IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v201y2020ics0951832019301425.html
   My bibliography  Save this article

Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications

Author

Listed:
  • Bellaera, R.
  • Bonifetto, R.
  • Di Maio, F.
  • Pedroni, N.
  • Savoldi, L.
  • Zanino, R.
  • Zio, E.

Abstract

The most promising configuration of a nuclear energy fusion system is the tokamak, the largest of which, called ITER, is under construction in Cadarache, France, which uses a complex system of superconducting magnets to generate a field of several tesla (T), aimed at confining the plasma in the toroidal chamber where nuclear fusion reactions occur. For industrial development, the safety of nuclear fusion systems has to be proved and verified by a systematic analysis of operational transients and accidental conditions. Although the final aim of fusion reactors is to reach steady state operation, present-day tokamaks present complex dynamic features, as their operation is based on the transformer principle with a subset of the superconducting magnets operating in a pulsed mode, to inductively generate plasma currents of the order of several MA. We adopt the framework of Integrated Deterministic and Probabilistic Safety Assessment (IDPSA), for identifying the component failures that may cause a Loss-Of-Flow-Accident (LOFA) in the cooling circuit of a superconducting magnet for fusion applications. Post-processing of the simulated scenarios for the identification of the abnormal transients is performed in an unsupervised manner resorting to a spectral clustering approach embedding a Fuzzy-C Means (FCM) that is compared with an Extended Symbolic Aggregate approximation (ESAX) from the literature that also resorts to the FCM for the classification.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:201:y:2020:i:c:s0951832019301425
    DOI: 10.1016/j.ress.2020.106945
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832019301425
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.106945?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. 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.
    2. Francesco Di Maio & Samuele Baronchelli & Enrico Zio, 2015. "A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 142-156, January.
    3. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    4. 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.
    5. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    6. Y. Wu & Z. Chen & L. Hu & M. Jin & Y. Li & J. Jiang & J. Yu & C. Alejaldre & E. Stevens & K. Kim & D. Maisonnier & A. Kalashnikov & K. Tobita & D. Jackson & D. Perrault, 2016. "Identification of safety gaps for fusion demonstration reactors," Nature Energy, Nature, vol. 1(12), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Destino, V. & Bonifetto, R. & Maio, F. Di & Pedroni, N. & Zanino, R. & Zio, E., 2021. "Identification of LOFA precursors in ITER superconducting magnet cryogenic cooling circuit," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Vincenzo Destino & Nicola Pedroni & Roberto Bonifetto & Francesco Di Maio & Laura Savoldi & Enrico Zio, 2021. "Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit," Energies, MDPI, vol. 14(17), pages 1-37, September.
    3. Ming Sun & Taosheng Li & Jie Yu & Daochuan Ge & Ying Bai & Longlong Tao, 2022. "A New Reliability Allocation Method Based on PSA and AHP for Fusion Reactors," Energies, MDPI, vol. 15(13), pages 1-10, July.

    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. Vincenzo Destino & Nicola Pedroni & Roberto Bonifetto & Francesco Di Maio & Laura Savoldi & Enrico Zio, 2021. "Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit," Energies, MDPI, vol. 14(17), pages 1-37, September.
    2. 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.
    3. Jonathan Dumas & Antoine Dubois & Paolo Thiran & Pierre Jacques & Francesco Contino & Bertrand Cornélusse & Gauthier Limpens, 2022. "The Energy Return on Investment of Whole-Energy Systems: Application to Belgium," Biophysical Economics and Resource Quality, Springer, vol. 7(4), pages 1-34, December.
    4. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    5. Guo, Zehua & Dailey, Ryan & Feng, Tangtao & Zhou, Yukun & Sun, Zhongning & Corradini, Michael L & Wang, Jun, 2021. "Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    8. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    10. Xia, Dongqin & Li, Yazhou & He, Yanling & Zhang, Tingting & Wang, Yongliang & Gu, Jibao, 2019. "Exploring the role of cultural individualism and collectivism on public acceptance of nuclear energy," Energy Policy, Elsevier, vol. 132(C), pages 208-215.
    11. Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    12. 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.
    13. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Chen, Xiaoqian, 2020. "An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    14. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Nicolae Brînzei & Jean-François Aubry, 2018. "Graphs models and algorithms for reliability assessment of coherent and non-coherent systems," Journal of Risk and Reliability, , vol. 232(2), pages 201-215, April.
    16. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    17. Tosoni, E. & Salo, A. & Govaerts, J. & Zio, E., 2019. "Comprehensiveness of scenarios in the safety assessment of nuclear waste repositories," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 561-573.
    18. 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.
    19. 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.
    20. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(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:201:y:2020:i:c:s0951832019301425. 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.