摘要
降阶模型(ROM)通过将全阶守恒方程映射至低阶子空间或构建数据驱动的代理模型,有效降低了物理模型的复杂性。相比传统的计算流体动力学(CFD)仿真,ROM在大规模仿真计算中计算效率更高。本文利用本征正交分解(POD)结合机器学习(ML),提出了一种适用于瞬态工况的ROM框架,并以此实现棒束子通道内质量流量参数瞬态预测。针对POD和ML不同方式结合的预测方法进行对比,结果显示长短期记忆神经网络(LSTM)+POD方法更适合短期预测,而在长期预测时POD+LSTM方法误差更小,可为未来进行其他复杂系统的预测提供方案。
The Reduced Order Model(ROM)effectively reduces the complexity of physical models by mapping full-order conservation equations to lower-order subspaces or building datadriven surrogate models.Compared with traditional computational fluid dynamics(CFD)simulation,ROM is more efficient in large-scale simulation.In this paper,a ROM framework is proposed by combining Proper Orthogonal Decomposition(POD)with machine learning(ML)to predict mass flow parameters in rod bundle subchannels.Comparison of prediction methods for different ways of combining POD and ML shows that the LSTM+POD method is more suitable for short-term prediction,while the POD+LSTM method has less error in long-term prediction,which can provide a solution for making predictions of other complex systems in the future.
作者
许宇杰
莫锦泓
董晓朦
刘永
徐安琪
于洋
Xu Yujie;Mo Jinhong;Dong Xiaomeng;Liu Yong;Xu Anqi;Yu Yang(Shenzhen Key Laboratory of Nuclear and Radiation Safety,College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen,Guangdong,518060,China;Institute for Advanced Study in Nuclear Energy and Safety,College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen,Guangdong,518060,China;National Key Laboratory of Parallel and Distributed Computing,National University of Defense Technology,Changsha,410073,China;Nuclear Power Institute of China,Chengdu,610213,China)
出处
《核动力工程》
北大核心
2025年第2期177-185,共9页
Nuclear Power Engineering
基金
广东省基础与应用基础研究基金面上项目(2023A1515011977)
深圳市优秀科技创新人才项目(RCBS20221008093119044)
四川省自然科学基金(青年科学基金)项目(2023NSFSC1321)。
