一种数据驱动的三维流场流线特征化筛选方法

A Data Driven Characteristically Filtering Method for 3D Flow Field

传统的流线可视化方法因视线遮挡和数据密集难以刻画流场特征,难以应对大规模数据,为此,从数据驱动的思路出发,提出了一种筛选三维流线的算法,实现对大规模精细流场的特征刻画.该算法对广泛撒点取得的流线集进行特征化,通过计算流线上各点的特征,并以此为依据对流线进行分段;基于所有分段的几何特征构建一组特征向量,并利用词袋方法建立一组词向量;以词向量为基础计算流线间的几何特征相似度,以评估各个流线间的相似性,实现对流线的筛选.通过在特定流线的查询和整体流线流场的压缩这2个典型应用场景上的应用,检验了该方法对流线筛选的效果.

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.