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Development of a code-agnostic computational infrastructure for the dynamic generation of accident progression event trees

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  • Catalyurek, Umit
  • Rutt, Benjamin
  • Metzroth, Kyle
  • Hakobyan, Aram
  • Aldemir, Tunc
  • Denning, Richard
  • Dunagan, Sean
  • Kunsman, David

Abstract

Analysis of dynamic accident progression trees (ADAPT) is a mechanized procedure for the generation of accident progression event trees. Use of ADAPT substantially reduces the manual and computational effort for Level 2 probabilistic risk assessment (PRA) of nuclear power plants; reduces the likelihood of input errors; determines the order of events dynamically; and treats accidents in a phenomenology consistent manner. ADAPT is based on the concept of dynamic event trees which use explicit modeling of the deterministic dynamic processes that take place within the plant (through system simulation codes such as MELCOR, RELAP) for the modeling of stochastic system evolution. The computational infrastructure of ADAPT is presented, along with a prototype implementation of ADAPT using MELCOR for the PRA modeling of a station blackout in a pressurized water reactor. The computational infrastructure allows for flexibility in linking with different simulation codes, parallel processing of the scenarios under consideration, on-line scenario management (initiation as well as termination) and user-friendly graphical capabilities.

Suggested Citation

  • Catalyurek, Umit & Rutt, Benjamin & Metzroth, Kyle & Hakobyan, Aram & Aldemir, Tunc & Denning, Richard & Dunagan, Sean & Kunsman, David, 2010. "Development of a code-agnostic computational infrastructure for the dynamic generation of accident progression event trees," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 278-294.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:3:p:278-294
    DOI: 10.1016/j.ress.2009.10.008
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    1. París, C. & Queral, C. & Mula, J. & Gómez-Magán, J. & Sánchez-Perea, M. & Meléndez, E. & Gil, J., 2019. "Quantitative risk reduction by means of recovery strategies," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 13-32.
    2. Su Han & Tengfei Wang & Jiaqi Chen & Ying Wang & Bo Zhu & Yiqi Zhou, 2021. "Towards the Human–Machine Interaction: Strategies, Design, and Human Reliability Assessment of Crews’ Response to Daily Cargo Ship Navigation Tasks," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    3. 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).
    4. Karanki, D.R. & Dang, V.N. & MacMillan, M.T. & Podofillini, L., 2018. "A comparison of dynamic event tree methods – Case study on a chemical batch reactor," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 542-553.
    5. 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.
    6. Pietro Turati & Nicola Pedroni & Enrico Zio, 2017. "An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 147-159, January.
    7. Ibánez, L. & Hortal, J. & Queral, C. & Gómez-Magán, J. & Sánchez-Perea, M. & Fernández, I. & Meléndez, E. & Expósito, A. & Izquierdo, J.M. & Gil, J. & Marrao, H. & Villalba-Jabonero, E., 2016. "Application of the Integrated Safety Assessment methodology to safety margins. Dynamic Event Trees, Damage Domains and Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 170-193.
    8. Picoco, Claudia & Rychkov, Valentin & Aldemir, Tunc, 2020. "A framework for verifying Dynamic Probabilistic Risk Assessment models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    9. Karanki, Durga Rao & Dang, Vinh N., 2016. "Quantification of Dynamic Event Trees – A comparison with event trees for MLOCA scenario," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 19-31.
    10. Zamalieva, Daniya & Yilmaz, Alper & Aldemir, Tunc, 2013. "Online scenario labeling using a hidden Markov model for assessment of nuclear plant state," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 1-13.
    11. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    12. 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).
    13. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    14. Rebollo, M.J. & Queral, C. & Jimenez, G. & Gomez-Magan, J. & Meléndez, E. & Sanchez-Perea, M., 2016. "Evaluation of the offsite dose contribution to the global risk in a Steam Generator Tube Rupture scenario," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 32-48.
    15. Karanki, D.R. & Rahman, S. & Dang, V.N. & Zerkak, O., 2017. "Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: Tradeoff between accuracy and accident simulations," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 91-102.

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