一种数据驱动的三维流场流线特征化筛选方法

doi:10.13190/j.jbupt.2019-152

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (6): 91-97.doi: 10.13190/j.jbupt.2019-152

一种数据驱动的三维流场流线特征化筛选方法

熊光正¹, 黄智濒¹, 戴志涛¹, 杨武兵²

1. 北京邮电大学智能通信软件与多媒体北京市重点实验室, 北京 100876;
2. 中国航天空气动力技术研究院, 北京 100074

收稿日期:2019-07-10 出版日期:2019-12-28 发布日期:2019-11-15
通讯作者: 黄智濒(1978-),男,讲师,E-mail:huangzb@bupt.edu.cn. E-mail:huangzb@bupt.edu.cn
作者简介:熊光正(1999-),男,硕士生.
基金资助:
2019年中央基本业务费项目（RC201958）

A Data Driven Characteristically Filtering Method for 3D Flow Field

XIONG Guang-zheng¹, HUANG Zhi-bin¹, DAI Zhi-tao¹, YANG Wu-bing²

1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing 100876, China;
2. China Academy of Aerospace Aerodynamics, Beijing 100074, China

Received:2019-07-10 Online:2019-12-28 Published:2019-11-15

摘要/Abstract

摘要： 传统的流线可视化方法因视线遮挡和数据密集难以刻画流场特征，难以应对大规模数据，为此，从数据驱动的思路出发，提出了一种筛选三维流线的算法，实现对大规模精细流场的特征刻画.该算法对广泛撒点取得的流线集进行特征化，通过计算流线上各点的特征，并以此为依据对流线进行分段；基于所有分段的几何特征构建一组特征向量，并利用词袋方法建立一组词向量；以词向量为基础计算流线间的几何特征相似度，以评估各个流线间的相似性，实现对流线的筛选.通过在特定流线的查询和整体流线流场的压缩这2个典型应用场景上的应用，检验了该方法对流线筛选的效果.

关键词: 数据驱动, 流线分段, 流场可视化, 词袋, 大规模精细流场

Abstract: With the wide application of fluid mechanics, more and more large-scale fine flow fields have emerged. Overlapping streamlines and dense fields make it hard to use the traditional streamline visualization methods to characterizes the flow fields or process with large-scale fine flow fields. Based on the idea of data-driven, this paper presents an algorithm to implement the characterization of large-scale fine flow fields. The algorithm characterizes streamlines obtained by widely spreading seed points, calculates the features of each point, segments the streamlines based on the features, and then constructs a set of feature vectors and a set of word vectors. Then, the algorithm calculates the geometric feature similarity between streamlines to evaluate streamline similarity and achieves streamlines filtering. Two typical application scenarios, streamline query and flow field compression, verify the proposed method.

Key words: data driven, streamline segmentation, flow field visualization, bag-of-word, large-scale fine flow field

中图分类号:

TP391.41

熊光正, 黄智濒, 戴志涛, 杨武兵. 一种数据驱动的三维流场流线特征化筛选方法[J]. 北京邮电大学学报, 2019, 42(6): 91-97.

XIONG Guang-zheng, HUANG Zhi-bin, DAI Zhi-tao, YANG Wu-bing. A Data Driven Characteristically Filtering Method for 3D Flow Field[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 91-97.

参考文献

[1] McLoughlin T, Laramee R S, Peikert R, et al. Over two decades of integration-based, geometric flow visualization[J]. Computer Graphics Forum, 2010, 29(6):1807-1829.
[2] Ye Xiangong, Kao David, Pang Alex. Strategy for seeding 3D streamlines[C]//IEEE Visualization. Minneapolis:[s.n.], 2005:471-478.
[3] Edmunds M, Laramee R S, Malki R, et al. Automatic stream surface seeding:a feature centered approach[J]. Computer Graphics Forum, 2012, 31(3pt2):1095-1104.
[4] 郭雨蒙, 王文珂, 李思昆. 基于相似度引导的流线种子点并行分布方法[J]. 系统仿真学报, 2014, 26(9):2155-2159. Guo Yumeng, Wang Wenke, Li Sikun. Parallel strategy of streamline seeding with similarity-guiding[J]. Journal of System Simulation, 2014, 26(9):2155-2159.
[5] Marchesin S, Chen C K, Ho C, et al. View-dependent streamlines for 3D vector fields[J]. IEEE Transactions on Visualization and Computer Graphics, 2010, 16(6):1578-1586.
[6] 李春鑫, 彭认灿, 高占胜, 等. 一种改进的三维流场数据可视化方法[J]. 武汉大学学报(信息科学版), 2017, 42(6):744-748. Li Chunxin, Peng Rencan, Gao Zhansheng, et al. An improved legible method for visualizing 3D flow field[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6):744-748.
[7] Li Yifei, Wang Chaoli, Shene C K. Extracting flow features via supervised streamline segmentation[J]. Computers & Graphics, 2015(52):79-92.
[8] Yu H, Wang C, Shene C K, et al. Hierarchical streamline bundles for visualizing 2D flow fields[C]//IEEE VisWeek 2010 Posters. Salt Lake City:[s.n.], 2010.
[9] Rossl C, Theisel H. Streamline embedding for 3D vector field exploration[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(3):407-420.
[10] Liu Shiguang, Song Hange. Streamline querying based on finite substructures[J]. Journal of Visualization, 2019, 22(3):571-585.
[11] Li Yifei. Extracting flow features using bag-of-features and supervised learning techniques[D]. Michigan:Open Access Dissertation, Michigan Technological University, 2015.
[12] Han Jun, Tao Jun, Zheng Hao, et al. Flow field reduction via reconstructing vector data from 3D streamlines using deep learning[J]. IEEE Computer Graphics and Applications, 2019, 39(4):54-67.
[13] Wang Zhongjie, Martinez Esturo J, Seidel H P, et al. Pattern search in flows based on similarity of stream line segments[C]//Proc Vision, Modeling and Visualization. Darmstadt, Germany:[s.n.], 2014:23-30.
[14] Li Yifei, Wang Chaoli, Shene C K. Streamline similarity analysis using bag-of-features[C]//Proc SPIE 9017, Visualization and Data Analysis 2014. San Francisco, California:[s.n.], 2014:90170N.
[15] Tang Bin. Parallel visualization of flow field based on streamline similarity[J]. International Journal of Performability Engineering, 2018, 14(4):717-728.
[16] Han Jun, Tao Jun, Zheng Hao, et al. Flow field reduction via reconstructing vector data from 3D streamlines using deep learning[J]. IEEE Computer Graphics and Applications, 2019, 39(4):54-67.
[17] Han Jun, Tao Jun, Wang Chaoli. FlowNet:a deep learning framework for clustering and selection of streamlines and stream surfaces[EB/OL]. 2018(2018-11-12)[2019-05-23]. https://doi.org/10.1109/TVCG.2018.2880207.

一种数据驱动的三维流场流线特征化筛选方法

A Data Driven Characteristically Filtering Method for 3D Flow Field

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	张雅斓班晓娟董子瑞. 面向单目视频的非牛顿流体仿真重建[J]. 北京邮电大学学报, 2023, 46(2): 109-115.
[2]	陈明, 王树鹏, 云晓春, 吴广君. 基于二维云模型过滤的重复图像发现[J]. 北京邮电大学学报, 2014, 37(s1): 60-65.