计算机工程与应用 ›› 2025, Vol. 61 ›› Issue (12): 291-298.DOI: 10.3778/j.issn.1002-8331.2403-0326

• 图形图像处理 • 上一篇    下一篇

基于信息熵和椭圆采样布置种子点的表面流线可视化

项人和,陈永辉,杨超,张晓蓉,黄政斌   

  1. 1.西南科技大学 计算机科学与技术学院,四川 绵阳 621000 
    2.中国空气动力研究与发展中心 计算空气动力研究所,四川 绵阳 621000
    3.空气动力学国家重点实验室,四川 绵阳 621000
  • 出版日期:2025-06-15 发布日期:2025-06-13

Information Entropy and Ellipse Sampling-Based Seeding Method for Surface Streamline Visualization

XIANG Renhe, CHEN Yonghui, YANG Chao, ZHANG Xiaorong, HUANG Zhengbin   

  1. 1.School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan 621000, China
    2.Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, Sichuan 621000, China
    3.State Key Laboratory of Aerodynamics, Mianyang, Sichuan 621000, China
  • Online:2025-06-15 Published:2025-06-13

摘要: 表面流线可视化是流场可视化的一个重要研究分支,流线生成的效果很大程度上取决于种子点的布置方法。针对实际流场数据类型复杂多样,传统基于信息熵的种子点放置方法只适用规则网格,无法充分体现流场的物理特性和曲线网格的结构特点的问题,提出了一种基于信息熵和泊松椭圆采样法的种子点放置方法。基于局部网格密度和泊松椭圆采样法计算网格点的影响范围。选择能够体现显著流场特征的信息熵局部最大值点作为初始种子点,根据网格点的影响范围,补充选择互不影响的网格点作为新的种子点,以全面地刻画表面流场的整体态势。实验结果表明,该方法生成的流线可以更清晰地表达表面流场的关键特征与全局信息。

关键词: 表面流线可视化, 种子点, 信息熵, 泊松椭圆采样

Abstract: Surface streamline visualization is one of the important research objects of flow field visualization, and the effectiveness of streamline generation largely depends on the selection of seed points. In response to the complex and diverse types of flow field data, conventional seed placement techniques based on information entropy only use regular grids, which cannot fully capture the physical attributes of the flow field and the structural characteristics of the curved grids. Therefore, an improved seeding method based on information entropy and Poisson ellipse sampling is proposed. Firstly, the influence range of grid points are computed using local grid density and Poisson ellipse sampling. Then, the initial seed points are chosen as the local maximum point of information entropy that reflect the significant flow field characteristics. Finally, according to the influence range of the grid points, the additional seed points that do not affect each other are selected, in order to comprehensively depict the overall situation of the surface flow field. Experiments have proved that the generated streamlines using this method can better express the key features and global information of the surface flow field.

Key words: surface streamline visualization, seed placement, information entropy, Poisson ellipse sampling