Nuclear Engineering and Technology

  • 제56권11호
  • /
  • Pages.4868-4879
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  • 2024
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Quantitative evaluation of uncertain parameters for thermal-hydraulic experiments based on the COSINE code

  • Yixuan Cheng (College of Smart Energy, Shanghai Jiao Tong University) ;
  • Hao Zhang (School of Nuclear Science and Engineering, Shanghai Jiao Tong University) ;
  • Meng Zhao (School of Nuclear Science and Engineering, Shanghai Jiao Tong University) ;
  • Lin Chen (School of Nuclear Science and Engineering, Shanghai Jiao Tong University) ;
  • Fanfan Zhou (College of Smart Energy, Shanghai Jiao Tong University) ;
  • Yanhua Yang (College of Smart Energy, Shanghai Jiao Tong University)
  • 투고 : 2024.01.23
  • 심사 : 2024.06.30
  • 발행 : 2024.11.25
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초록

Identifying important phenomena and parameters of LBLOCAs is an important step in nuclear power safety evaluation. Traditional identification processes are based on experience and incorporate important phenomena and parameters without quantifying specific aspects of the models. To accurately identify physical effects, this paper presents the use of multiphase field subchannel code for simulation analysis as applied to numerical examples and specific reactor thermal-hydraulic problems. The results indicate that the code can simulate the relevant phenomena, and the calculation band of the refill stage is narrow, while the calculation band of the reflood stage is wide. In addition, the key impact models are captured. The radiation model most significantly impacts the cladding temperature in the core, and the heat transfer model of transition boiling to the gas phase is most strongly influenced by the quenching time. The interfacial heat transfer model of the large bubble regime for the liquid plays a major role in influencing the water inventory of the lower plenum in the downcomer.

키워드

과제정보

This research was funded by the Natural Science Foundation of China (No. U21B2059). This support is gratefully acknowledged.

참고문헌

  1. J. Sugimoto, Y. Murao, Experimental study of effect of initial clad temperature on reflood phenomena during PWR-LOCA, J. Nucl. Sci. Technol. 20 (8) (1983) 645-657. https://doi.org/10.1080/18811248.1983.9733446
  2. R.P. Martin, A. Petruzzi, Progress in international best estimate plus uncertainty analysis methodologies, Nucl. Eng. Des. 374 (2021) 111033.
  3. H. Luo, Quantified PIRT and Uncertainty Quantification for Computer Code Validation, 2012.
  4. W. Jaeger, V.H.S. Espinoza, Uncertainty and sensitivity study in the frame of TRACE validation for reflood experiment, Nucl. Technol. 184 (3) (2017) 333-350.
  5. Q. Xiong, et al., Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs, Reliab. Eng. Syst. Saf. 221 (2022) 108337.
  6. J.M. Yoo, et al., Analysis of the effect of liquid droplet models on the reflood heat transfer using the CUPID code, Nucl. Eng. Des. 354 (2019) 110148.
  7. Q. Xiong, et al., Optimization of sensitivity analysis in best estimate plus uncertainty and the application to large break LOCA of a three-loop pressurized water reactor, Prog. Nucl. Energy 126 (2020) 103396.
  8. G. Perret, et al., Global sensitivity analysis and bayesian calibration on a series of reflood experiments with varying boundary conditions, Nucl. Technol. 208 (4) (2021) 711-722. https://doi.org/10.1080/00295450.2021.1936879
  9. X. Wu, et al., A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes, Nucl. Eng. Des. 384 (2021) 111460.
  10. J. Yang, et al., Best estimate plus uncertainty analysis of a large break LOCA on generation III reactor with RELAP5, Ann. Nucl. Energy. 127 (2019) 326-340. https://doi.org/10.1016/j.anucene.2018.12.019
  11. M. Ionescu-Bujor, D.G. Cacuci, A comparative review of sensitivity and uncertainty analysis of large-scale systems-I: deterministic methods, Nucl. Sci. Eng. 147 (2004) 189-203. https://doi.org/10.13182/NSE03-105CR
  12. D.G. Cacuci, M. Ionescu-Bujor, A comparative review of sensitivity and uncertainty analysis of large-scale systems-II: statistical methods, Nucl. Sci. Eng. 147 (2004) 204-217. https://doi.org/10.13182/04-54CR
  13. D. Li, et al., Comparison of local and global sensitivity analysis methods and application to thermal hydraulic phenomena, Prog. Nucl. Energy 158 (2023) 104612.
  14. Q. Xiong, et al., Global sensitivity analysis of LOFT large break loss of coolant accident with optimized moment-independent method, Ann. Nucl. Energy 139 (2020) 107289.
  15. C.S. Brown, H. Zhang, Uncertainty quantification and sensitivity analysis with CASL Core Simulator VERA-CS, Ann. Nucl. Energy 95 (2016) 188-201. https://doi.org/10.1016/j.anucene.2016.05.016
  16. V. Nicoulaud-Gouin, et al., Sensitivity analysis in a radiological impact assessment of a nuclear power plant discharge. A comparison of the Morris, Spearman and Sobol' approaches, J. Environ. Radioact. 242 (2022) 106770.
  17. R.A. Shaw, et al., Phenomena Identification and Ranking Tables (PIRT) for LBLOCA, 1987.
  18. L. Chen, et al., Development of multiphase subchannel code with new numerical method in COSINE code package, Ann. Nucl. Energy 191 (2023) 109902.
  19. W.R. Zwick, A Comparison of Five Rules for Determining the Number of Components in Complex Patterns, 1983.
  20. W.K. Hardle, L. Simar, Applied Multivariate Statistical Analysis, 2003.
  21. E.C. Fieller, et al., Tests for rank correlation coefficients. I, Biometrika 44 (1957) 470-481. https://doi.org/10.1093/biomet/44.3-4.470
  22. C. Spearman, The proof and measurement of association between two things. By C. Spearman, 1904, Am. J. Psychol. 100 (3-4) (1987) 441-471. https://doi.org/10.2307/1422689
  23. T.W. Anderson, D. Darling, A test of goodness of fit, J. Am. Stat. Assoc. 49 (1954) 765-769. https://doi.org/10.1080/01621459.1954.10501232
  24. T.W. Anderson, D. Darling, Asymptotic theory of certain "goodness of fit" criteria based on stochastic processes, Ann. Math. Stat. 23 (1952) 193-212. https://doi.org/10.1214/aoms/1177729437
  25. P. Weiss, et al., UPTF experiment: a synopsis of full scale test results, Nucl. Eng. Des. 122 (1990) 219-234. https://doi.org/10.1016/0029-5493(90)90208-F
  26. P.S. Damerell, J.W. Simons, Reactor Safety Issues Resolved by the 2D/3D Program Nuclear Regulatory Commission, 1993.
  27. X. Li, et al., Analysis of RELAP5 prediction of countercurrent flow limitation in downcomer at upper plenum test facility, Prog. Nucl. Energy 158 (2023) 104608.
  28. P. Ihle, K. Rust, Feba - flooding experiments with blocked arrays, Evaluation report, 1984. Test series V-VIII (1984) KFK-3659.
  29. D. Wicaksono, et al., Global sensitivity analysis of transient code output applied to a reflood experiment model using the TRACE code, Nucl. Sci. Eng. 184 (3) (2017) 400-429. https://doi.org/10.13182/NSE16-37
  30. A. Kovtonyuk, et al., PREMIUM BenchmarkPhase II: Identification of Influential Input Parameters, 2012. Pisa, Italy.
  31. Z.J.A. Belmonte, et al., The acceptance of nuclear energy as an alternative source of energy among Generation Z in the Philippines: an extended theory of planned behavior approach, Nucl. Eng. Technol. 55 (8) (2023) 3054-3070. https://doi.org/10.1016/j.net.2023.04.047
  32. M.S. Islam, et al., An empirical study of the risk-benefit perceptions between the nuclear and non-nuclear groups towards the nuclear power plant in Bangladesh, Nucl. Eng. Technol. 55 (12) (2023) 4617-4627. https://doi.org/10.1016/j.net.2023.07.047
  33. S.-W. Kim, et al., PWSCC growth rate model of alloy 690 for head penetration nozzles of Korean PWRs, Nucl. Eng. Technol. 51 (4) (2019) 1060-1068. https://doi.org/10.1016/j.net.2019.01.010