基于实测数据融合的堆芯物理模型反演优化方法及工业验证研究  

Method Research on Inversion Optimization of Core Physical Model and Industrial Application with Measured Data Fusion

作　　者：郭林 张凯[1] 万承辉 吴宏春[1] GUO Lin;ZHANG Kai;WAN Chenghui;WU Hongchun(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,陕西西安710049

出　　处：《原子能科学技术》2024年第7期1432-1439,共8页Atomic Energy Science and Technology

基　　金：国家重点研发计划(2022YFB1902600)。

摘　　要：由于堆芯运行过程中的组件辐照生长、冷却剂高速冲击等因素,燃料组件不可避免地会出现弯曲现象。但机组运行期间无法直接测量燃料组件弯曲状态,导致数值模拟采用的堆芯物理模型与真实堆芯状态之间存在差异,直观上表现为堆芯功率分布的计算值与实测值存在显著误差。为了提高数值模拟精度,本文开展了基于实测数据融合的堆芯物理模型反演优化方法研究:采用人工神经网络算法,通过大量样本训练建立堆芯物理模型与实测数据物理场之间的显式函数关系;基于三维变分算法和实测数据物理场,建立物理模型反演优化代价函数,通过实测数据反演优化得到与真实状态更为接近的堆芯物理模型。为了实现方法验证,本文利用国内某商用压水堆核电厂的功率分布实测数据对堆芯燃料组件弯曲实现了反演优化。数值结果表明:采用反演优化得到的堆芯物理模型,可将堆芯功率分布计算误差的最大值由13.4%降至7.7%,显著提升了堆芯数值模拟结果的精度。因此,本文提出的基于实测数据融合的堆芯物理模型反演优化方法能够显著提高堆芯数值模拟的精度,在核反应堆数字孪生技术研发中具有重要的应用价值。During the operation of pressurized water reactor(PWR),the fuel assemblies would inevitably occur the phenomenon of bowing,due to the factors such as axial irradiation growth,high speed impact of coolant during operation and so on.This phenomenon is intuitively manifested as the power quadrant tilt,which would influence the safe operation.In order to quantify the impact of fuelassembly bowing on the core key parameters and the safe operation,it is necessary to accurately establish the core physical model and perform simulation for the fuel-assembly bowing.However,the fuel-assembly bowing cannot be measured in real-time during core operation,which leads to discrepancies between the simulated core and the actual core,manifesting as the errors of power distribution between simulation and the measurements.To improve simulation accuracy,an optimal method of core physical model-inversion with the measured data fusion was proposed in this paper.Several steps were involved in this method.Firstly,the dataset would be generated,in which the inputs were different core physical models and the outputs were the samples of model-simulated value that corresponding to the measured data.Secondly,the artificial neural network(ANN)algorithm would be applied to train the dataset,and the functional relationship between the physical model and the modelsimulated value which corresponding the measured data would be established.Thirdly,the threedimensional variational(3DVAR)assimilation algorithm would be adopted to characterize the differences between the actual core physical model and the simulated model by the cost function,and the measured data would be applied to inverse a more realistic core physical model based on the 3DVAR.Finally,the optimized model would be obtained when the minimum value of cost function was solved.To verify the proposed method,the measured data of power distribution from a commercial PWR nuclear power plant(NPP)in China were applied for the inversion optimization of fuel assembly bowing model,in which the fuel

关 键 词：实测数据融合 模型反演优化 三维变分算法 人工神经网络算法 

分 类 号：TL329[核科学技术—核技术及应用]