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Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout

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  • Guo, Zehua
  • Dailey, Ryan
  • Feng, Tangtao
  • Zhou, Yukun
  • Sun, Zhongning
  • Corradini, Michael L
  • Wang, Jun

Abstract

The deposition of protective coatings on nuclear fuel cladding has been considered as a near-term Accident Tolerant Fuel (ATF) concept that will reduce the high-temperature oxidation rate and enhance accident tolerance of the cladding while providing additional benefits during normal operation and transients. In this study, an uncertainty analysis was employed to investigate the potential benefits of ATF Cr-coated-Zr cladding and canister for an unmitigated Short-Term Station Blackout (STSBO) sequence in a generic BWR plant using the MELCOR systems code. The MELCOR parameters that reflect the current state-of-knowledge of the relevant fuel assembly performance during core degradation were selected and characterized according to their ranges and distributions. An extensive set of simulations (240 MELCOR calculations) were performed for the Zr and Cr-coated-Zr cladding and canister materials, respectively, to determine their effect on core degradation with the associated uncertainties. The comparison between the Zr and Cr-coated-Zr calculations confirms that the use of ATF Cr-coated-Zr as cladding and canister component material in BWR might be an effective way to mitigate the accident progression and reduce the total hydrogen generation during the accident. The core degradation process was only delayed by less than a half hour, providing some additional time for compensatory actions to mitigate with the accident progression. In contrast, the effect of coated materials on total hydrogen generation was more substantial; i.e., hydrogen generation was almost reduced by half. In addition, a sensitivity analysis based on the Pearson and Spearman correlation coefficients was conducted to rank the significance of the considered parameter uncertainties. The Cr-coating failure temperature was identified as the dominant factor in the MELCOR simulations of core degradation and associated hydrogen generation. Understanding these effects will inform and guide researchers to focus on a more productive area of research and development for accident-tolerant fuel concepts and enhancement of core safety margins.

Suggested Citation

  • Guo, Zehua & Dailey, Ryan & Feng, Tangtao & Zhou, Yukun & Sun, Zhongning & Corradini, Michael L & Wang, Jun, 2021. "Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
  • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002970
    DOI: 10.1016/j.ress.2021.107770
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    1. Radaideh, Majdi I. & Borowiec, Katarzyna & Kozlowski, Tomasz, 2019. "Integrated framework for model assessment and advanced uncertainty quantification of nuclear computer codes under Bayesian statistics," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 357-377.
    2. Galushin, Sergey & Grishchenko, Dmitry & Kudinov, Pavel, 2020. "Implementation of framework for assessment of severe accident management effectiveness in Nordic BWR," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Zugazagoitia, Eneko & Queral, Cesar & Fernández-Cosials, Kevin & Gómez, Javier & Durán, Luis Felipe & Sánchez-Torrijos, Jorge & Posada, José María, 2020. "Uncertainty and sensitivity analysis of a PWR LOCA sequence using parametric and non-parametric methods," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
    5. Reyes-Fuentes, Melisa & del-Valle-Gallegos, Edmundo & Duran-Gonzalez, Julian & Ortíz-Villafuerte, Javier & Castillo-Durán, Rogelio & Gómez-Torres, Armando & Queral, Cesar, 2021. "AZTUSIA: A new application software for Uncertainty and Sensitivity analysis for nuclear reactors," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Maljovec, D. & Liu, S. & Wang, B. & Mandelli, D. & Bremer, P.-T. & Pascucci, V. & Smith, C., 2016. "Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 262-276.
    7. Zheng, Xiaoyu & Itoh, Hiroto & Kawaguchi, Kenji & Tamaki, Hitoshi & Maruyama, Yu, 2015. "Application of Bayesian nonparametric models to the uncertainty and sensitivity analysis of source term in a BWR severe accident," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 253-262.
    8. Karanki, D.R. & Rahman, S. & Dang, V.N. & Zerkak, O., 2017. "Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: Tradeoff between accuracy and accident simulations," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 91-102.
    9. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    10. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    11. Di Maio, Francesco & Picoco, Claudia & Zio, Enrico & Rychkov, Valentin, 2017. "Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 122-138.
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    1. Piotr Darnowski & Piotr Mazgaj & Mateusz Włostowski, 2021. "Uncertainty and Sensitivity Analysis of the In-Vessel Hydrogen Generation for Gen-III PWR and Phebus FPT-1 with MELCOR 2.2," Energies, MDPI, vol. 14(16), pages 1-28, August.
    2. Cho, Jaehyun & Lee, Sang Hun & Bang, Young Suk & Lee, Suwon & Park, Soo Yong, 2022. "Exhaustive simulation approach for severe accident risk in nuclear power plants: OPR-1000 full-power internal events," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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