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Application of Dynamic Probabilistic Safety Assessment Approach for Accident Sequence Precursor Analysis: Case Study for Steam Generator Tube Rupture 원문보기

Nuclear engineering and technology : an international journal of the Korean Nuclear Society, v.49 no.2, 2017년, pp.306 - 312  

Lee, Hansul (Kyung Hee University) ,  Kim, Taewan (Incheon National University) ,  Heo, Gyunyoung (Kyung Hee University)

Abstract AI-Helper 아이콘AI-Helper

The purpose of this research is to introduce the technical standard of accident sequence precursor (ASP) analysis, and to propose a case study using the dynamic-probabilistic safety assessment (D-PSA) approach. The D-PSA approach can aid in the determination of high-risk/low-frequency accident scena...

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제안 방법

  • In this paper, an SGTR accident in a Korean NPP was studied using the DET in the D-PSA to investigate the applicability of DPSA for accident sequence precursor (ASP) analysis. The risk quantification results from the D-PSA and the conventional PSA, the so-called static PSA (S-PSA) due to its relatively fixed nature, were compared.
  • The data set for the individual accident sequence was generated using the Monte-Carlo method. Sampling of the seven accident sequences was performed to identify prominent operator actions affecting core damage and to prevent the underestimation of core damage accident sequences and conditional core damage probability (CCDP). Additionally, 23 other sequences were sampled considering operator action failure on the basis of the results of the previous seven sequences.
  • The CCDP of the core damage accident sequences was calculated using the simulation results of the plant physical model. The technique for the CCDP calculation method is similar to the conventional PSA.
  • The risk quantification results from the D-PSA and the conventional PSA, the so-called static PSA (S-PSA) due to its relatively fixed nature, were compared. The authors recommended application plans and described the expected outcomes of D-PSA.
  • The model was slightly revised to account for the specific accident conditions as follows [14]: (1) deletion of reactor trip e event tree/fault tree modified; (2) deletion of depressurization of RCS for lowpressure safety injectiondevent tree modified; (3) deletion of low-pressure safety injection e event tree modified; and (4) addition of ‘MSIBV Failed to Open’ e fault tree modified.
  • The plant physical model was developed using the multi-dimensional analysis of reactor safety (MARS-KS) code developed by the Korea Atomic Energy Research Institute (KAERI) and the symbolic nuclear analysis package (SNAP) [8] code provided from the United States Nuclear Regulatory Commission (US NRC). The plant physical model was constructed for the low power and shut down (LPSD) condition to simulate the given SGTR accident. The nodalization for the plant physical model is based on [9], and the major initial conditions are summarized in Table 2.
  • The primary objective of the ASP program is to systematically evaluate operating experiences to identify, document, and rank those events in terms of the potential for inadequate core cooling and core damage. In addition, the program has the following secondary objectives: (1) to categorize the precursors for plant-specific and generic implications; (2) to provide a measure that can be used to trend nuclear plant core damage risk; and (3) to provide a partial check on PSApredicted dominant core damage scenarios [5].

이론/모형

  • An operator/crew state model was developed using the Module for SAmpling Input and QUantifying Estimator (MOSAIQUE) code developed by KAERI [10]. The operator crew state model was built as follows.
  • However, we selected seven potential accident sequences depending on the timing of operator actions, as shown in Table 3. The data set for the individual accident sequence was generated using the Monte-Carlo method. Sampling of the seven accident sequences was performed to identify prominent operator actions affecting core damage and to prevent the underestimation of core damage accident sequences and conditional core damage probability (CCDP).
  • The plant physical model was developed using the multi-dimensional analysis of reactor safety (MARS-KS) code developed by the Korea Atomic Energy Research Institute (KAERI) and the symbolic nuclear analysis package (SNAP) [8] code provided from the United States Nuclear Regulatory Commission (US NRC). The plant physical model was constructed for the low power and shut down (LPSD) condition to simulate the given SGTR accident.
  • The quantification results of ASP using S-PSA are cited from a previous study [14], which was the only reference available for comparison with the D-PSA results. This study used the PSA model for a full power OPR-1000. The model was slightly revised to account for the specific accident conditions as follows [14]: (1) deletion of reactor trip e event tree/fault tree modified; (2) deletion of depressurization of RCS for lowpressure safety injectiondevent tree modified; (3) deletion of low-pressure safety injection e event tree modified; and (4) addition of ‘MSIBV Failed to Open’ e fault tree modified.
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참고문헌 (14)

  1. A. Alfonsi, C. Rabiti, D. Mandelli, J.J. Cogliati, R.A. Kinoshita, A. Naviglio, Dynamic Event Tree Analysis through RAVEN, ANS PSA 2013 International Topical Meeting on Probabilistic Safety Assessment and Analysis, Columbia, SC, September 22-26, American Nuclear Society, LaGrange Park, IL, 2013. 

  2. M. Sonnekaib, J. Peschke, M. Kloos, B. Krzycacz-Hausmann, MCDET and MELCOR an Example of a Stochastic Module Coupled with an Integral Code for PSA Level 2, International Workshop on Level 2 PSA and Severe Accident Management, Koln, Germany, March 29-31, 2004. 

  3. A. Hakobyan, T. Aldemir, R. Denning, S. Dunagan, D. Kunsman, B. Rutt, U. Catalyurek, Dynamic generation of accident progression event trees, J. Nucl. Eng. Des. 238 (2008) 3457-3467. 

  4. J.M. Izquierdo, J. Hortal, M. Sanchez, E. Melendez, Proposal for a Suitable Strategy of Exceedance Frequency Computation. Implementation on SCAIS Simulation-Based Safety Code Cluster, September 14-17, Nuclear Energy for New Europe, Bled, Slovenia, 2009. 

  5. R.J. Belles, J.W. Cletcher, D.A. Copinger, B.W. Dolan, J.W. Minarick, M.D. Muhlheim, P.D. Oreilly, S. Weerakkody, H. Hamzehee, Precursors to Potential Severe Core Damage Accidents: 1992 a Status Report, NUREG/CR-4674, USNRC, United States, 1998. 

  6. K.S. Hsueh, A. Mosleh, The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants, J. Rel. Eng. Sys. Saf. 52 (1996) 297-314. 

  7. G.M. Oh, M.C. Kim, Y.H. Ryu, Accident Sequence Precursor Analysis of Ulchin Unit 4 Steam Generator Tube Rupture, May 29-30, Proceedings of the Korean Nuclear Society Spring Meeting, Gyeongju, Korea, 2003. 

  8. K. Jones, J. Rothe, W. Dunsford, Symbolic Nuclear Analysis Package (SNAP), NUREG/CR-6974, USNRC, United States, 2009. 

  9. B.G. Kim, H.J. Yoon, S.H. Kim, H.G. Kang, Dynamic sequence analysis for feed-and-bleed operation in an OPR1000, J. Ann. Nuc. Energy 71 (2014) 361-375. 

  10. Korea Atomic Energy Research Institute (KAERI), MOSAIQUE Users Guide Version 1.4, KAERI, Korea, 2011. 

  11. D. Gertman, H. Blackman, J. Marble, J. Byers, C. Smith, The SPAR-H Human Reliability Analysis Method, NUREG/CR-6883, USNRC, United States, 2005. 

  12. D.R. Karanki, Reliability and Safety Engineering, Chapter X, first ed., Springer Publishers, London, 2010. 

  13. 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, J. Rel. Eng. Sys. Saf. 142 (2015) 78-91. 

  14. S.H. Park, S.H. Jang, M.S. Jae, Development of a Framework for Assessing Accident Sequence Precursor and its Application, May 12-13, Transactions of the Korean Nuclear Society Spring Meeting, Jeju, Korea, 2016. 

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