Application of the Dynamic Probabilistic Assessment Method for Predicting Possible Accidents at NPPs with VVER-1200 Reactors

Authors

  • Dmitriy Yu. Ushakov
  • Yuriy B. Vorobyev
  • Aleksandr E. Chetverikov

DOI:

https://doi.org/10.24160/1993-6982-2026-4-144-152

Keywords:

NPP safety, computer technologies, computer codes, DPSA

Abstract

The article considers the principles of the dynamic probabilistic safety assessment (DPSA) method using the global optimum search approach – a genetic algorithm (GA). The DPSA method with the use of GA is implemented in the Nuclear Plant Optimizer (NPO) software, which employs parallel computing mode and connected with the RELAP5 computer code. The aim of the work is to demonstrate the possibility of combining the GA and DPSA technologies (GA–DPSA) for NPP safety analysis purposes. To demonstrate the proposed procedure, accidents involving guillotine break of the main coolant pipeline (MCP) and a small-break leak of the MCP cold leg for a VVER-1200 reactor were simulated. The thermal-hydraulic model was implemented using the RELAP5 best-estimate computer code. The computation results have revealed accident progression scenarios in which earlier actuation of safety systems is less favorable in terms of safety than their later actuation. The proposed methodology makes it possible to identify hypothetical accidents that have not been revealed as yet, and can be used for further studies on assessing the NPP safety.

Author Biographies

Dmitriy Yu. Ushakov

Ph.D.-student of Nuclear Power Plants Dept., NRU MPEI, e-mail: UshakovDY@mpei.ru

Yuriy B. Vorobyev

Ph.D. (Techn.), Assistant Professor of Nuclear Power Plants Dept., NRU MPEI, e-mail: VorobyevYB@mpei.ru

Aleksandr E. Chetverikov

Ph.D. (Techn.), Assistant Professor of Nuclear Power Plants Dept., NRU MPEI, e-mail: ChetverikovAY@mpei.ru

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Для цитирования: Ушаков Д.Ю., Воробьев Ю.Б., Четвериков А.Е. Применение метода динамического вероятностного анализа для прогнозирования возможных аварий на атомных электростанциях с ВВЭР-1200 // Вестник МЭИ. 2026. № 4. С. 144—152. DOI: 10.24160/1993-6982-2026-4-144-152

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Конфликт интересов: авторы заявляют об отсутствии конфликта интересов

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1. Beal J. e. a. Modeling Nuclear Power Plant Piping Reliability by Coupling a Human Reliability Analysis-based Maintenance Model with a Physical Degradation Model. Reliability Eng. & Syst. Safety. 2025;255:110655.

2. Hakobyan A.P. Severe Accident Analysis Using Dynamic Accident Progression Event Trees. Columbus: Ohio State University, 2006;225.

3. Zio E., Pedroni N. Estimation of the Functional Failure Probability of a Thermal–hydraulic Passive System by Subset Simulation. Nuclear Eng. and Design. 2009;239(3):580—599.

4. Zio E., Pedroni N. How to Effectively Compute the Reliability of a Thermal-hydraulic Nuclear Passive System. Nuclear Eng. and Design. 2011;241(1):310—327.

5. Chyong V.K.N., Vorob'ev Yu.B. Opredelenie Naibolee Opasnyh Avariynyh Situatsiy na AES i Identifikatsiya Ih Vozniknoveniya v Protsesse Ekspluatatsii. Vestnik MEI. 2016;5:30—38. (in Russian).

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7. Parhizkar T., Vinnem J.E., Utne I.B., Mosleh A. Supervised Dynamic Probabilistic Risk Assessment of Complex Systems, Pt. 1: General Overview. Reliability Eng. & Syst. Safety. 2021;208:107406.

8. Belhadj M., Hassan M., Aldemir T. On the Need for Dynamic Methodologies in Risk and Reliability Studies. Reliability Eng. & Syst. Safety. 1992;38(3):219—236.

9. Coppit D., Sullivan K.J., Dugan J.B. Formal Semantics of Models for Computational Engineering: a Case Study on Dynamic Fault Trees. Proc. XI Intern. Symp. Software Reliability Eng. San Jose, 2000:270—282.

10. Marseguerra M., Zio E., Devooght J., Labeau P.E. A Concept Paper on Dynamic Reliability Via Monte Carlo Simulation. Math. and Computers in Simulation. 1998;47(2—5):371—382.

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12. Zha B. e. a. Deep Transformer Networks for Time Series Classification: the NPP Safety Case. Proc. Intern. Topical Meeting on Probabilistic Safety Assessment and Analysis. 2021:1065—1074.

13. Kim J., Shah A.U.A., Kang H.G. Dynamic Risk Assessment with Bayesian Network and Clustering Analysis. Reliability Eng. & Syst. Safety. 2020;201:106959.

14. Lee J.H., Yilmaz A., Denning R., Aldemir T. An Online Operator Support Tool for Severe Accident Management in Nuclear Power Plants Using Dynamic Event Trees and Deep Learning. Annals of Nuclear Energy. 2020;146:107626.

15. Bae J., Park J.W., Lee S.J. Limit Surface/States Searching Algorithm with a Deep Neural Network and Monte Carlo Dropout for Nuclear Power Plant Safety Assessment. Appl. Soft Computing. 2022;124:109007.

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17. Koutsourelakis P.S., Pradlwarter H.J., Schuëller G.I. Reliability of Structures in High Dimensions, Pt. I: Algorithms and Applications. Probabilistic Eng. Mech. 2004;19(4):409—417.

18. Zio E., Pedroni N. An Optimized Line Sampling Method for the Estimation of the Failure Probability of Nuclear Passive Systems. Reliability Eng. & Syst. Safety. 2010;95(12):1300—1313.

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For citation: Ushakov D.Yu., Vorobyev Yu.B., Chetverikov A.E. Application of the Dynamic Probabilistic Assessment Method for Predicting Possible Accidents at NPPs with VVER-1200 Reactors. Bulletin of MPEI. 2026;4:144—152. (in Russian). DOI: 10.24160/1993-6982-2026-4-144-152

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Conflict of interests: the authors declare no conflict of interest

Published

2026-08-23

Issue

Section

Nuclear Power Plants, Fuel Cycle, Radiation Safety (Technical Sciences) (2.4.9)