Abstract: With the wide application of fluid mechanics, more and more large-scale fine flow fields have emerged. Overlapping streamlines and dense fields make it hard to use the traditional streamline visualization methods to characterizes the flow fields or process with large-scale fine flow fields. Based on the idea of data-driven, this paper presents an algorithm to implement the characterization of large-scale fine flow fields. The algorithm characterizes streamlines obtained by widely spreading seed points, calculates the features of each point, segments the streamlines based on the features, and then constructs a set of feature vectors and a set of word vectors. Then, the algorithm calculates the geometric feature similarity between streamlines to evaluate streamline similarity and achieves streamlines filtering. Two typical application scenarios, streamline query and flow field compression, verify the proposed method.

Key words: data driven, streamline segmentation, flow field visualization, bag-of-word, large-scale fine flow field