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面向核反应堆数字孪生的数据融合方法综述

宋美琪 陈富坤 刘晓晶

宋美琪, 陈富坤, 刘晓晶. 面向核反应堆数字孪生的数据融合方法综述[J]. 核动力工程. doi: 10.13832/j.jnpe.2024.11.0148
引用本文: 宋美琪, 陈富坤, 刘晓晶. 面向核反应堆数字孪生的数据融合方法综述[J]. 核动力工程. doi: 10.13832/j.jnpe.2024.11.0148
Song Meiqi, Chen Fukun, Liu Xiaojing. An Overview of Data Fusion Methods for the Digital Twin of Nuclear Reactor[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.11.0148
Citation: Song Meiqi, Chen Fukun, Liu Xiaojing. An Overview of Data Fusion Methods for the Digital Twin of Nuclear Reactor[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.11.0148

面向核反应堆数字孪生的数据融合方法综述

doi: 10.13832/j.jnpe.2024.11.0148
基金项目: 国家自然科学基金资助项目(12427811), 中核集团领创科研项目
详细信息
    作者简介:

    宋美琪(1992—),女,助理教授,现主要从事先进核能系统数字孪生,E-mail: songmeiqi@sjtu.edu.cn

    通讯作者:

    刘晓晶,E-mail: xiaojingliu@sjtu.edu.cn

  • 中图分类号: TL31;TP399

An Overview of Data Fusion Methods for the Digital Twin of Nuclear Reactor

  • 摘要: 核反应堆数字孪生的发展有望通过信息物理融合的实现提高核电站的安全性与经济性,而数据融合问题是信息物理融合的核心问题。因此本文面向核反应堆数字孪生领域,从数据融合的定义、融合对象、融合层次、融合方法以及数字孪生与数据融合的关系着手,进而从八个方面探讨了数据融合方法在核反应堆数字孪生的全生命周期中的应用与研究,从数据方面与融合方法方面指出当前研究存在的挑战,能够为未来核反应堆数字孪生发展过程中解决数据融合关键问题提供参考。

     

  • 图  1  数字孪生发展中可能存在的数据融合方式[16]

    Figure  1.  Possible Data Fusion Methods in the Development of DT[16]

    图  2  数字孪生故障诊断、预测与健康管理中的不同对象、不同层次之间的融合框架

    Figure  2.  The Fusion Framework of Different Objects and Levels in DT Fault Diagnostics, Prognostics and Health Management

    图  3  数据融合在数字孪生框架中的应用[24]

    Figure  3.  Application of Data Fusion in DT Framework[24]

    图  4  核反应堆数字孪生系统运行机理

    Figure  4.  Interaction Operation Mechanism of a Real-World Nuclear Reactor and its DT

    图  5  核电厂安全可靠运维框架图[53]

    Figure  5.  Nuclear Power Plant Safe and Reliable Operation and Maintenance Framework[53]

    表  1  数据融合的主要数学模型

    Table  1.   The Main Mathematical Model of Data Fusion

    数据融合主要方法 数学模型
    统计推断[42]  贝叶斯推理、D-S证据理论、随机集理论、支持向量机理论等
    信号处理与估计[43,44]  最小二乘法、三维变分法、四维变分法、卡尔曼滤波类算法、粒子滤波、马尔可夫链蒙特卡罗(MCMC)、
    期望极大化算法(EM)等
    人工智能[45,46]  传统机器学习方法、深度学习方法、迁移学习方法、强化学习方法、基于规则的推理、专家系统等
    下载: 导出CSV
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  • 收稿日期:  2024-11-25
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