摘要
静态空间修正因子(SSF)的计算是压水堆动态刻棒的重要环节。本文以国内某M310机组第2循环为目标堆芯,根据换料设计规则,实现了4500个堆芯装载方案的随机生成,并基于CMS程序系统计算了每种方案下的SSF作为神经网络训练的样本集,利用卷积神经网络(CNN)方法建立了堆芯装载方案、落棒步序与SSF之间的映射关系,快速预测SSF。数值实验结果表明:在统计的最后20轮测试集的真实值与预测值的平均相对误差(MRE)中,最大MRE低于2.25%,平均MRE低于1.20%;其预测的机组第2循环实际堆芯装载下的SSF与CMS计算值也具有较好的一致性,表明构建的CNN模型具有良好的预测精度和泛化能力。CNN方法计算SSF具备工程应用的可行性。
Dynamic rod worth measurement(DRWM)stands as the predominant method for control rod worth measurement in low-power physics experiment of commercial pressurized water reactors(PWRs)worldwide,significantly reducing experiment timelines,eliminating borated wastewater,and improving nuclear plant economics.The static spatial correction factor(SSF)calculation is a critical component of this process,where SSF accuracy directly determines the reliability of rod worth measurements.Traditional SSF computation methods,such as Monte Carlo codes and deterministic codes,face challenges in computational complexity,massive data demands,and inefficiency.This study addresses these limitations by integrating deep learning to enhance SSF calculation efficiency and precision.Focusing on the Cycle 2 core of a domestic M310 reactor,an in-depth analysis of refueling design rules was conducted.By refining three fundamental constraints,the sample space was reduced from 1×514 to 87091200,enabling tractable modeling.A Python-based algorithm was developed to randomly generate 4500 core loading configurations,and validated SSF values for each were computed using the square fuel assembly core management system(CMS)code to establish a neural network dataset.Input features(core loading patterns and rod sequences)underwent preprocessing:1)conversion into a 26×1 vector(loading configuration)and a 1×226 vector(rod sequence),merged into a time-sequential matrix;2)normalization to eliminate scale-driven dominance;3)dataset partitioning into training and test sets at an 8∶2 ratio.A convolutional neural network(CNN)model was constructed on the TensorFlow framework to map inputs to SSF outputs.Critical components including activation functions,optimizers and loss functions,were selected through empirical studies.Hyperparameters(convolutional layers,epochs,learning rates,batch sizes)were optimized by evaluating MSE(mean squared error)and MAE(mean absolute error)metrics,defining the final network architecture and training parameters for the CNN-based SSF predictor.Numerical results show that training and testing MSE values decline sharply within 5 epochs,stabilizing near 5.70×10−5,while MRE(mean relative error)stabilizes after 20 epochs,with training MRE primarily<4%and testing MRE concentrated within 0%-2%.Over the final 20 test epochs,the maximum MRE remains below 2.25%,with an average MRE of 1.20%.For the actual Cycle 2 core configuration,all predicted SSF values(226×9 data points across 9 rod groups)show absolute relative deviations<2%against CMS benchmarks.The constructed CNN model demonstrates high prediction accuracy,stability,and generalization capability,indicating the feasibility of applying the CNN method for SSF computation in engineering practice.
作者
赵阶成
喻宏
孙铭泽
石琛
潘翠杰
任培
ZHAO Jiecheng;YU Hong;SUN Mingze;SHI Chen;PAN Cuijie;REN Pei(Department of Reactor Engineering Technology,China Institute of Atomic Energy,Beijing 102413,China;Xi’an Jiaotong University,Xi’an 710049,China)
出处
《原子能科学技术》
北大核心
2025年第12期2660-2668,共9页
Atomic Energy Science and Technology
关键词
动态刻棒
静态空间修正因子
卷积神经网络
泛化能力
dynamic rod worth measurement
static spatial correction factor
convolutional neural network
generalization capability
