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A Bayesian Network Approach to Predicting Severity Status in Nuclear Reactor Accidents with Resilience to Missing Data

Author

Listed:
  • Kaiyu Li

    (College of Nuclear Science and Technology, Naval University of Engineering of PLA, Wuhan 430033, China
    Wuhan Second Ship Design and Research Institute, Wuhan 430000, China)

  • Ling Chen

    (College of Nuclear Science and Technology, Naval University of Engineering of PLA, Wuhan 430033, China)

  • Xinxin Cai

    (Wuhan Second Ship Design and Research Institute, Wuhan 430000, China)

  • Cai Xu

    (Third Military Representative Office Stationed in Wuhan, Wuhan 430000, China)

  • Yuncheng Lu

    (Wuhan Second Ship Design and Research Institute, Wuhan 430000, China)

  • Shengfeng Luo

    (Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China)

  • Wenlin Wang

    (Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China)

  • Lizhi Jiang

    (College of Nuclear Science and Technology, Naval University of Engineering of PLA, Wuhan 430033, China)

  • Guohua Wu

    (Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China)

Abstract

Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R 2 values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production.

Suggested Citation

  • Kaiyu Li & Ling Chen & Xinxin Cai & Cai Xu & Yuncheng Lu & Shengfeng Luo & Wenlin Wang & Lizhi Jiang & Guohua Wu, 2025. "A Bayesian Network Approach to Predicting Severity Status in Nuclear Reactor Accidents with Resilience to Missing Data," Energies, MDPI, vol. 18(11), pages 1-20, May.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2684-:d:1662005
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