IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v251y2024ics0951832024004782.html
   My bibliography  Save this article

A case study to address the limitation of accident scenario identifications with respect to diverse manual responses

Author

Listed:
  • Park, Jinkyun
  • Kim, Hyeonmin

Abstract

Probabilistic safety assessment (PSA) is a tool for securing the operational safety of nuclear power plants. One unique benefit of PSA is to identify accident scenarios leading to undesirable consequences. Since these accident scenarios result from highly complicated combinations of automatic and manual responses, a huge number of simulations are generally required with a precise thermal-hydraulic (TH) code. Therefore, current PSA typically reduces the number of TH code runs by focusing on limited numbers of safety-critical components with reasonable engineering assumptions. Nevertheless, it has been pointed out that the current catalog of accident scenarios in PSA may be insufficient to some extent. In order to corroborate this claim, in this work, 14,348,907 simulation conditions were randomly generated after combining 79 manual responses identified from the emergency operating procedure of a steam generator tube rupture (SGTR) event in a reference nuclear power plant. Results show that about 14.7 % of the simulations correspond to an undesirable consequence pertaining to the SGTR. Accordingly, in terms of enhancing the quality of PSA results, the results of this study support that it is inevitable to develop a promising way to reduce the amount of computational resources required by large numbers of TH code runs.

Suggested Citation

  • Park, Jinkyun & Kim, Hyeonmin, 2024. "A case study to address the limitation of accident scenario identifications with respect to diverse manual responses," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004782
    DOI: 10.1016/j.ress.2024.110406
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024004782
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110406?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hu, Yunwei & Parhizkar, Tarannom & Mosleh, Ali, 2022. "Guided simulation for dynamic probabilistic risk assessment of complex systems: Concept, method, and application," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Park, Jinkyun & Jung, Wondea & Yang, Joon-Eon, 2012. "Investigating the effect of communication characteristics on crew performance under the simulated emergency condition of nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 1-13.
    4. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Suo, Weilan & Wang, Lin & Li, Jianping, 2021. "Probabilistic risk assessment for interdependent critical infrastructures: A scenario-driven dynamic stochastic model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Rahman, S. & Karanki, D.R. & Epiney, A. & Wicaksono, D. & Zerkak, O. & Dang, V.N., 2018. "Deterministic sampling for propagating epistemic and aleatory uncertainty in dynamic event tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 62-78.
    7. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2023. "Reliable neural networks for regression uncertainty estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Jinkyun Park, 2009. "The Complexity of Proceduralized Tasks," Springer Series in Reliability Engineering, Springer, number 978-1-84882-791-2, June.
    9. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. Karanki, Durga Rao & Kim, Tae-Wan & Dang, Vinh N., 2015. "A dynamic event tree informed approach to probabilistic accident sequence modeling: Dynamics and variabilities in medium LOCA," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 78-91.
    11. Zeng, Zhiguo & Barros, Anne & Coit, David, 2023. "Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    12. Mandelli, D. & Parisi, C. & Alfonsi, A. & Maljovec, D. & Boring, R. & Ewing, S. & St Germain, S. & Smith, C. & Rabiti, C. & Rasmussen, M., 2019. "Multi-unit dynamic PRA," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 303-317.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Zheng, Xiaoyu & Tamaki, Hitoshi & Sugiyama, Tomoyuki & Maruyama, Yu, 2022. "Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    3. Lilli, Giordano & Sanavia, Matteo & Oboe, Roberto & Vianello, Chiara & Manzolaro, Mattia & De Ruvo, Pasquale Luca & Andrighetto, Alberto, 2024. "A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Feng, Jian Rui & Yu, Guanghui & Zhao, Mengke & Zhang, Jiaqing & Lu, Shouxiang, 2022. "Dynamic risk assessment framework for industrial systems based on accidents chain theory: The case study of fire and explosion risk of UHV converter transformer," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Park, Jinkyun & Kim, Yochan, 2018. "A novel speech-act coding scheme to visualize the intention of crew communications to cope with simulated off-normal conditions of nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 236-246.
    6. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Wang, Chenyushu & Cai, Baoping & Shao, Xiaoyan & Zhao, Liqian & Sui, Zhongfei & Liu, Keyang & Khan, Javed Akbar & Gao, Lei, 2023. "Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    8. Pan, Xing & Du, Hengte & Yu, Haofan, 2024. "A method for safety analysis of human-machine systems based on dynamic Bayesian simulation," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    9. Maidana, Renan G. & Parhizkar, Tarannom & Martin, Gabriel San & Utne, Ingrid B., 2024. "Dynamic probabilistic risk assessment with K-shortest-paths planning for generating discrete dynamic event trees," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Antonello, Federico & Buongiorno, Jacopo & Zio, Enrico, 2022. "A methodology to perform dynamic risk assessment using system theory and modeling and simulation: Application to nuclear batteries," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Li, Jin-Yang & Lu, Jubin & Zhou, Hao, 2023. "Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    12. Ge, Xiangyu & Zhou, Qianxiang & Liu, Zhongqi, 2020. "Assessment of space station on-orbit maintenance task complexity," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    13. Lin Wang & Yuping Xing, 2022. "Risk Assessment of a Coupled Natural Gas and Electricity Market Considering Dual Interactions: A System Dynamics Model," Energies, MDPI, vol. 16(1), pages 1-18, December.
    14. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Xu, Gaowei & Azhari, Fae, 2022. "Data-driven optimization of repair schemes and inspection intervals for highway bridges," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    16. Jang, Inseok & Kim, Yochan & Park, Jinkyun, 2021. "Investigating the Effect of Task Complexity on the Occurrence of Human Errors observed in a Nuclear Power Plant Full-Scope Simulator," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    17. He, Rui & Zhu, Jingyu & Chen, Guoming & Tian, Zhigang, 2022. "A real-time probabilistic risk assessment method for the petrochemical industry based on data monitoring," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    18. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. Yoon, Jae Young & Kim, Dong-San, 2022. "Estimating the adverse effects of inter-unit radioactive release on operator actions at a multi-unit site," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    20. Li, Weijun & Sun, Qiqi & Zhang, Jiwang & Zhang, Laibin, 2024. "Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004782. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.