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Shaping the future of nuclear reactors with digital twins: Current developments and perspectives

Author

Listed:
  • Huang, Qingyu
  • Zeng, Wei
  • Liu, Jia
  • Zhang, Zhuo
  • Deng, Jian
  • Qiu, Zhifang
  • Xu, Le
  • Wei, Zonglan
  • Lu, Qi
  • Gong, Lanxin
  • Shi, Chunsen
  • Zhong, Xianping

Abstract

Digital Twin(DT) technology offers a transformative pathway to mitigate the fundamental trade-off in nuclear energy between uncompromising safety and economic competitiveness. This review synthesizes the state of the field, revealing a critical insight: despite progress, implementations of nuclear DTs globally remain nascent, predominantly confined to low-to-mid maturity levels. This developmental lag stems from unique, sector-specific challenges: severe data scarcity due to extreme in-vessel conditions, an irreconcilable trade-off between high-fidelity multi-physics model accuracy and real-time computational demands, and the “black-box” nature of data-driven artificial intelligence conflicting with nuclear safety demand for interpretability and verifiable trust. A comparative analysis against aerospace, power grid, maritime, and healthcare sectors confirms that nuclear applications face exceptionally stringent regulatory requirements and uniquely high technical barriers. To overcome these hurdles, this work establishes a comprehensive DT application framework and introduces a novel five-tier maturity hierarchy for nuclear reactors. This model provides a standardized, actionable roadmap for technological evolution—from basic simulation guidance to fully symbiotic autonomy—thereby positioning the DT as the indispensable engine for the future of safe, efficient, and intelligent nuclear power.

Suggested Citation

  • Huang, Qingyu & Zeng, Wei & Liu, Jia & Zhang, Zhuo & Deng, Jian & Qiu, Zhifang & Xu, Le & Wei, Zonglan & Lu, Qi & Gong, Lanxin & Shi, Chunsen & Zhong, Xianping, 2025. "Shaping the future of nuclear reactors with digital twins: Current developments and perspectives," Applied Energy, Elsevier, vol. 402(PA).
  • Handle: RePEc:eee:appene:v:402:y:2025:i:pa:s0306261925016526
    DOI: 10.1016/j.apenergy.2025.126922
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    References listed on IDEAS

    as
    1. Song, Houde & Song, Meiqi & Liu, Xiaojing, 2022. "Online autonomous calibration of digital twins using machine learning with application to nuclear power plants," Applied Energy, Elsevier, vol. 326(C).
    2. Hwang, Jaemin & Kim, Jiwon & Yoon, Sungmin, 2025. "DT-BEMS: Digital twin-enabled building energy management system for information fusion and energy efficiency," Energy, Elsevier, vol. 326(C).
    3. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    4. Mason, Paolo, 2017. "A Bayesian analysis of component life expectancy and its implications on the inspection schedule," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 87-94.
    5. 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.
    6. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    7. Wadim Strielkowski & Gordon Rausser & Evgeny Kuzmin, 2022. "Digital Revolution in the Energy Sector: Effects of Using Digital Twin Technology," Lecture Notes in Information Systems and Organization, in: Vikas Kumar & Jiewu Leng & Victoria Akberdina & Evgeny Kuzmin (ed.), Digital Transformation in Industry, pages 43-55, Springer.
    8. Compare, Michele & Bellani, Luca & Zio, Enrico, 2017. "Reliability model of a component equipped with PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 4-11.
    9. Klemenc, Jernej, 2015. "Influence of fatigue–life data modelling on the estimated reliability of a structure subjected to a constant-amplitude loading," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 238-247.
    10. Brendan Kochunas & Xun Huan, 2021. "Digital Twin Concepts with Uncertainty for Nuclear Power Applications," Energies, MDPI, vol. 14(14), pages 1-32, July.
    11. Zhou, Shiqi & Lin, Meng & He, Jun & Wu, Yuzeng & Wang, Xu, 2025. "Unsupervised clustering research of nuclear power plants under unlabeled unknown fault diagnosis scenario," Energy, Elsevier, vol. 326(C).
    12. M. R. Mahendrini Fernando Ariyachandra & Gayan Wedawatta, 2023. "RETRACTED: Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    13. Ben Qi & Jingang Liang & Jiejuan Tong, 2023. "Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective," Energies, MDPI, vol. 16(4), pages 1-27, February.
    14. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
    15. Slot, René M.M. & Sørensen, John D. & Sudret, Bruno & Svenningsen, Lasse & Thøgersen, Morten L., 2020. "Surrogate model uncertainty in wind turbine reliability assessment," Renewable Energy, Elsevier, vol. 151(C), pages 1150-1162.
    16. Wang, Fu & Xiahou, Tangfan & Zhang, Xian & He, Pan & Yang, Taibo & Niu, Jiang & Liu, Caixue & Liu, Yu, 2024. "Convolutional preprocessing Transformer-based fault diagnosis for rectifier-filter circuits in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    17. Hossein Omrany & Karam M. Al-Obaidi & Amreen Husain & Amirhosein Ghaffarianhoseini, 2023. "Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
    18. Long Chen & Xiang Xie & Qiuchen Lu & Ajith Kumar Parlikad & Michael Pitt & Jian Yang, 2021. "Gemini Principles-Based Digital Twin Maturity Model for Asset Management," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    19. Peter Bauer & Bjorn Stevens & Wilco Hazeleger, 2021. "A digital twin of Earth for the green transition," Nature Climate Change, Nature, vol. 11(2), pages 80-83, February.
    20. repec:aen:eeepjl:1_2_a07 is not listed on IDEAS
    21. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    22. Alberto Carrassi & Marc Bocquet & Laurent Bertino & Geir Evensen, 2018. "Data assimilation in the geosciences: An overview of methods, issues, and perspectives," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 9(5), September.
    23. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2020. "A novel method for maintenance record clustering and its application to a case study of maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    24. Chookah, M. & Nuhi, M. & Modarres, M., 2011. "A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1601-1610.
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