DOI QR코드

DOI QR Code

Safety analysis of marine nuclear reactor in severe accident with dynamic fault trees based on cut sequence method

  • Fang Zhao (University of South China) ;
  • Shuliang Zou (University of South China) ;
  • Shoulong Xu (University of South China) ;
  • Junlong Wang (Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China) ;
  • Tao Xu (Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China) ;
  • Dewen Tang (University of South China)
  • 투고 : 2021.12.22
  • 심사 : 2022.08.14
  • 발행 : 2022.12.25

초록

Dynamic fault tree (DFT) and its related research methods have received extensive attention in safety analysis and reliability engineering. DFT can perform reliability modelling for systems with sequential correlation, resource sharing, and cold and hot spare parts. A technical modelling method of DFT is proposed for modelling ship collision accidents and loss-of-coolant accidents (LOCAs). Qualitative and quantitative analyses of DFT were carried out using the cutting sequence (CS)/extended cutting sequence (ECS) method. The results show nine types of dynamic fault failure modes in ship collision accidents, describing the fault propagation process of a dynamic system and reflect the dynamic changes of the entire accident system. The probability of a ship collision accident is 2.378 × 10-9 by using CS. This failure mode cannot be expressed by a combination of basic events within the same event frame after an LOCA occurs in a marine nuclear reactor because the system contains warm spare parts. Therefore, the probability of losing reactor control was calculated as 8.125 × 10-6 using the ECS. Compared with CS, ECS is more efficient considering expression and processing capabilities, and has a significant advantage considering cost.

키워드

과제정보

The authors would like to express their sincere information to Zou Shuliang, Xu Shoulong and Wang Junlong for their guidance and help in this paper.

참고문헌

  1. Christophe Bonneuil, Pierre-Louis Choquet, Benjamin Franta, Early warnings and emerging accountability: total's responses to global warming, 1971-2021, Global Environmental Change (2021), 102386, https://doi.org/10.1016/j.gloenvcha.2021.102386. 
  2. R. Poudyal, P. Loskot, R. Nepal, et al., Mitigating the current energy crisis in Nepal with renewable energy sources, Renewable and Sustainable Energy Reviews 116 (2019), 109388, https://doi.org/10.1016/j.rser.2019.109388. 
  3. T. Kusunoki, N. Odano, T. Yoritsune, et al., Design of advanced integral-type marine reactor, MRX. Nuclear Engineering & Design 201 (2-3) (2000) 155-175, https://doi.org/10.1016/S0029-5493(00)00285-5. 
  4. S. Yang, D.A. Rui, A. Hw, A novel approach for occupational health and safety and environment risk assessment for nuclear power plant construction project, Jou. Cle. Pro 258 (2020), https://doi.org/10.1016/j.jclepro.2020.120945. 
  5. A.R. Sich, The Chernobyl Nuclear Power Plant Unit-4 Accident, 30-52, https://doi.org/10.1016/B978-0-12-819725-7.00080-5, 2021. 
  6. M. Leslie, Amid uncertainty for US nuclear power, three mile Island shuts down, Engineering 6 (1) (2020), https://doi.org/10.1016/j.eng.2019.11.004, 2.4-5. 
  7. K. Akahane, S. Yonai, S. Fukuda, et al., The Fukushima nuclear power plant accident and exposures in the environment, Env 32 (2) (2012) 136-143, https://doi.org/10.1007/s10669-011-9381-2. 
  8. X. Zheng, H. Tamaki, T. Sugiyama, et al., Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations[J], Reliability Engineering & System Safety 223 (2022), 108503, https://doi.org/10.1016/j.ress.2022.108503. 
  9. M. Ghadhab, S. Junges, J.P. Katoen, et al., Safety analysis for vehicle guidance systems with dynamic fault trees, Reliability Engineering & System Safety 186 (6) (2019) 37-50, https://doi.org/10.1016/j.ress.2019.02.005. 
  10. E. Gascard, Z. Simeu-Abazi, Quantitative analysis of dynamic fault trees by means of Monte Carlo simulations: event-driven simulation approach, Reliability Engineering & System Safety 180 (12) (2018) 487-504, https://doi.org/10.1016/j.ress.2018.07.011. 
  11. J. Dehlinger, J.B. Dugan, Dynamic event/fault tree analysis of multi-agent systems using galileo, IEEE Computer Society (2008) 429-434, https://doi.org/10.1109/QSIC.2008.14. 
  12. G. Merle, J.M. Roussel, J.J. Lesage, et al., Probabilistic algebraic analysis of fault trees with priority dynamic gates and repeated events, IEEE Transactions on Reliability 59 (1) (2010) 250-261, https://doi.org/10.1109/TR.2009.2035793. 
  13. D. Ge, M. Lin, Y. Yang, et al., Quantitative analysis of dynamic fault trees using improved sequential binary decision diagrams, Reliability Engineering & System Safety 142 (2015) 289-299, https://doi.org/10.1016/j.ress.2015.06.001. 
  14. K.S. Hsueh, A. Mosleh, The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants, Reliability Engineering & System Safety 52 (3) (1996) 297-314, https://doi.org/10.1016/0951-8320(95)00140-9. 
  15. D.R. Karanki, V.N. Dang, T.W. Kim, Discrete dynamic event tree analysis of MLOCA using ADS-TRACE[C], International Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, PSA 2011 1 (2011) 610-622. 
  16. A. Alfonsi, et al., Dynamic Event Tree Analysis through RAVEN", ANS PSA 2013 International Topical Meeting on Probabilistic Safety Assessment and Analysis, Sepp. 22-26,2013, American Nuclear Society, Columbia, SC, USA, 2013 (CD-ROM). 
  17. D.R. Karanki, V.N. Dang, Quantification of Dynamic Event Trees - a comparison with event trees for MLOCA scenario, Reliability Engineering & System Safety 147 (2016) 19-31, https://doi.org/10.1016/j.ress.2015.10.017. 
  18. 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, Reliability Engineering and System Safety 142 (2015), https://doi.org/10.1016/j.ress.2015.04.011. 
  19. M. Amirsoltani, A. Pirouzmand, M. Mematollahi, Development of a dynamic event tree (DET)to analyze SBO accident in VVER-1000/V446 nuclear reactor, Annals of Nuclear Energy 165 (2022), 108786, https://doi.org/10.1016/j.anucene.2021.108786. 
  20. Tarannom Parhizkar, B. Ingrid, Utne, an-Erik Vinnem, Online Probabilistic Risk Assessment of Complex Marine Systems, Springer, 2022, https://doi.org/10.1007/978-3-030-88098-9. 
  21. Roshandel R. ParhizkarT, Long term performance degradation analysis and optimization of anode supported solid oxide fuel cell stacks, Energy Conversion Management 133 (2017) 20-30, https://doi.org/10.1016/j.enconman.2016.11.045. 
  22. R. Roshandel, T. Parhizkar, Degradation based optimization framework for long term applications of energy systems. case study: solid oxide fuel cell stacks, Energy 107 (2016) 172-1816, https://doi.org/10.1016/j.energy.2016.04.007. 
  23. T. Parhizkar, S. Hafeznezami, Degradation based operational optimization model to improve the productivity of energy systems. case study: solid oxide fuel cell stacks, Energy Conversion Management 158 (2018) 81-91.  https://doi.org/10.1016/j.enconman.2017.12.045
  24. R. Roshandel, T. Parhizgar, A new approach to optimize the operating conditions of a polymer electrolyte membrane fuel cell based on degradation mechanisms, Energy Systems 4 (3) (2013) 219-237, https://doi.org/10.1007/s12667-012-0075-8. 
  25. T. Parhizkar, Long-term degradation-based modeling and optimization framework, in: Handbook of Research on Predictive Modeling and Optimization Methods in Science Andengineering, 2018 (IGI Global). 
  26. T. Parhizkar, A. Mosleh, R. Roshandel, Aging based optimal scheduling framework for power plants using equivalent operating hour approach, Applied Energy 205 (2017) 1345-1363, https://doi.org/10.1016/j.apenergy.2017.08.065. 
  27. A.F. Sotoodeh, T. Parhizkar, M. Mehrgoo, et al., Aging based design and operation optimization of organic rankine cycle systems, Energy Conversion Management 199 (2019), 111892. 
  28. C.A. Thieme, I.B. Utne, S. Haugen, Assessing ship risk model applicability to marine autonomous surface ships, Ocean Engineering 165 (2018) 140-154, https://doi.org/10.1016/j.oceaneng.2018.07.040. 
  29. T. Parhizkar, J.E. Vinnem, I.B. Utne, A. Mosleh, Supervised dynamic probabilistic risk assessment of complex systems, Part 1: general Overview, Reliability Engineering & SystemSafety (2020), 107406, https://doi.org/10.1016/j.ress.2020.107406. 
  30. T. Parhizkar, I.B. Utne, J.E. Vinnem, A. Mosleh, Supervised dynamic probabilistic risk assessment of complex systems, part 2:Application to risk-informed decision making, practice and results, Reliability Engineering & System Safety 208 (2021), 107392, https://doi.org/10.1016/j.ress.2020.107392. 
  31. https://www.nrc.gov/docs/ML1906/ML19066A390.pdf. 
  32. M. Kloos, J. Peschke, MCDET: a probabilistic dynamics method combining Monte Carlo simulation with the discrete dynamic event tree approach, Nuclear Science and Engineering 153 (2) (2006) 137-156, https://doi.org/10.13182/NSE06-A2601. 
  33. A. Hakobyan, T. Aldemir, R. Denning, et al., Dynamic generation of accident progression event trees, Nuclear Engineering and Design 238 (2008) 3457-3467, https://doi.org/10.1016/j.nucengdes.2008.08.005. 
  34. A. Mosleh, Y.H. Chang, Dynamic PRA Using ADS with RELAP5 Code as its Thermal Hydraulic Module. PSAM, vol. 4, Springer-Verlag, New York, 1998, pp. 2468-2473. 
  35. J.M. Izquierdo, J. Hortal, J. Veci, et al., TRETA: a general purpose simulation code with application on nuclear power plant transients, Journal of the Spanish Nuclear Society 58 (1987) 41-47.