AI助力核反应堆物理，提升仿真与安全性能
Artificial Intelligence in Reactor Physics: Current Status and Future Prospects
- 用机器学习替代或优化传统求解方法，提高计算效率
- 已在稳态、瞬态及燃耗问题中实现工业级应用
- 适合关注核能智能化的工程师与研究者
反应堆物理研究中子特性，利用模型分析中子与材料在核反应堆中的相互作用。人工智能在反应堆物理中已取得显著进展，应用于运行模拟、安全设计、实时监测、堆芯管理与维护等领域。本文全面综述了人工智能在反应堆物理中的应用，重点关注机器学习（ML）方法，系统梳理其应用场景、前沿课题、未解挑战及未来方向。从方程求解到状态参数预测，涵盖稳态、瞬态和燃耗问题，多数研究通过提升确定性方法效率或修正不确定性模型，实现工业需求的建模。然而，当前研究仍较分散，模型泛化能力不足。未来潜力在于解决理论难题，推动构建代理模型与数字孪生等工业应用。
原文摘要 · Abstract (English)
Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics, e.g., in operational simulations, safety design, real-time monitoring, core management and maintenance. This paper presents a comprehensive review of AI approaches in reactor physics, especially considering the category of Machine Learning (ML), with the aim of describing the application scenarios, frontier topics, unsolved challenges and future research directions. From equation solving and state parameter prediction to nuclear industry applications, this paper provides a step-by-step overview of ML methods applied to steady-state, transient and combustion problems. Most literature works achieve industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods, which leads to successful applications. However, research on ML methods in reactor physics is somewhat fragmented, and the ability to generalize models needs to be strengthened. Progress is still possible, especially in addressing theoretical challenges and enhancing industrial applications such as building surrogate models and digital twins.
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