과제정보
The authors would like to express their gratitude to the Programa Propio of the Universidad Politecnica de Madrid, without its founding, this research could not had been possible.
참고문헌
- USNRC, "10 CFR Part 50 - Domestic Licensing of Production and Utilization Facilities." .
- Safety Assessment and Verification for Nuclear Power Plants: Safety Guide, IAEA, Vienna, 2001.
- Regulatory Guide 1.157 (Task RS 701-4), Best-Estimate Calculations of Emergency Core Cooling System Performance.," p. 20.
- B. Boyack, et al., Quantifying Reactor Safety Margins: Application of Code Scaling, Applicability, and Uncertainty Evaluation Methodology to a LargeBreak, Loss-Of-Coolant-Accident, NRC NUREG-Series Publications/CR-5249, 1989 iv.
- F. D'Auria, C. Camargo, O. Mazzantini, The Best Estimate Plus Uncertainty (BEPU) approach in licensing of current nuclear reactors, Nucl. Eng. Des. 248 (2012) 317-328, https://doi.org/10.1016/j.nucengdes.2012.04.002.
- F. D'Auria, O. Mazzantini, The best-estimate plus uncertainty (BEPU) challenge in the licensing of current generation of reactors, in: Proceedings Of an International Conference On Opportunities And Challenges For Water Cooled Reactors In the 21st Century, Vienna, Austria, 27-30 October 2009, Vienna, 2011, p. 13 [Online]. Available: http://www-pub.iaea.org/MTCD/Publications/PDF/P1500_CD_Web/htm/pdf/topic4/4S08_F.%20D. Auria.
- N.R.C. US, Regulatory Guide 1.70, standard Format and content of safety analysis reports for nuclear power plants - LWR edition, Rev 3 (" Dec. 1978).
- N.R.C. US, Regulatory Guide 1.203, Trans. Acc. Anal. Methods (2005) 53.
- F. D'Auria, N. Debrecin, H. Glaeser, Strengthening nuclear reactor safety and analysis, Nucl. Eng. Des. 324 (Dec. 2017) 209-219, https://doi.org/10.1016/j.nucengdes.2017.09.008.
- F. D'Auria, N. Debrecin, H. Glaeser, The technological challenge for current generation nuclear reactors, NUCET 5 (3) (Sep. 2019) 183-199, https://doi.org/10.3897/nucet.5.38117.
- IAEA, Best Estimate Safety Analysis for Nuclear Power Plants: Uncertainty Evaluation, Internat Atomic Energy Agency, Vienna, 2008.
- L.L. Briggs, Nuclear Engineering Division, "Uncertainty Quantification Approaches for Advanced Reactor Analyses, Argonne National Lab. (ANL), Argonne, IL (United States), 2009, https://doi.org/10.2172/956921.
- C. Frepoli, J.P. Yurko, R.H. Szilard, C.L. Smith, R. Youngblood, H. Zhang, 10 CFR 50.46c rulemaking: a novel approach in restating the LOCA problem for PWRs, Nucl. Technol. 196 (2) (Nov. 2016) 187-197, https://doi.org/10.13182/NT16-66.
- X. Wu, T. Kozlowski, H. Meidani, K. Shirvan, Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory, Nucl. Eng. Des. 335 (Aug. 2018) 339-355, https://doi.org/10.1016/j.nucengdes.2018.06.004.
- S. Wilks, Determination of sample sizes for setting tolerance limits, Ann. Math. Stat. 12 (1) (1941) 91-96, https://doi.org/10.1214/aoms/1177731788.
- S. Wilks, Statistical prediction with special reference to the problem of tolerance limits, Ann. Math. Stat. 13 (4) (1945) 400-409, https://doi.org/10.1214/aoms/1177731537.
- Alan D. Hutson, Calculating nonparametric confidence intervals for quantiles using fractional order statistics, J. Appl. Stat. 26 (3) (1999) 343-353, https://doi.org/10.1080/02664769922458.
- R. Beran, P. Hall, Interpolated nonparametric prediction intervals and confidence intervals, J. Roy. Stat. Soc. B 55 (3) (1993) 643-652, https://doi.org/10.1111/j.2517-6161.1993.tb01929.x.
- E. Zio, F. Di Maio, Bootstrap and order statistics for quantifying thermalhydraulic code uncertainties in the estimation of safety margins, Sci. Technol. Nucl. Instal. 2008 (2008) 1-9, https://doi.org/10.1155/2008/340164.
- F. Sanchez-Saez, A. Sanchez, J. Villanueva, S. Carlos, S. Martorell, Uncertainty analysis of a large break loss of coolant accident in a pressurized water reactor using non-parametric methods 174, Reliability Engineering & System Safety, Jun. 2018, pp. 19-28, https://doi.org/10.1016/j.ress.2018.02.005.
- S. Ferson, V. Kreinovick, L. Ginzburg, F. Sentz, Constructing Probability Boxes and Dempster-Shafer Structures, SNL Report, 2003, p. 809606, https://doi.org/10.2172/809606. SAND2002-4015.
- I.S. Hong, A. Connolly, Generalized tolerance limit evaluation method to determine statistically meaningful minimum code simulations, in: Volume 4: Structural Integrity; Next Generation Systems; Safety And Security; Low Level Waste Management And Decommissioning; Near Term Deployment: Plant Designs, Licensing, Construction, Workforce and Public Acceptance, Orlando, Florida, USA, Jan, 2008, pp. 653-660, https://doi.org/10.1115/ICONE16-48448.
- J. Hou, et al., BENCHMARK FOR UNCERTAINTY ANALYSIS IN MODELLING (UAM) FOR DESIGN, OPERATION AND SAFETY ANALYSIS OF LWRs, in: Specification and Support Data for the Core Cases (Phase II), 3 Vols, vol. 2, OECD Nuclear Energy Agency, 2019.
- G.J. Hahn, W.Q. Meeker, L.A. Escobar, Statistical Intervals: A Guide for Practitioners and Researchers, John Wiley & Sons, 2017.
- R. Mendizabal, Contribucion al estudio de las metodologias de calculo realista con incertidumbre (BEPU), dentro del analisis determinista de seguridad de plantas nucleares, " Universidad Politecnica de Madrid, Madrid, 2016.
- US NRC, "Regulatory Guide 1.105, Set-points for safety related instrumentation. Rev. 3".
- S. Bi, M. Broggi, P. Wei, M. Beer, The Bhattacharyya distance: enriching the Pbox in stochastic sensitivity analysis, Mech. Syst. Signal Process. 129 (Aug. 2019) 265-281, https://doi.org/10.1016/j.ymssp.2019.04.035.
- L.L. Sharon, in: Sampling: Design and Analysis, second ed., Brooks/Cole, 2009.
- D. Grabaskas, R. Denning, T. Aldemir, M. Nakayama, The use of Latin Hypercube sampling for the efficient estimation of confidence intervals, in: International Congress on Advances In Nuclear Power Plants 2012, ICAPP 2012 vol. 2, 2012.
- C. Baudrit, D. Dubois, Practical representations of incomplete probabilistic knowledge, Comput. Stat. Data Anal. 51 (1) (Nov. 2006) 86-108, https://doi.org/10.1016/j.csda.2006.02.009.
- V. Voinov, N. Balakrishnan, M.S. Nikulin, Chi-squared Goodness of Fit Tests with Applications, Elsevier/AP, Amsterdam, 2013.
- AndersoneDarling Test, in the Concise Encyclopedia Of Statistics, Springer New York, New York, NY, 2008, pp. 12-14.
- Kolmogorov-Smirnov Test, in the Concise Encyclopedia Of Statistics, Springer New York, New York, NY, 2008, pp. 283-287.
- R.F. Engle, Chapter 13 Wald, likelihood ratio, and Lagrange multiplier tests in econometrics, in: In Handbook Of Econometrics, vol. 2, 1984, pp. 775-826.
- Y. Pawitan, A reminder of the fallibility of the Wald statistic: likelihood explanation, Am. Statistician 54 (1) (2000) 54-56, https://doi.org/10.1080/00031305.2000.10474509.
- S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses, Ann. Math. Stat. 9 (1) (1938) 60-62, https://doi.org/10.1214/aoms/1177732360.
- C.E. Bonferroni, Teoria statistica delle classi e calcolo delle probabilita, 1936.
- B. Efron, R. Tibshirani, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy, Stat. Sci. 1 (1) (Feb. 1986) 54-75, https://doi.org/10.1214/ss/1177013815.
- T.G. Bali, The generalized extreme value distribution, Econ. Lett. 79 (3) (Jun. 2003) 423-427, https://doi.org/10.1016/S0165-1765(03)00035-1.
- R. Schobi, B. Sudret, Propagation of uncertainties modelled by parametric P-boxes using sparse polynomial Chaos expansions, in: In 12th Int. Conf. On Applications Of Statistics And Probability In Civil Engineering (ICASP12), Canada, Vancouver, 2015, p. 9 [Online]. Available: https://hal.archives-ouvertes.fr/hal01247151.
- C.F. Wu, M. Hamada, Experiments: Planning, Analysis, and Parameter Design Optimization, Wiley, New York, 2000.
- M.H. Kutner (Ed.), Applied Linear Statistical Models, fifth ed., McGraw-Hill Irwin, Boston, 2005.
- E. Zugazagoitia, C. Queral, K. Fernandez-Cosials, J. Gomez, L. Duran-Vinuesa, J. Sanchez-Torrijos, J.M. Posada, Uncertainty and sensitivity analysis of a PWR LOCA sequence using parametric and non-parametric methods, Reliab. Eng. Syst. Saf. 193 (2020), https://doi.org/10.1016/j.ress.2019.106607.