%A SHEN Liming,GUO Yumeng,WANG Wenke %T Streamline Parallel Distribution Method Based on Vector Complete Information Entropy %0 Journal Article %D 2021 %J Frontiers of Data and Computing %R 10.11871/jfdc.issn.2096-742X.2021.04.004 %P 44-53 %V 3 %N 4 %U {http://www.jfdc.cnic.cn/CN/abstract/article_148.shtml} %8 2021-08-20 %X

[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.