Advanced Control for Nuclear Energy Systems: Intelligent Control and Operation Strategies

Dear Colleagues,

Nuclear energy is a promising alternative for providing stable and clean energy for AI data centers, and it continues to play a crucial role in meeting global energy demands while mitigating carbon emissions. As new reactor designs and deployment strategies emerge, ensuring the safe, efficient, and flexible operation of nuclear power plants has become more critical than ever. Advanced control systems, leveraging cutting-edge technologies in artificial intelligence, machine learning, and data analytics, hold significant promise for enhancing reliability and optimizing performance in nuclear energy systems. From addressing the complexities of load-following capabilities to implementing predictive maintenance solutions, intelligent control approaches can significantly improve the operational margins and economic viability of both conventional and next-generation nuclear reactors.

This Special Issue, entitled “Advanced Control for Nuclear Energy Systems: Intelligent Control and Operation Strategies”, aims to bring together researchers and practitioners to share the latest developments and breakthroughs in this rapidly evolving field. Submissions encompassing theoretical frameworks, simulation studies, and real-world applications are all welcomed. By showcasing novel strategies for monitoring, control, and automation, this Special Issue sheds light on the integration of smart technologies into nuclear energy systems to achieve robust, reliable, and adaptable systems under diverse and dynamic operational conditions. Ultimately, this collection fosters a deeper understanding of advanced control strategies and promotes innovative collaborations that will pave the way for safer, more flexible, and economically viable nuclear energy infrastructures.

Topics of interest for publication include, but are not limited to, the following themes:

• Intelligent control algorithms and architectures for nuclear reactors;
• Digital twins, simulation, and modeling for nuclear plant optimization;
• Machine learning and data-driven approaches for fault detection and diagnosis;
• Predictive maintenance and condition monitoring in nuclear power plants;
• Robust and adaptive control strategies for load-following reactors;
• Advanced sensing and instrumentation for enhanced plant monitoring;
• Cyber–physical security and secure control strategies;
• Control system design and verification for small modular reactors and microreactors;
• Human–machine interfaces and supervisory control systems;
• Real-time optimization of nuclear processes and operations.

Dr. Yang Liu
Dr. Xingang Zhao
Guest Editors

Manuscript Submission Information

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