聚变堆活化腐蚀产物多尺度可视分析方法研究

doi:10.11996/JG.j.2095-302X.2023030588

图学学报 ›› 2023, Vol. 44 ›› Issue (3): 588-598.DOI: 10.11996/JG.j.2095-302X.2023030588

• 计算机图形学与虚拟现实 • 上一篇下一篇

聚变堆活化腐蚀产物多尺度可视分析方法研究

罗月童¹^,²(), 杨梦男¹, 彭俊¹, 周波¹, 张延孔¹()

1.合肥工业大学计算机与信息学院，安徽合肥 230601
2.安全关键工业测控技术教育部工程研究中心，安徽合肥 230009

收稿日期:2022-07-17 接受日期:2022-12-13 出版日期:2023-06-30 发布日期:2023-06-30
通讯作者: 张延孔(1990-)，男，讲师，硕士。主要研究方向为可视化、可视分析等。E-mail：zhangyankong@hfut.edu.cn
作者简介:
罗月童(1978-)，男，教授，博士。主要研究方向为计算机辅助设计、可视分析。E-mail：ytluo@hfut.edu.cn

Study on multi-scale visual analysis method of activated corrosion products in fusion reactor

LUO Yue-tong¹^,²(), YANG Meng-nan¹, PENG Jun¹, ZHOU Bo¹, ZHANG Yan-kong¹()

1. School of Computer and Information, Hefei University of Technology, Hefei Anhui 230601, China
2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei Anhui 230009, China

Received:2022-07-17 Accepted:2022-12-13 Online:2023-06-30 Published:2023-06-30
Contact: ZHANG Yan-kong (1990-), lecturer, master. His main research interests cover visualization and visual analytics, etc. E-mail：zhangyankong@hfut.edu.cn
About author:
LUO Yue-tong (1978-), professor, Ph.D. His main research interests cover computer aided design, visual analysis. E-mail：ytluo@hfut.edu.cn

摘要/Abstract

摘要：

聚变能被认为是人类的终极清洁能源，而安全是聚变能发展的生命线。聚变堆冷却管道中的活化腐蚀产物是聚变堆的主要放射性源项，对聚变堆的屏蔽设计、人员防护和事故后果等都有重要影响。随着时间的推移，活化腐蚀产物不断累积，研究活化腐蚀产物在冷却管道中的分布特征对聚变安全有重要意义。现阶段，该领域的专家缺乏一种能够快速有效地分析活化腐蚀产物分布特征的方法。考虑到在研究过程中，根据不同的特性对聚变堆冷却管道的各个部分进行了分类，获得了大量的部件，因此，活化腐蚀产物广泛分布在部件中，由Co57、Co60、Mn54等多种放射性物质组成，活化腐蚀产物的组成和分布随着时间的推移而演变，因此活化腐蚀产物数据是多变量时序数据。针对活化腐蚀产物数据的特点和领域专家的分析需求，设计开发了一套可视分析系统，其主要特点包括：①针对冷却管道部件过多的问题，提出基于多层次聚类分析，以快速选出代表性部件进行深入分析，避免逐一查看所有部件；②针对时间跨度大的问题，基于多粒度划分方法自动提取潜在关键时间段，以聚焦关键时间段；③设计一套多视图联动可视分析系统，通过灵活交互快速得到活化腐蚀产物的整体分布特征。根据上述特点，最终设计了一个基于Web的系统，可以对聚变堆活化腐蚀产物进行多尺度、多粒度分析。国际热核聚变实验堆(ITER)是目前世界上最大的国际合作项目，以ITER反应堆2013—2020年活化腐蚀产物数据为例验证该方法的有效性，表明了该工具有助于选出代表性部件和关键时间段，能大幅度提高工作效率。

关键词: 可视分析, 多变量时序数据, 多层次分析, 聚变核安全, 活化腐蚀产物

Abstract:

Fusion energy is regarded as the ultimate clean energy for human use, and safety is paramount for the advancement of fusion energy. Experts in this field must ensure the safety of the entire process from production to usage. Currently, the problem of activated corrosion products in the cooling pipes of fusion reactors poses a significant challenge to safety. These products are the primary sources of radioactivity in fusion reactors and cause safety problems. They have a critical impact on the shielding design, personnel protection, and accident consequences of fusion reactors. Moreover, the activated corrosion products are distributed throughout the cooling pipe and accumulate over time. Therefore, studying their distribution characteristics in cooling pipes is essential for ensuring fusion safety. At this stage, experts in the field lack a method that can quickly and efficiently analyze the distribution characteristics of the activated corrosion products. During the research process, various parts of the cooling pipe of the fusion reactor were classified according to different characteristics, and a large number of components were obtained. Therefore, the activated corrosion products are widely distributed in the components and are composed of various radioactive substances such as Co57, Co60, and Mn54. The composition and distribution of the activated corrosion products evolve over time; therefore, the activated corrosion product data are multivariate time-series data. Considering the characteristics of the activated corrosion product data and the analytical needs of domain experts, a visual analysis system was designed and developed. Its main features include the following: ① To address the problem of too many components of the cooling pipe, it proposed a multi-level clustering analysis approach to divide the components and help domain experts quickly select representative components for in-depth analysis, thereby avoiding checking all components one by one. ② The system addressed the challenge of a large time span by automatically extracting potential critical time periods using the multi-granularity division method, thus enabling domain experts to focus on critical time periods. ③ A set of multi view linkage visual analysis systems was designed to assist domain experts in quickly obtaining the overall distribution characteristics of activated corrosion products through flexible interaction. Based on the above characteristics, a Web-based system that can conduct multi-scale and multi-granularity analyses of fusion reactor activated corrosion products was designed. The International Thermonuclear Experimental Reactor (ITER) is currently the largest international cooperation project in the world. The effectiveness of the method was tested using the activated corrosion product data of the ITER reactor from 2013 to 2020. Domain experts pointed out that the tool is useful in selecting representative components and key time periods and can greatly improve their work efficiency.

Key words: visibility analysis, multivariable time series data, multi-layer analysis, fusion nuclear safety, activated corrosion products

中图分类号:

TP391

罗月童, 杨梦男, 彭俊, 周波, 张延孔. 聚变堆活化腐蚀产物多尺度可视分析方法研究[J]. 图学学报, 2023, 44(3): 588-598.

LUO Yue-tong, YANG Meng-nan, PENG Jun, ZHOU Bo, ZHANG Yan-kong. Study on multi-scale visual analysis method of activated corrosion products in fusion reactor[J]. Journal of Graphics, 2023, 44(3): 588-598.

图/表 14

图1 领域科学家分析活化腐蚀产物流程

Fig. 1 Domain scientists analyze the process of activated corrosion products

图2 数据结构示意图

Fig. 2 Data structure diagram

图3 系统流程图

Fig. 3 System flow chart

图4 系统界面((a)属性配置选择；(b)聚类结果分析；(c)参数面板；(d)部件物理结构展示；(e)部件关联可视化；(f)部件具体时段数据可视化)

Fig. 4 System interface ((a) Attribute configuration selection; (b) Cluster result analysis; (c) Parameter panel; (d) Component physical structure display; (e) Component association visualization; (f) Component specific time period data visualization)

图5 弦图

Fig. 5 Chord diagram

图6 节点图

Fig. 6 Node diagram

图7 多层次分组操作

Fig. 7 Multilevel grouping operation

图8 关键帧序列

Fig. 8 Keyframe sequence

图9 部件搜索

Fig. 9 Components search

图10 多层次分组操作((a)第一次多层次操作；(b)最后一次多层次操作)

Fig. 10 Multilevel grouping operation ((a) The first multilevel operation; (b) Last multilevel operation)

图11 最终聚类结果

Fig. 11 Final clustering results

图12 簇展开图

Fig. 12 Cluster expansion diagram

图13 代表性部件时序数据折线图((a)不同簇对比；(b)同簇对比)

Fig. 13 Line chart of spatiotemporal data of representative components ((a) Comparison of different clusters; (b) Same cluster comparison)

图14 异常部件数据特征

Fig. 14 Abnormal component data characteristics

参考文献 23

[1]	中国国际核聚变能源计划执行中心. “人造太阳”计划: 和平利用聚变能[J]. 国际人才交流, 2019(9): 8-10.
	China International Nuclear Fusion Energy Program Execution Center. “Artificial Sun” project: peaceful utilization of fusion energy[J]. International Talent, 2019(9): 8-10. (in Chinese)
[2]	张竞宇, 李璐, 宋文, 等. 水冷聚变堆活化腐蚀产物源项分析程序开发[J]. 原子能科学技术, 2015, 49(S1): 68-74.
	ZHANG J Y, LI L, SONG W, et al. Program development for source term analysis of activated corrosion product in water-cooled fusion reactor[J]. Atomic Energy Science and Technology, 2015, 49(S1): 68-74. (in Chinese)
[3]	DI PACE L, DACQUAIT F, SCHINDLER P, et al. Development of the PACTITER code and its application to safety analyses of ITER primary cooling water system[J]. Fusion Engineering and Design, 2007, 82(3): 237-247. DOI URL
[4]	ARDITSAS P J. Activation product transport using TRACT: ore estimation of an ITER cooling loop[J]. Fusion Engineering and Design, 1999, 45(2): 169-185. DOI URL
[5]	WU Y C, XIE X, WANG J C, et al. ForVizor: visualizing spatio-temporal team formations in soccer[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 25(1): 65-75 DOI URL
[6]	FUJIWARA T, SHILPIKA, SAKAMOTO N, et al. A visual analytics framework for reviewing multivariate time-series data with dimensionality reduction[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(2): 1601-1611. DOI URL
[7]	CHEN H C, YANG B, LIU J M, et al. Mining spatiotemporal diffusion network: a new framework of active surveillance planning[J]. IEEE Access, 2019, 7: 108458-108473. DOI URL
[8]	WANG X M, CHEN W, XIA J Z, et al. ConceptExplorer: visual analysis of concept drifts in multi-source time-series data[C]// 2020 IEEE Conference on Visual Analytics Science and Technology. New York: IEEE Press, 2021: 1-11.
[9]	AIGNER W, MIKSCH S, SCHUMANN H, et al. Time & time-oriented data[M]//Visualization of Time-Oriented Data. London: Springer London, 2011: 45-68.
[10]	HOCHHEISER H, SHNEIDERMAN B. Dynamic query tools for time series data sets: timebox widgets for interactive exploration[J]. Information Visualization, 2004, 3(1): 1-18. DOI URL
[11]	PENG R D. A method for visualizing multivariate time series data[J]. Journal of Statistical Software, 2008, 25(Code Snippet 1): 1-17.
[12]	PYLVÄNEN M, ÄYRÄMÖ S, KÄRKKÄINEN T. Visualizing time series state changes with prototype based clustering[M]// Adaptive and Natural Computing Algorithms. Heidelberg: Springer, 2009: 619-628.
[13]	WANG T D, PLAISANT C, SHNEIDERMAN B, et al. Temporal summaries: supporting temporal categorical searching, aggregation and comparison[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(6): 1049-1056. DOI PMID
[14]	BACH B, SHI C L, HEULOT N, et al. Time curves: folding time to visualize patterns of temporal evolution in data[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 559-568. DOI URL
[15]	GU Y, WANG C L. TransGraph: hierarchical exploration of transition relationships in time-varying volumetric data[J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(12): 2015-2024. DOI URL
[16]	LIU S X, YIN J L, WANG X T, et al. Online visual analytics of text streams[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(11): 2451-2466. DOI URL
[17]	LIU D Y, XU P P, REN L. TPFlow: progressive partition and multidimensional pattern extraction for large-scale spatio-temporal data analysis[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1): 1-11.
[18]	ADHIKARI R, AGRAWAL R K. A combination of artificial neural network and random walk models for financial time series forecasting[J]. Neural Computing and Applications, 2014, 24(6): 1441-1449. DOI URL
[19]	BERTHOLD M R, HÖPPNER F. On clustering time series using euclidean distance and Pearson correlation[EB/OL]. [2022-02-13]. https://arxiv.org/abs/1601.02213.
[20]	BAI S H, QI H D, XIU N H. Constrained best euclidean distance embedding on a sphere: a matrix optimization approach[J]. SIAM Journal on Optimization, 2015, 25(1): 439-467. DOI URL
[21]	HSU C J, HUANG K S, YANG C B, et al. Flexible dynamic time warping for time series classification[J]. Procedia Computer Science, 2015, 51(1): 2838-2842. DOI URL
[22]	AßFALG J, KRIEGEL H P, KRÖGER P, et al. Similarity search on time series based on threshold queries[M]//Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2006: 276-294.
[23]	DING H, TRAJCEVSKI G, SCHEUERMANN P, et al. Querying and mining of time series data[J]. Proceedings of the VLDB Endowment, 2008, 1(2): 1542-1552. DOI URL

聚变堆活化腐蚀产物多尺度可视分析方法研究

Study on multi-scale visual analysis method of activated corrosion products in fusion reactor

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 23

相关文章 6

编辑推荐

Metrics

本文评价

[1]	马小东, 任芃锟, 赵凡. 起止点数据可视分析研究[J]. 图学学报, 2022, 43(1): 1-10.
[2]	刘丽艳 , 张宏鑫 , 陈为 , 邸奕宁 , 刘嘉信 , 满家巨 . 可视分析增强的平行智能交通系统框架[J]. 图学学报, 2021, 42(3): 485-491.
[3]	张慧军,陈俊杰 . 利用问题求解理论来研究交互式复杂信息的可视分析行为[J]. 图学学报, 2020, 41(3): 325-334.
[4]	章蓉 1，陈谊 1，张梦录 1，孟可欣 2. 高维数据聚类可视分析方法综述[J]. 图学学报, 2020, 41(1): 44-56.
[5]	刘新月 1，谢文军 1，尹成胜 2，陈金光 2，刘璐 1，罗月童 1 . 城市交通事故的局部相关性可视分析方法[J]. 图学学报, 2019, 40(5): 843-851.
[6]	李文芳 1,2，程鑫 3，路强 2,3 . 一种基于图的电力数据可视分析方法[J]. 图学学报, 2019, 40(1): 124-130.