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

The advantages of artificial intelligence (AI) algorithms in rapid prediction, self-learning, and strong generalizability have been applied to address the complexities of thermal-hydraulic 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.