IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v208y2021ics0951832020308899.html
   My bibliography  Save this article

Risk monitor implementation for the LVR-15 research reactor

Author

Listed:
  • Ferretto, D.
  • Mazzini, G.
  • Ambrosini, W.
  • Aldorf, R.
  • Hrehor, M.

Abstract

This paper presents the implementation of a Phoenix Risk Monitor (RM) for the Light Water Reactor-15 (LVR-15), a nuclear research reactor installed in the Czech Republic. The aim of the work was to introduce dynamic capabilities in the previously developed Probabilistic Safety Assessment (PSA) model. The paper includes a description of the main characteristics of the PSA of LVR-15 and a particular focus is assigned to the developed risk monitor interface, obtained starting from scratch, and to the methodology adopted in its implementation. The interface is conceived to visualize in a meaningful way the safety level of the reactor, suggesting possible interventions to the operator to improve it whenever needed. The risk monitor has been tested with reference to specific conditions of interest, considering, in particular, the effect of external events on the safety status of the reactor, also including the specific weather conditions occurring on a seasonal basis on the reactor site.

Suggested Citation

  • Ferretto, D. & Mazzini, G. & Ambrosini, W. & Aldorf, R. & Hrehor, M., 2021. "Risk monitor implementation for the LVR-15 research reactor," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
  • Handle: RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020308899
    DOI: 10.1016/j.ress.2020.107403
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020308899
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2020.107403?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. Hiromitsu Kumamoto, 2007. "Satisfying Safety Goals by Probabilistic Risk Assessment," Springer Series in Reliability Engineering, Springer, number 978-1-84628-682-7, February.
    2. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. KanÄ ev, DuÅ¡ko & ÄŒepin, Marko & Gjorgiev, Blaže, 2014. "Development and application of a living probabilistic safety assessment tool: Multi-objective multi-dimensional optimization of surveillance requirements in NPPs considering their ageing," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 135-147.
    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. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. KanÄ ev, DuÅ¡ko & Gjorgiev, Blaže & Volkanovski, Andrija & ÄŒepin, Marko, 2016. "Time-dependent unavailability of equipment in an ageing NPP: Sensitivity study of a developed model," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 11-20.
    3. 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).
    4. BahooToroody, Ahmad & De Carlo, Filippo & Paltrinieri, Nicola & Tucci, Mario & Van Gelder, P.H.A.J.M., 2020. "Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Zhang, Jintao & Bagtzoglou, Yiannis & Zhu, Jin & Li, Baikun & Zhang, Wei, 2023. "Fragility-based system performance assessment of critical power infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Martón, I. & Sánchez, A.I. & Carlos, S. & Mullor, R. & Martorell, S., 2023. "Prognosis of wear-out effect on of safety equipment reliability for nuclear power plants long-term safe operation," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Polinpapilinho F. Katina & James C. Pyne & Charles B. Keating & Dragan Komljenovic, 2021. "Complex System Governance as a Framework for Asset Management," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    10. Doménech, E. & Escriche, I. & Martorell, S., 2009. "An approach for assessing CCP effectiveness in food production applications by predictive QRA modelling," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1451-1460.
    11. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    12. Dimaio, F. & Scapinello, O. & Zio, E. & Ciarapica, C. & Cincotta, S. & Crivellari, A. & Decarli, L. & Larosa, L., 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Martorell, P. & Martón, I. & Sánchez, A.I. & Martorell, S., 2017. "Unavailability model for demand-caused failures of safety components addressing degradation by demand-induced stress, maintenance effectiveness and test efficiency," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 18-27.
    14. 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).
    15. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.
    16. Victor Bolbot & Gerasimos Theotokatos & Rainer Hamann & George Psarros & Evangelos Boulougouris, 2021. "Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants," Energies, MDPI, vol. 14(20), pages 1-19, October.
    17. Hayama, Ryouhei & Higashi, Masayasu & Kawahara, Sadahiro & Nakano, Shirou & Kumamoto, Hiromitsu, 2010. "Fault-tolerant automobile steering based on diversity of steer-by-wire, braking and acceleration," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 10-17.
    18. Tang, Zhang-Chun & Zuo, Ming J. & Xiao, Ningcong, 2016. "An efficient method for evaluating the effect of input parameters on the integrity of safety systems," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 111-123.
    19. Ariannik, Mohamadreza & Razi-Kazemi, Ali A. & Lehtonen, Matti, 2020. "An approach on lifetime estimation of distribution transformers based on degree of polymerization," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    20. Ramin Moradi & Andrés Ruiz-Tagle Palazuelos & Enrique Lopez Droguett & Katrina M Groth, 2023. "Toward a framework for risk monitoring of complex engineering systems with online operational data: A deep learning-based solution," Journal of Risk and Reliability, , vol. 237(5), pages 910-921, October.

    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:208:y:2021:i:c:s0951832020308899. 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.