大规模核素扩散数据可视化方法研究与应用

贾亚宁; 郑晓磊; 陈春花; 何桃; 龙鹏程

doi:10.11889/j.1000-3436.2018.rrj.36.050701

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大规模核素扩散数据可视化方法研究与应用

辐射交叉学科研究 | 更新时间：2021-12-10

- 大规模核素扩散数据可视化方法研究与应用
- Research and application of a spatiotemporal visualization method for large-scale nuclide diffusion data
- 辐射研究与辐射工艺学报 2018年36卷第5期
- 作者机构：
  
  1.中国科学院核能安全技术研究所中子输运理论与辐射安全重点实验室合肥 230031
  2.中国科学技术大学合肥 230027
- 作者简介：
  
  [ "贾亚宁, 女, 1993年10月出生, 2016年于东北大学获工学学士学位, 目前为中国科学技术大学硕士研究生在读, 核能科学与工程专业, E-mail: yaning.jia@fds.org.cn", "JIA Yaning (female) was born in October 1993, and obtained her bachelor's degree from Northeastern University in 2016. Now she is a master candidate at University of Science and Technology of China, majoring in nuclear science and engineering. E-mail: yaning.jia@fds.org.cn" ]
  何桃, 博士, E-mail: tao.he@fds.org.cn Ph.D. HE Tao, E-mail: tao.he@fds.org.cn
- 基金信息：
  
  国家磁约束核聚变能发展研究专项项目(2014GB112000);中国科学院信息化项目(XXH13506-104);国家科技部国家科技基础条件平台项目(DKA2017-12-02-17);中国科学院青年创新促进会专项项目;产业化基金资助
- DOI：10.11889/j.1000-3436.2018.rrj.36.050701
  中图分类号：
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贾亚宁, 郑晓磊, 陈春花, 等. 大规模核素扩散数据可视化方法研究与应用[J]. 辐射研究与辐射工艺学报, 2018,36(5):050701-7.

Yaning JIA, Xiaolei ZHENG, Chunhua CHEN, et al. Research and application of a spatiotemporal visualization method for large-scale nuclide diffusion data[J]. Journal of Radiation Research and Radiation Processing, 2018,36(5):050701-7.
贾亚宁, 郑晓磊, 陈春花, 等. 大规模核素扩散数据可视化方法研究与应用[J]. 辐射研究与辐射工艺学报, 2018,36(5):050701-7. DOI： 10.11889/j.1000-3436.2018.rrj.36.050701.

Yaning JIA, Xiaolei ZHENG, Chunhua CHEN, et al. Research and application of a spatiotemporal visualization method for large-scale nuclide diffusion data[J]. Journal of Radiation Research and Radiation Processing, 2018,36(5):050701-7. DOI： 10.11889/j.1000-3436.2018.rrj.36.050701.

摘要

在核应急响应期间，利用可视化手段直观展示大规模核素扩散数据的空间分布，有助于应急指挥人员快速有效地进行科学决策与指挥。本文基于科学计算可视化技术，选用改进的八叉树模型对扩散数据建立空间索引，并根据操作干预水平提取数据水平时空切片，完成了大规模核素扩散数据的可视化。以日本福岛核事故为例，对其大规模核素扩散数据进行分层可视化，直观显示了放射性核素在不同高度的空间分布状态，表明本文方法可为核应急快速响应与决策提供技术支持。

Abstract

During a nuclear emergency response, detailed spatiotemporal distribution visualization helps to make quick and effective scientific decisions to analyze large-scale nuclide diffusion data. Thus, a method based on scientific visualization is developed in this study. An optimized index management method, based on a traditional Octree structure, is adopted to establish spatial indexes for diffusion data and extract horizontal spatiotemporal slices according to the operation intervention levels. Considering the Fukushima nuclear accident in Japan as an example, large-scale radionuclide diffusion data are visualized in layers, so that the spatial distribution of radionuclides at different heights can be visualized. The results show that this method can provide technical support and facilitate decision-making during nuclear emergency response incidents.

关键词

大规模数据处理核素扩散可视化核事故应急

Keywords

Large-scale data processingNuclide diffusionSpatiotemporal visualizationNuclear accident emergency

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