DOI QR코드

DOI QR Code

Application of data driven modeling and sensitivity analysis of constitutive equations for improving nuclear power plant safety analysis code

  • ChoHwan Oh (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Doh Hyeon Kim (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Jeong Ik Lee (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2022.05.08
  • Accepted : 2022.08.27
  • Published : 2023.01.25

Abstract

Constitutive equations in a nuclear reactor safety analysis code are mostly empirical correlations developed from experiments, which always accompany uncertainties. The accuracy of the code can be improved by modifying the constitutive equations fitting wider range of data with less uncertainty. Thus, the sensitivity of the code with respect to the constitutive equations is evaluated quantitatively in the paper to understand the room for improvement of the code. A new methodology is proposed which first starts by dividing the thermal hydraulic conditions into multiple sub-regimes using self-organizing map (SOM) clustering method. The sensitivity analysis is then conducted by multiplying an arbitrary set of coefficients to the constitutive equations for each sub-divided thermal-hydraulic regime with SOM to observe how the code accuracy varies. The randomly chosen multiplier coefficient represents the uncertainty of the constitutive equations. Furthermore, the set with the smallest error with the selected experimental data can be obtained and can provide insight which direction should the constitutive equations be modified to improve the code accuracy. The newly proposed method is applied to a steady-state experiment and a transient experiment to illustrate how the method can provide insight to the code developer.

Keywords

Acknowledgement

This work was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety(KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission(NSSC) of the Republic of Korea. (No. 1903002).

References

  1. KAERI, MARS CODE MANUAL Vol. 1 Code Structure, System Models, and Solution Methods, 2009.
  2. D. Bestion, System Code Models and Capabilities, THICKET, Session III, 2008. Paper 06.
  3. E.W. Coryell, et al., Summary of Important Results and SCDAP/RELAP5 Analyses for OECD LOFT Experiment LP-FP2, NUREG/CR-6160, NEA-CSNI-R, 1994 (94)3, EGG-2721.
  4. Y.S. Kim, et al., Second ATLAS domestic standard problem (DSP-02) for a code assessment, Nuclear Engineering and Technology 45 (7) (2013) 871-894. https://doi.org/10.5516/NET.02.2013.009
  5. K.H. Kang, et al., Code assessment of ATLAS integral effect test simulating main steam-line break accident of an advanced pressurized water reactor, Journal of Nuclear Science and Technology 55 (Issue 1) (2018) 104-112. https://doi.org/10.1080/00223131.2017.1383212
  6. Y.S. Park, et al., Open calculation result of DSP-05 activity utilizing ATLAS test facility with multiple steam generator tube rupture under PAFS operation scenario, in: Transactions of the Korean Nuclear Society Virtual Spring Meeting, July 9-10, 2020.
  7. E. Stubbe, et al., International Standard Problem No. 20, Steam Generator Tube Rupture in the Nuclear Power Plant DOEL 2, Belgium, Final Report, 1988. CSNI-Report No. 154, December.
  8. K.Y. Choi, et al., A summary of 50th OECD/NEA/CSNI international standard problem exercise (ISP-50), Nuclear Engineering and Technology 44 (6) (2012) 561-586. https://doi.org/10.5516/NET.02.2012.708
  9. Y.S. Kim, et al., First ATLAS domestic standard problem (DSP-01) for the code assessment, Nuclear Engineering and Technology 43 (Issue 1) (2011) 25-44. https://doi.org/10.5516/NET.2011.43.1.025
  10. J. Malet, et al., OECD international standard problem ISP-47 on containment thermal-hydraulics-conclusions of the TOSQAN part, Nuclear Engineering and Design 240 (Issue 10) (2010) 3209-3220. https://doi.org/10.1016/j.nucengdes.2010.05.061
  11. D. Lubbesmeyer, et al., ISP-42 Description of the PANDA-Facility, TM-42-98-41 ALPHA-836-0, Internal Report, 1998. November.
  12. Kimber George, et al., International Standard Problem No. 38, BETHSY Test 6,9c: Loss of Residual Heat Removal System during Mid-loop Operation, Final Comparison Report, 1998. NEA/CSNI/R(97)38, Vols. I and II, June.
  13. H. Purhonen, et al., ISP-33, OECD/NEA-CSNI International Standard Problem No. 33, PACTEL Natural Circulation Stepwise Coolant Inventory Reduction Experiment, Comparison Report, I, NEA/CSNI/R, December, 1994 (94)24, Parts 1 and 2.
  14. CSNI: International, Standard Problems (ISP), Brief Descriptions (1975-1999), NEA/CSNI/R, 2000 (2000)5, February.
  15. S.N. Aksan, et al., User Effects on the Transient System Code Calculations, vol. 35, NEA/CSNI/R, 1994, 94.
  16. J.J. Lee, et al., Node configuration uncertainty in nuclear safety analyses, Nuclear Engineering and Design 355 (Issue 15) (2019), 110286.
  17. K.Y. Choi, et al., Comparison Report of Open Calculations for ATLAS Domestic Standard Problem (DSP-01), KAERI/TR-4073/2010, Korea Atomic Energy Research Institute, 2010.
  18. J. Heo, K.D. Kim, PAPIRUS, a parallel computing framework for sensitivity analysis, uncertainty propagation, and estimation of parameter distribution, Nuclear Engineering and Design 292 (2015) 237-247. https://doi.org/10.1016/j.nucengdes.2015.07.002
  19. C.H. Ban, et al., Development and application of KEPRI realistic evaluation methodology (KREM) for LB-LOCA, in: Best Estimate-2004: Proceedings of International Meeting on Updates in Best Estimate Methods in Nuclear Installations Safety Analysis, 2004. Washington, D.C., November 14-18.
  20. B.J. Yun, et al., Characteristics of the local bubble parameters of a subcooled boiling flow in an annulus, Nuclear Engineering and Design 240 (Issue 9) (2010) 2295-2303. https://doi.org/10.1016/j.nucengdes.2009.11.014
  21. S.N. Kim, G. Peter, PWR pressurizer modeling, Nuclear Engineering and Design 102 (Issue 2) (1987) 199-209. https://doi.org/10.1016/0029-5493(87)90253-6
  22. T. Kohonen, The self-organizing map, Neurocomputing 21 (Issue 1) (1998) 1-6. https://doi.org/10.1016/S0925-2312(98)00030-7
  23. J. Vesanto, E. Alhoniemi, Clustering of the self-organizing map, IEEE Transaction on Neural Networks 11 (3) (2000) 586-600. https://doi.org/10.1109/72.846731
  24. J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, 1967, pp. 281-297.
  25. B. Desgraupes, Clustering Indices, University of Paris Ouest-Lab Modal'X, 2013.
  26. P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster Analysis, Journal of Computational and Applied Mathematics 20 (1987) 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
  27. R. Tibshirani, et al., Estimating the number of clusters in a data set via the gap statistic, Journal of Royal Statistical Society: Series B (Statistical Methodology) 63 (Issue 2) (2001) 411-423. https://doi.org/10.1111/1467-9868.00293
  28. M.D. McKay, et al., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21 (Issue 2) (1979) 239-245. https://doi.org/10.1080/00401706.1979.10489755
  29. D.H. Kim, J.I. Lee, Optimization of liquid interfacial heat transfer coefficient of MARS-KS code to match SUBO data, in: Transactions of the Korean Nuclear Society Virtual Spring Meeting, 2020. July 9-10.
  30. W. Ambrosini, et al., Evaluation of accuracy of thermal hydraulic code calculation, Energia Nucleare 7 (Issue 2) (1990) 5-16.
  31. E. Keogh, C.A. Ratanamahatana, Exact indexing of dynamic time warping, Knowledge and Information Systems 7 (Issue 3) (2005) 358-386. https://doi.org/10.1007/s10115-004-0154-9
  32. Eamonn Keogh, et al., Segmenting time series: a survey and novel approach, Data Mining in Time Series Databases 57 (2004) 1-21. https://doi.org/10.1142/9789812565402_0001
  33. Jessica Lin, et al., Experiencing SAX: a novel symbolic representation of time series, Data Mining and Knowledge Discovery 15 (2007) 107-114. https://doi.org/10.1007/s10618-007-0064-z