Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (12): 264-270.DOI: 10.3778/j.issn.1002-8331.1612-0424

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Automatic stream surface seeding and constructing algorithm based on clustering

TANG Ye, XIE Lijun, GUI Liye, HE Lisha, ZHENG Yao   

  1. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
  • Online:2018-06-15 Published:2018-07-03


唐  烨,解利军,桂立业,何丽莎,郑  耀   

  1. 浙江大学 航空航天学院,杭州 310027

Abstract: Surface-based visualization is an important branch of scientific visualization, and plays an important role in the field of aerospace. However, classical methods of stream-surface visualization require users to place seed lines manually, which means that seed lines are difficult to be placed at interesting domain of flow data. A novel method which can place stream-surfaces automatically is presented in this paper. Firstly, the flow field is partitioned according to the similarity of the velocity gradient, curvature and position. Then one seed line is placed in each partition which starts from the center of the partition and grows along the direction of the velocity curvature until it arrives the boundary of its partition. Finally, stream-surfaces are constructed with the advancing front method starting from each seed line with Hermite interpolation.  The experiment has shown that stream surfaces constructed automatically through this method can present main flow structures in most cases. The generated seed lines can also be taken as reference when users want to place seed lines manually for more detailed representation.

Key words: clustering of flow data, stream surface, K-means, scientific visualization

摘要: 基于流面的流场可视化方法是科学数据可视化的重要分支,在航空航天等领域有着重要的应用。但现有的可视化方法需要人工在流场中布置种子线,往往难以布置在有代表性区域,不能生成表现力丰富的流面。提出一种基于聚类的种子线自动布线及流面生成法,通过对流场中速度场的曲率、梯度以及坐标位置间的比较,量化两点间相似度,随后进行聚类实现流场分区。然后在每个分区中心布置种子线。最后积分生成流面。实验显示,该方法生成的流面能表达流场的主要流动结构,其布线结果可以直接使用,也可以作为进一步精细分析时人工布线位置的参考。

关键词: 流场数据聚类, 流面, K-means, 科学可视化