Lee, Resonance treatment using pin-based pointwise energy slowing-down method, J. Comput. Phys. 330 (2017) 134, https://doi.org/10.1016/j.jcp.2016.11.007.   DOI
39 S. Choi, A. Khassenov, D. Lee, Resonance self-shielding method using resonance interference factor library for practical lattice physics computations of LWRs, J. Nucl. Sci. Technol. 53 (8) (2016) 1142, https://doi.org/10.1080/00223131.2015.1095686.   DOI
40 D. Rochman, et al., Best estimate plus uncertainty analysis for the 244Cm prediction in spent fuel characterization, in: Proc. ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), Real Collegio, Lucca, Italy, 2018, pp. 13-19. May.
41 L. Fiorito, et al., Nuclear data uncertainty propagation to integral responses using SANDY, Ann. Nucl. Energy 101 (2017) 359, https://doi.org/10.1016/j.anucene.2016.11.026.   DOI
42 A. Yamamoto, et al., Uncertainty quantification of LWR core characteristics using random sampling method, Nucl. Sci. Eng. 181 (2) (2015) 160, https://doi.org/10.13182/NSE14-152.   DOI