基于多视图聚类算法的三维流场关键点附近的流线筛选

Streamline Selection around Critical Points of 3D Flow Fields by the Multi-View Clustering

在关键点周围进行流线可视化时,流场特征复杂多样以及流线之间可能存在共点或对称性等情况,可能导致常规的基于几何或相似性的筛选方法失效.为此,提出了基于数据驱动思想的关键点周围的流线筛选方法MvCcp,它是基于多视图聚类算法的流线筛选方法,通过对流场进行不同粒度的体素化,生成流线的位置视图和基于距离场直方图的特征视图数据,并通过多视图聚类算法进行流线筛选.针对HalfCylinder等6个典型的关键点周围的三维流场,与其他3种典型的流线筛选方法进行了定性可视化比较,并基于MSE,PSNR,SSIM,AAD等定量指标进行对比实验表明,MvCcp在所有实验中具有更出色和更稳定的表现.

When visualizing streamlines around critical points,the complex and diverse characteristics of the flow field and the possible existence of common points or symmetries among streamlines may lead to the failure of conventional geometric or similarity-based selection methods.Therefore,a data-driven streamline selection method around critical points,MvCcp,is proposed.It is a method based on multi-view clustering algorithm.By voxelizing the flow field with different granularity,the location distribution view and the geometric feature view data based on the histogram of their distance fields are generated,and the streamlines are selected by the multi-view clustering algorithm.The qualitative visualization of the 3D flow field around six typical critical points such as HalfCylinder was compared with three other typical selection methods,and the comparison experiments based on quantitative metrics such as MSE,PSNR,SSIM,AAD showed that MvCcp had more excellent and more stable performance in all experimental 3D flow fields.