关键词: 流场数据聚类, 流面, K-means, 科学可视化

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