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Quantification of Dynamic Event Trees – A comparison with event trees for MLOCA scenario

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  • Karanki, Durga Rao
  • Dang, Vinh N.

Abstract

Dynamic event trees (DETs) provide the means to simulate physical system evolutions, the evolution of system states due to stochastic events, and the dynamic interactions between these evolutions. For risk assessment, the framework avoids the need to specify a priori the sequence of stochastic events prior to the plant response simulation and to iterate between the definition of the sequences and simulation of the responses. For nuclear power plants, DETs have been applied to treat scenarios up to core damage as well as post-core damage accident scenarios. The quantification of the frequencies of the sequences leading to the undesired system outcomes, while conceptually straightforward, faces several implementation issues. These include, for instance, the treatment of support system dependencies and of events characterized by a continuous aleatory variable. Some solutions to these issues are proposed and applied in a case study dealing with Medium Break Loss of Coolant Accident (MLOCA) scenarios. Additionally, the results obtained from DET quantification are compared with those estimated with a “classical†event tree model for these scenarios. This comparison provides some case-specific results on the impact of the improved modeling of dynamics on risk estimates.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:147:y:2016:i:c:p:19-31
    DOI: 10.1016/j.ress.2015.10.017
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    References listed on IDEAS

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    1. 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.
    2. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    3. 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.
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    Cited by:

    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. Taleb-Berrouane, Mohammed & Khan, Faisal & Amyotte, Paul, 2020. "Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. 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.
    4. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Nejad, Hamed S. & Parhizkar, Tarannom & Mosleh, Ali, 2022. "Automatic generation of event sequence diagrams for guiding simulation based dynamic probabilistic risk assessment (SIMPRA) of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Gascard, Eric & Simeu-Abazi, Zineb, 2018. "Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 487-504.
    7. 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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