人工智能算法在核反应堆热工水力预测分析中的初步探索  

Application of Artificial Intelligence Algorithms in Thermal-Hydraulic Analysis of Nuclear Reactors

作　　者：章静[1] 王明军[1] 田文喜[1] 苏光辉[1] 秋穗正[1] Zhang Jing;Wang Mingjun;Tian Wenxi;Su Guanghui;Qiu Suizheng(School of Nuclear Science and Technology,Xi’an Jiaotong University,Xi’an,710049,China)

机构地区：[1]西安交通大学核科学与技术学院,西安710049

出　　处：《核动力工程》2025年第2期127-140,共14页Nuclear Power Engineering

基　　金：国家自然科学基金面上项目(12175173)。

摘　　要：人工智能算法快速预测、自学习与强泛用性的优势已应用于解决核反应堆热工水力现象和机理复杂的问题,包括热工水力参数预测、热工安全分析程序优化与计算流体动力学(CFD)效率提升等。本文回顾了人工智能算法在流型、沸腾换热及临界流等热工水力参数预测研究现状,针对严苛运行条件下机理不明、预测范围局限性问题,基于人工智能非线性快速预测优势扩展分析范围与精度;针对热工分析程序受限于参数模型的问题,利用人工智能自学习、自适应与极强泛用性优势,通过模型校准及数据同化技术提升复杂现象参数识别能力与预测性能;基于模型降阶与快速预测,提高热工水力物理场复杂现象参数的计算效率和多维复现重构能力。提出人工智能算法在反应堆系统大型关键设备全寿期准确预测、液态金属快堆等新型先进反应堆的加快设计迭代、跨尺度多物理场复杂交互的加速优化的未来应用前景。The advantages of artificial intelligence(AI)algorithms in rapid prediction,selflearning,and strong generalizability have been applied to address the complexities of thermalhydraulic phenomena and mechanisms in nuclear reactors.These applications include predictions of thermal-hydraulic parameters,optimization of thermal safety analysis codes,and enhancements in computational fluid dynamics(CFD)efficiency.This paper reviews the current state of research on AI algorithms in predicting thermal-hydraulic parameters such as flow regimes,boiling heat transfer,and critical flow.To address challenges such as unknown mechanisms and limited prediction ranges under extreme operating conditions,this study leverages the nonlinear rapid prediction capabilities of AI to expand the scope and accuracy of analyses.For thermal analysis codes constrained by parameter models,the self-learning,adaptive,and highly generalizable features of AI are utilized to improve the identification and prediction of complex phenomenon parameters through model calibration and data assimilation techniques.By employing model reduction and fast prediction methods,AI enhances the computational efficiency and the multidimensional reconstruction of complex thermal-hydraulic physical fields.Furthermore,the study highlights the future prospects of AI algorithms in accurately predicting the full lifecycle performance of key components in large-scale reactor systems,accelerating design iterations for advanced reactors such as liquid-metal fast reactors,and optimizing cross-scale,multiphysics interactions in a more efficient manner.

关 键 词：人工智能 核反应堆热工水力 计算流体动力学(CFD) 热工水力参数预测 安全分析程序 

分 类 号：TL334[核科学技术—核技术及应用]