Streamline Parallel Distribution Method Based on Vector Complete Information Entropy

doi:10.11871/jfdc.issn.2096-742X.2021.04.004

Abstract

Abstract:

[Objective] Streamline visualization depends on the streamline distribution method, which plays an important role in understanding flow field data. The existing streamline distribution methods only consider the directional component of the vector field and ignore magnitude information. In addition, considering that the flow field data are steadily on the increase, a parallel streamline distribution method based on complete vector information entropy is proposed in this paper. [Methods] The proposed method considers both direction and magnitude components of the vector and calculates the entropy field in parallel in blocks to guide the placement of streamlines. An improved pruning method is applied to improve the efficiency of the algorithm. [Results] In this paper, different numbers of streamlines are generated to conduct comparative experiments with existing methods on three flow field datasets. The experimental results show that the proposed method can generate streamlines that reflect the information of both direction and magnitude of the flow field efficiently. [Conclusions] The parallel streamline distribution method proposed in this paper can effectively reveal more flow field information without missing the significant characteristics of the flow field.

Key words: streamline visualization, streamline distribution, complete vector information entropy

SHEN Liming,GUO Yumeng,WANG Wenke. Streamline Parallel Distribution Method Based on Vector Complete Information Entropy[J]. Frontiers of Data and Computing, 2021, 3(4): 44-53.

Figures/Tables 9

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References 12

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	生成所有流线后剪枝	已有流线限制新流线生成
流线数量	24	24
运行时间	74.562 s	68.145 s

	生成所有流线后剪枝	已有流线限制新流线生成
流线数量	21	21
运行时间	11.091 s	9.945 s

	串行	2线程	4线程	6线程
数据集1	9.945 s	7.75 s	6.436 s	5.903 s
数据集2	68.145 s	54.931 s	44.623 s	40.976 s
数据集3	186.935 s	133.084 s	121.296 s	115.564 s