Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Recent research towards integrated deterministic-probabilistic safety assessment in Korea

  • Received : 2020.10.27
  • Accepted : 2021.05.11
  • Published : 2021.11.25
Download PDF
⟨ Previous Next ⟩

Abstract

For a long time, research into integrated deterministic-probabilistic safety assessment has been continuously conducted to point out and overcome the limitations of classical ET (event tree)/FT (fault tree) based PSA (probabilistic safety assessment). The current paper also attempts to assert the reason why a technical transformation from classical PSA is necessary with a re-interpretation of the categories of risk. In this study, residual risk was classified into interpolating- and extrapolating-censored categories, which represent risks that are difficult to identify through an interpolation or extrapolation of representative scenarios due to potential nonlinearity between hardware and human behaviors intertwined in time and space. The authors hypothesize that such risk can be dealt with only if the classical ETs/FTs are freely relocated, entailing large-scale computation associated with physical models. The functional elements that are favorable to find residual risk were inferred from previous studies. The authors then introduce their under-development enabling techniques, namely DICE (Dynamic Integrated Consequence Evaluation) and DeBATE (Deep learning-Based Accident Trend Estimation). This work can be considered as a preliminary initiative to find the bridging points between deterministic and probabilistic assessments on the pillars of big data technology.

Keywords

Acknowledgement

This work was supported by a Nuclear Research & Development Program grant from the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (2019M2C9A1055906), and by a Nuclear Safety Research Program grant from the Korea Foundation of Nuclear Safety (KOFONS), funded by the Nuclear Safety and Security Commission of the Republic of Korea (No. 1803008).

References

  1. R.S. Denning, R.J. Budnitz, Impact of probabilistic risk assessment and severe accident research in reducing reactor risk, Prog. Nucl. Energy 102 (2018) 90-102. https://doi.org/10.1016/j.pnucene.2017.05.021
  2. P. Kafka, Probabilistic risk assessment for nuclear power plants, in: Krishna B. Misra (Ed.), Handbook of Performability Engineering, Springer-Verlag, London, 2008, pp. 1179-1192.
  3. A. Mosleh, PRA: a perspective on strengths, current limitations and possible improvements, Nucl. Eng. Technol. 46 (2014) 1-10. https://doi.org/10.5516/NET.03.2014.700
  4. F. Niehaus, Use of probabilistic safety assessment (PSA) for nuclear installations, Saf. Sci. 40 (2002) 153-176. https://doi.org/10.1016/S0925-7535(01)00047-9
  5. J.E. Yang, Fukushima Dai-Ichi accident: lessons learned and future actions from the risk perspectives, Nucl. Eng. Technol. 46 (2014) 27-38. https://doi.org/10.5516/NET.03.2014.702
  6. N. Siu, D. Marksberry, S. Cooper, K. Coyne, M. Stutzke, PSA technology challenges revealed by the Great East Japan Earthquake, in: PSAM Topical Conference in Light of the Fukushima Dai-Ichi Accident, 2013. Tokyo, Japan, April 15-17.
  7. J.E. Yang, Multi-unit risk assessment of nuclear power plants: current status and issues, Nucl. Eng. Technol. 50 (2018) 1199-1209. https://doi.org/10.1016/j.net.2018.09.010
  8. T. Aldemir, A survey of dynamic methodologies for probabilistic safety assessment of nuclear power plants, Ann. Nucl. Energy 52 (2013) 113-124. https://doi.org/10.1016/j.anucene.2012.08.001
  9. E. Zio, The future of risk assessment, Reliab. Eng. Syst. Saf. 177 (2018) 176-190. https://doi.org/10.1016/j.ress.2018.04.020
  10. The Republic of Korea, Seventh National Report for the Convention on Nuclear Safety, 2016.
  11. C.K. Seong, G. Heo, S.J. Baek, J.W. Yoon, M.C. Kim, Analysis of the technical status of multiunit risk assessment in nuclear power plants, Nucl. Eng. Technol. 50 (2018) 319-326. https://doi.org/10.1016/j.net.2017.12.015
  12. International Atomic Energy Agency, Technical Approach to Probabilistic Safety Assessment for Multiple Reactor Units, IAEA, Vienna, 2019. Safety Reports Series No. 96.
  13. International Atomic Energy Agency, Consideration of External Hazards in Probabilistic Safety Assessment for Single Unit and Multi-Unit Nuclear Power Plants, IAEA, Vienna, 2018. Safety Reports Series No. 92.
  14. E. Zio, Integrated deterministic and probabilistic safety assessment: concepts, challenges, research directions, Nucl. Eng. Des. 280 (2014) 413-419. https://doi.org/10.1016/j.nucengdes.2014.09.004
  15. International Atomic Energy Agency, Considerations on Performing Integrated Risk Informed Decision Making, IAEA, Vienna, 2020. IAEA-TECDOC-1909.
  16. L. Ibanez, J. Hortal, C. Queral, J. Gomez-Magan, M. Sanchez-Perea, I. Fernandez, E. Melendez, A. Exposito, J.M. Izquierdo, J. Gil, H. Marrao, E. Villalba-Jabonero, Application of the integrated safety assessment methodology to safety margins. Dynamic event trees, damage domains and risk assessment, Reliab. Eng. Syst. Saf. 147 (2016) 170-193. https://doi.org/10.1016/j.ress.2015.05.016
  17. A. Hakobyan, T. Aldemir, R. Denning, S. Dunagan, D. Kunsman, B. Rutt, U. Catalyurek, Dynamic generation of accident progression event trees, Nucl. Eng. Des. 238 (2008) 3457-3467. https://doi.org/10.1016/j.nucengdes.2008.08.005
  18. E. Hofer, M. Kloos, B. Krzykacz-Hausmann, J. Peschke, M. Woltereck, An approximate epistemic uncertainty analysis approach in the presence of epistemic and aleatory uncertainties, Reliab. Eng. Syst. Saf. 77 (2002) 229-238. https://doi.org/10.1016/S0951-8320(02)00056-X
  19. J. Park, The Complexity of Proceduralized Tasks, Springer-Verlag, 2019.
  20. N.A. Stanton, P. Salmon, D. Jenkins, G. Walker, Human Factors in the Design and Evaluation of Control Room Operations, CRC Press, 2010.
  21. B. Hallbert, T. Morgan, J. Hugo, J. Oxstrand, J.J. Persensky, A Formalized Approach for the Collection of HRA Data from Nuclear Power Plant Simulators, NUREG/CR-7163, Washington DC, 2014.
  22. C.E.L. Jones, D.L. Phipps, D.M. Ashcroft, Understanding procedural violations using Safety-I and Safety-II: the case of community pharmacies, Saf. Sci. 105 (2018) 114-120. https://doi.org/10.1016/j.ssci.2018.02.002
  23. N. Siu, Technical Opinion Paper1 Dynamic PRA for Nuclear Power Plants: Not if but when?. https://www.nrc.gov/docs/ML1906/ML19066A390.
  24. J. Gil, I. Fernandez, S. Murcia, J. Gomez, H. Marrao, C. Queral, A. Exposito, G. Rodriguez, L. Ibanez, J. Hortal, J.M. Izquierdo, M. S ~ anchez, E. Melendez, A code for simulation of human failure events in nuclear power plants: SIMPROC, Nucl. Eng. Des. 241 (2011) 1097-1107. https://doi.org/10.1016/j.nucengdes.2010.03.040
  25. M. Cepin, Advantages and difficulties with the application of methods of probabilistic safety assessment to the power systems reliability, Nucl. Eng. Des. 246 (2012) 136-140. https://doi.org/10.1016/j.nucengdes.2011.08.082
  26. H.S. Lee, T.W. Kim, G. Heo, Application of dynamic probabilistic safety assessment approach for accident sequence precursor analysis: case study for steam generator tube rupture, Nucl. Eng. Techol. 49 (2017) 306-312. https://doi.org/10.1016/j.net.2016.12.012
  27. B.K. Kim, H.C. No, Application of Wilks' formula and concept of state change time to integrated deterministic and probabilistic safety assessment for evaluation of the safety margin of DEC accidents, Nucl. Eng. Des. 352 (2019) 110195. https://doi.org/10.1016/j.nucengdes.2019.110195
  28. J. Nielsen, A. Tokuhiro, R. Hiromoto, L. Tu, Branch-and-Bound algorithm applied to uncertainty quantification of a boiling water reactor station blackout, Nucl. Eng. Des. 295 (2015) 283-304. https://doi.org/10.1016/j.nucengdes.2015.07.029
  29. H. Muhammad, Y. Hidekazu, M. Takeshi, Y. Ming, Common cause failure analysis of PWR containment spray system by GO-FLOW methodology, Nucl. Eng. Des. 262 (2013) 350-357. https://doi.org/10.1016/j.nucengdes.2013.04.028
  30. J. Nielsen, A. Tokuhiro, R. Hiromoto, J. Khatry, Optimization method to branch-and-bound large SBO state spaces under dynamic probabilistic risk assessment via use of LENDIT scales and S2R2 sets, J. Nucl. Sci. Technol. 51 (2014) 1212-1230. https://doi.org/10.1080/00223131.2014.917995
  31. C. Queral, L. Mena-Rosell, G. Jimenez, M. Sanchez-Perea, J. Gomez-Magan, J. Hortal, Verification of SAMGs in SBO sequences with Seal LOCA. Multiple damage domains, Ann. Nucl. Energy 98 (2016) 90-111. https://doi.org/10.1016/j.anucene.2016.07.021
  32. K.M. Yoon, H.G. Kang, A case study of time-dependent risk informed integrated safety assessment under complex accident sequences, Nucl. Eng. Des. 333 (2018) 63-75. https://doi.org/10.1016/j.nucengdes.2018.03.044
  33. D.R. Karanki, T.W. Kim, V.N. Dang, A dynamic event tree informed approach to probabilistic accident sequence modeling: dynamics and variabilities in medium LOCA, Reliab. Eng. Syst. Saf. 142 (2015) 78-91. https://doi.org/10.1016/j.ress.2015.04.011
  34. C. Picoco, V. Rychkov, T. Aldemir, A framework for verifying dynamic probabilistic risk assessment models, Reliab. Eng. Syst. Saf. 203 (2020) 107099. https://doi.org/10.1016/j.ress.2020.107099
  35. D. Maljovec, S. Liu, B. Wang, D. Mandelli, P.-T. Bremer, V. Pascucci, C. Smith, Analyzing simulation-based PRA data through traditional and topological clustering: a BWR station blackout case study, Reliab. Eng. Syst. Saf. 145 (2016) 262-276. https://doi.org/10.1016/j.ress.2015.07.001
  36. C. Queral, J. Gomez-Magan, C. Paris, J. Rivas-Lewicky, M. Sanchez-Perea, J. Gil, J. Mula, E. Melendez, J. Hortal, J.M. Izquierdo, I. Fernandez, Dynamic event trees without success criteria for full spectrum LOCA sequences applying the integrated safety assessment (ISA) methodology, Reliab. Eng. Syst. Saf. 171 (2018) 152-168. https://doi.org/10.1016/j.ress.2017.11.004
  37. D.R. Karanki, V.N. Dang, Quantification of dynamic event trees - a comparison with event trees for MLOCA scenario, Reliab. Eng. Syst. Saf. 147 (2016) 19-31. https://doi.org/10.1016/j.ress.2015.10.017
  38. D. Kancev, A plant-specific HRA sensitivity analysis considering dynamic operator actions and accident management actions, Nucl. Eng. Techol. 52 (2020) 1983-1989. https://doi.org/10.1016/j.net.2020.02.021
  39. K. Vierow, K. Hogan, K. Metzroth, T. Aldemir, Application of dynamic probabilistic risk assessment techniques for uncertainty quantification in generation IV reactors, Prog. Nucl. Energy 77 (2014) 320-328. https://doi.org/10.1016/j.pnucene.2014.04.012
  40. X.L. Pan, J.Q. Wang, R. Yuan, F. Wang, R. Yuan, H. Lin, L. Hu, J. Wang, Biasing transition rate method based on direct MC simulation for probabilistic safety assessment, Nucl. Sci. Tech. 28 (2017) 91. https://doi.org/10.1007/s41365-017-0255-2
  41. Y. Zhao, J. Tong, L. Zhang, G. Wu, Diagnosis of operational failures and ondemand failures in nuclear power plants: an approach based on dynamic Bayesian networks, Ann. Nucl. Energy 138 (2020) 107181. https://doi.org/10.1016/j.anucene.2019.107181
  42. Z.K. Jankovsky, M.R. Denman, T. Aldemir, Dynamic event tree analysis with the SAS4A/SASSYS-1 safety analysis code, Ann. Nucl. Energy 115 (2018) 55-72. https://doi.org/10.1016/j.anucene.2018.01.001
  43. F. Aubert, B. Baude, P. Gauthe, M. Marques, N. Perot, F. Bertrand, C. Vaglio-Gaudard, V. Rychkov, M. Balmain, Implementation of probabilistic assessments to support the ASTRID decay heat removal systems design process, Nucl. Eng. Des. 340 (2018) 405-413. https://doi.org/10.1016/j.nucengdes.2018.09.002
  44. D. Mandelli, C. Parisi, A. Alfonsi, D. Maljovec, R. Boring, S. Ewing, S. St Germain, C. Smith, C. Rabiti, M. Rasmussen, Multi-unit dynamic PRA, Reliab. Eng. Syst. Saf. 185 (2019) 303-317. https://doi.org/10.1016/j.ress.2018.12.029
  45. M. Kloos, J. Peschke, Improved modelling and assessment of the performance of firefighting means in the frame of a fire PSA, Science and Technology of Nuclear Installations (2015), https://doi.org/10.1155/2015/238723.
  46. H. Sezen, J. Hur, C. Smith, T. Aldemir, R. Denning, A computational risk assessment approach to the integration of seismic and flooding hazards with internal hazard, Nucl. Eng. Des. 355 (2019) 110341. https://doi.org/10.1016/j.nucengdes.2019.110341
  47. Y. Li, A. Mosleh, Modeling and simulation of crew to crew response variability due to problem-solving styles, Reliab. Eng. Syst. Saf. 194 (2020) 105840. https://doi.org/10.1016/j.ress.2017.05.020
  48. D. Zamalieva, A. Yilmaz, T. Aldemir, A probabilistic model for online scenario labeling in dynamic event tree generation, Reliab. Eng. Syst. Saf. 120 (2013) 18-26. https://doi.org/10.1016/j.ress.2013.02.028
  49. T. Sakurahara, Z. Mohaghegh, S. Reihani, E. Kee, M. Brandyberry, S. Rodgers, An integrated methodology for spatio-temporal incorporation of underlying failure mechanisms into fire probabilistic risk assessment of nuclear power plants, Reliab. Eng. Syst. Saf. 169 (2018) 242-257. https://doi.org/10.1016/j.ress.2017.09.001
  50. U. Catalyurek, B. Rutt, K. Metzroth, A. Hakobyan, T. Aldemir, R. Denning, S. Dunagan, D. Kunsman, Development of a code-agnostic computational infrastructure for the dynamic generation of accident progression event trees, Reliab. Eng. Syst. Saf. 95 (2010) 278-294. https://doi.org/10.1016/j.ress.2009.10.008
  51. S. Johst, M. Hage, J. Peschke, Extension of a Level 2 PSA event tree based on results of a probabilistic dynamic safety analysis of induced Steam Generator Tube Rupture, Nucl. Technol. 207 (2021) 352-362. https://doi.org/10.1080/00295450.2020.1766347
  52. V. Rychkov, K. Kawahara, ADAPT-MAAP4 coupling for a dynamic event tree study, in: International Topical Meeting on Probabilistic Safety Assessment and Analysis, Sun Valley, Idaho, USA, 2015. April 26-30.
  53. K. Rychkov, K. Kawahara, PSA Level 2 with dynamic event trees. Lessons learned and perspectives, in: International Topical Meeting on Probabilistic Safety Assessment and Analysis, Sun Valley, USA, 2015. April 26-30.
  54. X. Zheng, H. Tamaki, J. Ishikawa, T. Sugiyama, Y. Maruyama, Severe accident scenario uncertainty analysis using the dynamic event tree method, in: Probabilistic Safety Assessment & Management (PSAM14), 2018. Los Angeles, CA, USA, September 16-21.
  55. S. Jang, A. Yamaguchi, Dynamic scenario quantification for level 2 PRA of sodium-cooled fast reactor based on continuous Markov chain and Monte Carlo method coupled with meta-model of thermal-hydraulic analysis, J. Nucl. Sci. Technol. 55 (2018) 850-858. https://doi.org/10.1080/00223131.2018.1445564
  56. D.M. Osborn, T. Aldemir, R. Denning, D. Mandelli, Seamless Level 2/Level 3 dynamic probabilistic risk assessment clustering, in: International Topical Meeting on Probabilistic Safety Assessment and Analysis, 2013. Columbia, South Carolina, USA, September 22-27.
  57. J.H. Lee, A. Yilmaz, R. Denning, T. Aldemir, An online operator support tool for severe accident management in nuclear power plants using dynamic event trees and deep learning, Ann. Nucl. Energy 146 (2020) 107626. https://doi.org/10.1016/j.anucene.2020.107626
  58. M.J. Rebollo, C. Queral, G. Jimenez, J. Gomez-Magan, E. Melendez, M. Sanchez-Perea, Evaluation of the offsite dose contribution to the global risk in a Steam Generator Tube Rupture scenario, Reliab. Eng. Syst. Saf. 147 (2016) 32-48. https://doi.org/10.1016/j.ress.2015.10.016
  59. J.H. Lee, A. Yilmaz, R. Denning, T. Aldemir, Use of dynamic event trees and deep learning for real-time emergency planning in power plant operation, Nucl. Technol. 205 (2019) 1035-1042. https://doi.org/10.1080/00295450.2018.1541394
  60. S.W. Lee, S.J. Baek, G.Y. Heo, T.W. Kim, J.H. Kim, Development of DICE(Dynamic Integrated Consequence Evaluation) for procedure coverability studies: conceptual design phase, in: Korean Nuclear Society Autumn Meeting, 2018. Yeosu, Korea, October 25-26.
  61. S.J. Baek, G. Heo, T.W. Kim, J.H. Kim, Introduction to DICE (Dynamic Integrated Consequence Evaluation) toolbox for checking coverability of operational procedures in NPPs, in: The Annual European Safety and Reliability Conference ESREL, 2019. Hannover, Germany, September 22-26.
  62. S.J. Baek, T.W. Kim, G. Heo, J.H. Kim, Branching rules and quantification in dynamic probabilistic safety assessment: development of DICE (Dynamic Integrated Consequence Evaluation), in: Korean Nuclear Society Autumn Meeting, 2019. Ilsan, Korea, October 24-25.
  63. S.J. Baek, T.W. Kim, J.H. Kim, G. Heo, Application of DICE (Dynamic Integrated Consequence Evaluation) case study on branching rules examples, in: Transactions of the Korean Nuclear Society Virtual Spring Meeting, 2020. July 9-10.
  64. J.M. Izquierdo, J. Hortal, M. Sanchez Perea, E. Melendez, C. Queral, J. Rivas-Lewicky, Current status and applications of integrated safety assessment and simulation code system for ISA, Nucl. Eng. Technol. 49 (2017) 295-305. https://doi.org/10.1016/j.net.2017.01.013
  65. D.R. Karanki, S. Rahman, V.N. Dang, O. Zerkak, Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: tradeoff between accuracy and accident simulations, Reliab. Eng. Syst. Saf. 162 (2017) 91-102. https://doi.org/10.1016/j.ress.2017.01.015
  66. MARS CODE MANUAL VOLUME II: Input Requirements, KAERI/TR-2811/2004, Korea Atomic Energy Research Institute, 2010.
  67. G.M. Yoon, D.I. Lee, J.H. Kim, Feasibility study on application of artificial operator to estimation of HEPs, in: Asian Symposium on Risk Assessment and Management, 2019. Gyeongju, Korea, 30 September-2 October.
  68. D. Mandelli, A. Yilmaz, T. Aldemir, K. Metzroth, R. Denning, Scenario clustering and dynamic probabilistic risk assessment, Reliab. Eng. Syst. Saf. 115 (2013) 146-160. https://doi.org/10.1016/j.ress.2013.02.013
  69. L. Podofillini, D. Mercurio, V.N. Dang, E. Zio, An identification and grouping approach to analyze the output of a dynamic safety assessment, ProcediaSocial and Behavioral Sciences 2 (2010) 7724-7725. https://doi.org/10.1016/j.sbspro.2010.05.198
  70. R. Gomez-Bombarelli, J.N. Wei, D. Duvenaud, J.M. Hernandez-Lobato, B. Sanchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T.D. Hirzel, R.P. Adams, A. Aspuru-Guzik, Automatic chemical design using a data-driven continuous representation of molecules, ACS Cent. Sci. 4 (2018) 268-276. https://doi.org/10.1021/acscentsci.7b00572
  71. H. Kim, J. Cho, J. Park, Application of a deep learning technique to the development of a fast accident scenario identifier, IEEE Access 8 (2020) 177363-177373, 2020. https://doi.org/10.1109/access.2020.3026104