数值核反应堆大数据及其应用

汪岸; 任帅; 苗雪; 董玲玉; 朱迎; 陈丹丹; 胡长军

doi:10.11959/j.issn.2096-0271.2021048

您当前的位置：

首页 >

文章列表页 >

数值核反应堆大数据及其应用

专题：国产环境下的大数据处理系统 | 更新时间：2024-06-03

- 数值核反应堆大数据及其应用
- Big data of numerical nuclear reactor and its application
- 大数据 2021年7卷第5期页码：2021048
- 作者机构：
- 作者简介：
  
  [ "汪岸（1993- ），男，北京科技大学博士生，主要研究方向为高性能计算、数据挖掘" ]
  [ "任帅（1992- ），男，北京科技大学博士生，主要研究方向为大数据存储与处理、机器学习、数据挖掘" ]
  [ "苗雪（1992- ），女，北京科技大学博士生，主要研究方向为并行与分布式计算、机器学习、多物理场耦合分析" ]
  [ "董玲玉（1996- ），女，北京科技大学博士生，主要研究方向为高性能计算、计算流体力学" ]
  [ "朱迎（1997- ），女，北京科技大学硕士生，主要研究方向为并行与分布式计算、多物理场耦合分析" ]
  [ "陈丹丹（1995- ），女，北京科技大学博士生，主要研究方向为计算材料学、数据挖掘" ]
  [ "胡长军（1963- ），男，北京科技大学终身教授、博士生导师，智能超算融合应用技术教育部工程研究中心主任，主要研究方向为高性能计算、领域数据工程" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U1867217);国家重点研发计划资助项目;The National Key Research and Development Program of China(2017YFB0202303)
- DOI：10.11959/j.issn.2096-0271.2021048
  中图分类号： TP274
- 网络首发：2021-09，
  
  纸质出版：2021-09-15
- 稿件说明：
移动端阅览
汪岸, 任帅, 苗雪, 等. 数值核反应堆大数据及其应用[J]. 大数据, 2021,7(5):2021048.

An WANG, Shuai REN, Xue MIAO, et al. Big data of numerical nuclear reactor and its application[J]. Big data research, 2021, 7(5): 2021048.
汪岸, 任帅, 苗雪, 等. 数值核反应堆大数据及其应用[J]. 大数据, 2021,7(5):2021048. DOI： 10.11959/j.issn.2096-0271.2021048.

An WANG, Shuai REN, Xue MIAO, et al. Big data of numerical nuclear reactor and its application[J]. Big data research, 2021, 7(5): 2021048. DOI： 10.11959/j.issn.2096-0271.2021048.

摘要

数值核反应堆（数值堆）运行过程中涉及的海量数据可被用于优化现有数值堆模型、获取核能领域科学发现、推动数值堆研究。对现有的数据驱动建模和堆内微观现象预测的相关工作进行综述。在此基础上，结合领域特点提出了数值核反应堆大数据的概念，并分析了它作为工业大数据和模拟大数据的重要特征。以中国数值反应堆原型系统（CVR 1.0）为例，从数值堆大数据的多样性、关联性、非精确性等特征出发，运用神经网络、数理统计、数值分析等多学科的技术开展了建模优化和科学发现两个方向的研究工作，证明了数值核反应堆大数据特征对数值堆研究的指导作用。

Abstract

The massive amount of data involved in the operation of numerical nuclear reactor (numerical reactor) can be used to optimize existing numerical reactor models

obtain scientific discoveries in the field of nuclear energy

and promote numerical reactor research.Based on the review of the existing data-driven modeling and the prediction of microscopic phenomena in reactors

the concept of the big data of numerical nuclear reactor was put forward

and its important characteristics as industrial and simulation big data were analyzed according to the characteristics of the field of nuclear energy.Taking China virtual reactor 1.0 (CVR 1.0) as an example

starting from the variety

dependency and inaccuracy of the big data of numerical nuclear reactor

the research work of modeling optimization and scientific discovery was carried out by using the multidisciplinary techniques such as neural network

mathematical statistics and numerical analysis

which illustrates the guiding role of the characteristics of the big data of numerical nuclear reactors in numerical reactor research.

关键词

Keywords

references

邓力 , 史敦福 , 李刚 . 数值反应堆多物理耦合关键技术 [J ] . 计算物理 , 2016 , 33 ( 6 ): 631 - 638 .

DENG L , SHI D F , LI G . Key technologies of coupling for multiphysics in numerical reactor [J ] . Chinese Journal of Computational Physics , 2016 , 33 ( 6 ): 631 - 638 .

杨文 , 胡长军 , 刘天才 , 等 . 数值反应堆及CVR1.0研究进展 [J ] . 原子能科学技术 , 2019 , 53 ( 10 ): 1821 - 1832 .

YANG W , HU C J , LIU T C , et al . Research progress of China virtual reactor (CVR1.0) [J ] . Atomic Energy Science and Technology , 2019 , 53 ( 10 ): 1821 - 1832 .

HADAD K , PIROUZMAND A , AYOOBIAN N . Cellular neural networks (CNN) simulation for the TN approximation of the time dependent neutron transport equation in slab geometry [J ] . Annals of Nuclear Energy , 2008 , 35 ( 12 ): 2313 - 2320 .

LONG Z C , LU Y P , MA X Z , et al . PDENet:learning PDEs from data [C ] // Proceedings of the 35th International Conference on Machine Learning .[S.l.:s.n. ] , 2018 : 3208 - 3216 .

VYAWAHARE V A , ESPINOSA-PAREDES G , DATKHILE G , et al . Artificial neural network approximations of linear fractional neutron models [J ] . Annals of Nuclear Energy , 2018 , 113 : 75 - 88 .

TANO M E , RAGUSA J C . SweepNet:an artificial neural network for radiation transport solves [J ] . Journal of Computational Physics , 2021 , 426 : 109757 .

CHAN-WAI-NAM Q , MIKAEL J , WARIN X . Machine learning for semi linear PDEs [J ] . Journal of Scientific Computing , 2019 , 79 ( 3 ): 1667 - 1712 .

HURÉ C , PHAM H , WARIN X . Some machine learning schemes for highdimensional nonlinear PDEs [J ] . arXiv preprint,2019,arXiv:1902.01599. .

RAISSI M , KARNIADAKIS G E . Hidden physics models:machine learning of nonlinear partial differential equations [J ] . Journal of Computational Physics , 2018 , 357 : 125 - 141 .

DURAISAMY K , IACCARINO G , XIAO H . Turbulence modeling in the age of data [J ] . Annual Review of Fluid Mechanics , 2019 , 51 ( 1 ): 357 - 377 .

LING J L , KURZAWSKI A , TEMPLETON J . Reynolds averaged turbulence modelling using deep neural networks with embedded invariance [J ] . Journal of Fluid Mechanics , 2016 , 807 : 155 - 166 .

TRACEY B D , DURAISAMY K , ALONSO J J . A machine learning strategy to assist turbulence model development [C ] // Proceedings of the 53rd AIAA Aerospace Sciences Meeting . Reston:American Institute of Aeronautics and Astronautics , 2015 .

HUANG K , KRÜGENER M ,, BROWN A , et al . Machine learning-based optimal mesh generation in computational fluid dynamics [J ] . arXiv preprint,2021,arXiv:2102.12923 .

PODRYABINKIN E V , TIKHONOV E V , SHAPEEV A V , et al . Accelerating crystal structure prediction by machinelearning interatomic potentials with active learning [J ] . Physical Review B , 2019 , 99 ( 6 ): 064114 .

PILANIA G , WANG C C , JIANG X , et al . Accelerating materials property predictions using machine learning [J ] . Scientific Reports , 2013 , 99 , 2810 .

JIA W L , WANG H , CHEN M H , et al . Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning [J ] . arXiv preprint,2020,arXiv:2005.00223 .

ALBARQI M , ALSULAMI R , GRAHAM J . Automated data processing of neutron depth profiling spectra using an artificial neural network [J ] . Nuclear Instruments and Methods in Physics Research Section A:Accelerators,Spectrometers,Detectors and Associated Equipment , 2020 , 953 : 163217 .

CAO C L , GAN Q , SONG J , et al . A twostep neutron spectrum unfolding method for fission reactors based on artificial neural network [J ] . Annals of Nuclear Energy , 2020 , 139 : 107219 .

UPADHYAYA B R , ERYUREK E . Application of neural networks for sensor validation and plant monitoring [J ] . Nuclear Technology , 1992 , 97 ( 2 ): 170 - 176 .

ZAVALJEVSKI N , GROSS K C . Support vector machines for nuclear reactor state estimation [R ] . 2000 .

陈涵瀛 , 高璞珍 , 谭思超 . 摇摆流动不稳定性的遗传算法优化神经网络预测 [J ] . 原子能科学技术 , 2015 , 49 ( 2 ): 273 - 278 .

CHEN H Y , GAO P Z , TAN S C . Prediction of flow instability under rolling motion based on neural network optimized by genetic algorithm [J ] . Atomic Energy Science and Technology , 2015 , 49 ( 2 ): 273 - 278 .

张大林 , 陈宇彤 , 梁禹 , 等 . 一种基于机器学习的棒束子通道热工水力特性预测方法:CN111081400A [P ] .2020-04-28.

ZHANG D L , CHEN Y T , LIANG Y , et al . Rod bundle sub-channel thermal hydraulic characteristic prediction method based on machine learning:CN111081400A [P ] .2020-04-28.

GÓMEZ-BOMBARELLI R , ASPURUGUZIK A . Machine learning and bigdata in computational chemistry [M ] // Handbook of materials modeling . Cham : Springer International Publishing , 2018 : 1 - 24 .

BUTLER K T , DAVIES D W , CARTWRIGHT H , et al . Machine learning for molecular and materials science [J ] . Nature , 2018 , 559 ( 7715 ): 547 - 555 .

GASPAROTTO P , CERIOTTI M . Recognizing molecular patterns by machine learning:an agnostic structural definition of the hydrogen bond [J ] . The Journal of Chemical Physics , 2014 , 141 ( 17 ): 174110 .

ISAYEV O , FOURCHES D , MURATOV E N , et al . Materials cartography:representing and mining materials space using structural and electronic fingerprints [J ] . Chemistry of Materials , 2015 , 27 ( 3 ): 735 - 743 .

CERIOTTI M . Unsupervised machine learning in atomistic simulations,between predictions and understanding [J ] . The Journal of Chemical Physics , 2019 , 150 ( 15 ): 150901 .

TORDA A E , VAN GUNSTEREN W F . Algorithms for clustering molecular dynamics configurations [J ] . Journal of Computational Chemistry , 1994 , 15 ( 12 ): 1331 - 1340 .

KARPEN M E , TOBIAS D J , BROOKS C L . Statistical clustering techniques for the analysis of long molecular dynamics trajectories:analysis of 2.2-ns trajectories of YPGDV [J ] . Biochemistry , 1993 , 32 ( 2 ): 412 - 420 .

HILL C . Summary report of the 6th biennial technical meeting,in:international atomic and molecular code centres network meeting on database services for radiation damage in nuclear materials [R ] . 2019 .

BHARDWAJ U , SAND A E , WARRIER M . Classification of clusters in collision cascades [J ] . Computational Materials Science , 2020 , 172 : 109364 .

BHARDWAJ U , SAND A E , WARRIER M . Graph theory based approach to characterize self interstitial defect morphology [J ] . Computational Materials Science , 2021 , 195 : 110474 .

STOLLER R E . Primary radiation damage formation [M ] // Comprehensive nuclear materials . Amsterdam : Elsevier , 2012 : 293 - 332 .

VON TOUSSAINT U , DOMÍNGUEZGUTIÉRREZ F J , COMPOSTELLA M , et al . FaVAD:a software workflow for characterization and visualizing of defects in crystalline structures [J ] . Computer Physics Communications , 2021 , 262 : 107816 .

GORYAEVA A M , LAPOINTE C , DAI C , et al . Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores [J ] . Nature Communications , 2020 , 11 ( 1 ): 4691 .

HINSEN K . The approximation tower in computational science:why testing scientific software is difficult [J ] . Computing in Science ＆ Engineering , 2015 , 17 ( 4 ): 72 - 77 .

单浩栋 , 徐李 , 胡赟 , 等 . 三维高保真特征线程序ANT-MOC的开发与测试 [J ] . 核动力工程 , 2020 , 41 ( S1 ): 1 - 5 .

SHAN H D , XU L , HU Y , et al . Development and verification of three-dimensional MOC code ANT-MOC [J ] . Nuclear Power Engineering , 2020 , 41 ( S1 ): 1 - 5 .

TAKEDA T , IKEDA H . 3-D neutron transport benchmarks [J ] . Journal of Nuclear Science and Technology , 1991 , 28 ( 7 ): 656 - 669 .

张雷 , 许磊 , 杜云涛 , 等 . 一种基于三维R树的时空数据的存储及检索和更新方法:CN110532255A [P ] .2019-12-03.

ZHANG L , XU L , DU Y T , et al . Threedimensional R tree-based spatio-temporal data storage,retrieval and updating method:CN110532255A [P ] .2019-12-03.

汪学锋 , 李锋 , 周炜 , 等 . 流固耦合网格插值方法研究 [J ] . 船舶力学 , 2009 , 13 ( 4 ): 571 - 578 .

WANG X F , LI F , ZHOU W , et al . Research on grid interpolation method of fluidstructure coupling [J ] . Journal of Ship Mechanics , 2009 , 13 ( 4 ): 571 - 578 .

MONGE A E , ELKAN C . An efficient domain-independent algorithm for detecting approximately duplicate database records [C ] // Proceedings of the Workshop on Research Issues on Data Mining and Knowledge Discovery .[S.l.:s.n. ] , 1997 .

ETIENNE A , HERNÁNDEZ-MAYORAL M , GENEVOIS C , et al . Dislocation loop evolution under ion irradiation in austenitic stainless steels [J ] . Journal of Nuclear Materials , 2010 , 400 ( 1 ): 56 - 63 .

AUGER P , PAREIGE P , AKAMATSU M , et al . APFIM investigation of clustering in neutron-irradiated FeCu alloys and pressure vessel steels [J ] . Journal of Nuclear Materials , 1995 , 225 : 225 - 230 .

浏览量

590

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于湖仓一体技术的“多湖一中台”工业大数据平台研究与应用实践

图灵指数——学术大数据下的跨领域跨年代学者影响力评估

工业大数据分析综述：模型与算法

结合深度学习的工业大数据应用研究

混凝土泵送机械大数据挖掘与应用