Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (4): 44-53.

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

• Special Issue: Visualization and Visual Analysis • Previous Articles     Next Articles

Streamline Parallel Distribution Method Based on Vector Complete Information Entropy

SHEN Liming1(),GUO Yumeng2(),WANG Wenke1,*()   

  1. 1. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, Hunan 410003, China
    2. Academy of Military Political Work, Academy of Military Sciences, Beijing 100091, China
  • Received:2021-06-10 Online:2021-08-20 Published:2021-08-30
  • Contact: WANG Wenke;;


[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