基于混合驱动降阶模型的中子注量率快速预测方法研究  

Research on Fast Prediction Method of Neutron Flux Based on Hybrid Driven Reduced Order Model

作　　者：赵梓炎 向钊才 赵鹏程[1] Zhao Ziyan;Xiang Zhaocai;Zhao Pengcheng(School of Nuclear Science and Technology,University of South China,Hengyang,Hunan,421001,China)

机构地区：[1]南华大学核科学技术学院,湖南衡阳421001

出　　处：《核动力工程》2024年第4期1-8,共8页Nuclear Power Engineering

基　　金：装备预研教育部联合基金(8091B032243)。

摘　　要：反应堆参数发生扰动后的瞬间,中子注量率和反应堆功率的准确预测对反应堆安全运行至关重要,而现有的本征正交分解(POD)与Galerkin投影相结合的方法存在累积误差而导致精度不高的问题。使用隐式差分法得到一维中子时空扩散的精确解,并作为基准数据,引入2个长短期记忆(LSTM)神经网络项,用于降低POD的累积误差和截断误差,实现物理驱动和数据驱动的混合驱动模型的构建。结果表明,添加神经网络修正项后,对中子注量率、总功率和各阶模态系数预测的均方根误差(RMSE)均降低了1~2个数量级,添加神经网络扩展项后,在预测相同阶数情况下计算时间显著减小,基于2阶和3阶扩展到6阶的改进模型相较于原始6阶模型分别提速了13%和7.6%。混合驱动模型可以很好得改善POD快速预测精度,结果有一定的参考价值。The accurate prediction of neutron flux and reactor power is very important for the safe operation of the reactor immediately after the disturbance of reactor parameters.The traditional method combining POD and Galerkin projection has the problem of low accuracy due to cumulative error.In this study,the implicit difference method is used to obtain the exact solution of one-dimensional neutron spatiotemporal diffusion.As the reference data,two LSTM neural network terms are introduced to eliminate the cumulative error and truncation error of POD,and to build a hybrid drive model driven by physics and data.The results show that the root-mean-square error of neutron flux,total power and each order modal coefficient is reduced by 1-2 orders of magnitude after adding the neural network correction term,and the calculation time is significantly reduced under the same order of prediction when the neural network extension term is added.The improved model based on 2nd and 3rd order scaling to 6th order is 13%and 7.6%faster than the original 6th order model,respectively.The hybrid drive model can improve the rapid prediction accuracy of POD,and the results have certain reference value.

关 键 词：本征正交分解(POD) Galerkin投影 长短期记忆(LSTM)神经网络 降阶模型 中子注量率预测 

分 类 号：TL33[核科学技术—核技术及应用]