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Safety Analysis of a Nuclear Power Plant against Unexpected Tsunamis

Author

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  • Byung-Ho Kim

    (Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Korea)

  • Min-Jong Song

    (Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Korea)

  • Yong-Sik Cho

    (Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Korea)

Abstract

The East Sea (Sea of Japan), surrounded by Korea, Japan, and Russia, is highly vulnerable to catastrophic tsunamis. Several nuclear power plants (NPPs) operate along the eastern coast of Korea and several more are under construction. Unexpected tsunamis can affect these power plants. The safety of NPPs has attracted worldwide attention since the Fukushima NPP accident. In this study, a coupled numerical model comprising propagation and run-up models was employed to investigate the safety of an NPP against unexpected tsunami attacks. The maximum and minimum tsunami heights and arrival times of the leading tsunami were numerically predicted to ensure the safety of the Uljin NPP, where six plants are already operational and two more are under construction. The predicted numerical results were compared with the safety guidelines proposed by relevant authorities. These results indicate that NPPs are reasonably safe from unexpected tsunamis. Additionally, we confirmed that the tsunami heights and arrival times of a leading tsunami becomes smaller and delayed as the latitude of the epicenter increased.

Suggested Citation

  • Byung-Ho Kim & Min-Jong Song & Yong-Sik Cho, 2022. "Safety Analysis of a Nuclear Power Plant against Unexpected Tsunamis," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13540-:d:947674
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    References listed on IDEAS

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    2. Hyoungsu Park & Daniel T. Cox & Andre R. Barbosa, 2018. "Probabilistic Tsunami Hazard Assessment (PTHA) for resilience assessment of a coastal community," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1117-1139, December.
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    Cited by:

    1. Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.

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