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

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

宋美琪^{1, 3,},
陈富坤^{1, 3},
刘晓晶^{2, 3, ,}

1.
上海交通大学国家电投智慧能源创新学院，上海，200240
2.
上海交通大学核科学与工程学院，上海，200240
3.
上海市数值反应堆技术融合创新中心，上海，200240

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
计量
- 文章访问数: 14
- HTML全文浏览量: 6
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2024-11-25
- 修回日期: 2024-12-19
- 网络出版日期: 2025-01-16

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

Song Meiqi^{1, 3
,},
Chen Fukun^{1, 3},
Liu Xiaojing^{2, 3
, ,}

1.
College of Smart Energy, Shanghai Jiao Tong University, Shanghai, 200240, China
2.
School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
3.
Shanghai Digital Nuclear Reactor Technology Integration Innovation Center, Shanghai Jiao Tong University, Shanghai, 200240, China

摘要

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。
- 数字孪生 /
- 核反应堆 /
- 数据融合 /
- 数据同化 /
- 人工智能
Abstract: The development of digital twin of nuclear reactor has the potential to enhance the security and financial viability of nuclear energy generation facilities by achieving a cyber-physical fusion, while the key challenge of cyber-physical fusion is data fusion problem. In this study, the concept of data fusion is introduced at first, including a definition, the fusion object, the fusion level, the fusion method, and the relationship between digital twin and data fusion. Subsequently, the application and research status of data fusion methods in the entire life cycle of digital twin in nuclear reactor are discussed from eight perspectives. In conclusion, the limitations and challenges inherent in current research has been identified, particularly concerning data sources and the data fusion methods employed. It is anticipated that this research will provide suggestions for the resolution of significant challenges associated with data fusion in the development of digital twin for nuclear reactor.
- Digital twin /
- Nuclear reactor /
- Data fusion /
- Data assimilation /
- Artificial intelligence

HTML全文

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]

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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]

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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]

下载: 全尺寸图片幻灯片

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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