基于POD及神经网络的蒸汽发生器传热管内温度场的快速重构技术研究  

Research on Rapid Reconstruction Technology of Temperature Field in Heat Transfer Tube of Steam Generator Based on POD and Neural Network

作　　者：张贺[1,2] 梁彪 王博 谭思超[1,2] 韩蕊 李江宽 田瑞峰[1,2] Zhang He;Liang Biao;Wang Bo;Tan Sichao;Han Rui;Li Jiangkuan;Tian Ruifeng(Heilongjiang Provincial Key Laboratory of Nuclear Power System&Equipment,Harbin Engineering University,Harbin,150001,China;College of Nuclear Science and Technology,Harbin Engineering University,Harbin,150001,China)

机构地区：[1]哈尔滨工程大学黑龙江省核动力装置性能与设备重点实验室,哈尔滨150001 [2]哈尔滨工程大学核科学与技术学院,哈尔滨150001

出　　处：《核动力工程》2025年第2期90-97,共8页Nuclear Power Engineering

基　　金：中核集团领创基金项目(CNNC-LCKY-202251)。

摘　　要：套管式直流蒸汽发生器的二次侧流域涉及到复杂的两相流动,数值模拟方法虽然能够精准地进行仿真计算,但其计算速度缓慢,对于多工况、瞬态条件下的计算耗时长,计算资源占用较大。模型降阶是一种将复杂系统转化为一个近似简化系统的方法,能够在保留原系统主要特征的同时实现快速计算。本研究采用本征正交分解(POD)方法对换热管内温度场进行模型降阶,截取有限模态对原复杂系统进行投影获取模态系数,应用神经网络方法捕捉长短期时序模态系数分布规律。研究结果表明,预测重构温度场误差在15%范围内,且预测速度相较于数值模拟方法能够提升4个数量级。因此,本研究建立的模型降阶耦合神经网络的预测方法能够用于套管内温度场的快速预测,为其内部热工水力分析提供支撑。The secondary flow region of a casing once-through steam generator involves complex two-phase flow.Although numerical simulation methods can achieve precise simulation calculations,they are slow and time-consuming for multi-condition and transient calculations,and consume significant computational resources.Model reduction is a method that transforms a complex system into an approximately simplified system,enabling rapid calculations while retaining the main characteristics of the original system.This study employs the Proper Orthogonal Decomposition(POD)method to reduce the model of the temperature field inside the heat exchange tubes,capturing the modal coefficients by projecting the original complex system onto a limited number of modes.A neural network method is applied to capture the distribution patterns of shortand long-term time series modal coefficients.The research results indicate that the error in predicting the reconstructed temperature field is within 15%,and the prediction speed is improved by four orders of magnitude compared to numerical simulation methods.Therefore,the prediction method established in this study,which couples model order reduction with neural networks,can be utilized for the rapid prediction of the temperature field within the casing,providing support for internal thermal-hydraulic analysis.

关 键 词：温度场 模型降阶 神经网络 快速预测 

分 类 号：TL334[核科学技术—核技术及应用]