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Development of a Physics-Based Monitoring Algorithm Detecting CO 2 Ingress Accidents in a Sodium-Cooled Fast Reactor

Author

Listed:
  • Hyeonmin Kim

    (ICT Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Jung-Taek Kim

    (ICT Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Jaehyuk Eoh

    (SFR Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Dong-Won Lim

    (Department of Mechanical Engineering, University of Suwon, Gyeonggi-do 18323, Korea)

Abstract

One of the benefits of the supercritical CO 2 Brayton cycle in Sodium-cooled Fast Reactors is an enhanced plant safety, since potential reactions of CO 2 with liquid sodium have been reported to be less stringent than a sodium-water reaction found in the Rankine cycle. However, moderate chemical interactions between CO 2 and liquid sodium make detecting CO 2 ingress accidents harder. Thus, this paper proposes a new physics-based detection algorithm by comparing the real-time pressure measurements of two identical heat exchangers for the early detection. The CO 2 ingress occurs owing to a crack at the pressure boundary wall, a certain self-recovery of structural damage does not happen over time, and an accident probabilistically starts at only one component of two. The proposed physics-based method with the probabilistic analysis was compared to the pure data-based method. Finally, the damage degradation was developed with a simplified mass and energy transfer model, and the proposed algorithm was verified with experimental data. The results show that a 99.99% detection probability can be achieved for the air ingress of 30 cc/s, which is equivalent to the 0.12 g/s CO 2 ingress, in a 70 s detection time, limiting down to 0.1% false alarms due to sensor noise.

Suggested Citation

  • Hyeonmin Kim & Jung-Taek Kim & Jaehyuk Eoh & Dong-Won Lim, 2018. "Development of a Physics-Based Monitoring Algorithm Detecting CO 2 Ingress Accidents in a Sodium-Cooled Fast Reactor," Energies, MDPI, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:1-:d:191909
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    References listed on IDEAS

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    1. Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
    2. Abram, Tim & Ion, Sue, 2008. "Generation-IV nuclear power: A review of the state of the science," Energy Policy, Elsevier, vol. 36(12), pages 4323-4330, December.
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    Cited by:

    1. Alexandra Akins & Derek Kultgen & Alexander Heifetz, 2023. "Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder," Energies, MDPI, vol. 16(13), pages 1-19, June.

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