A clustering-based approach to vortex extraction

Since vortex is an important flow structure and has significant influence on numerous industrial applications, vortex extraction is always a research hotspot in flow visualization. This paper presents a novel vortex extraction method by employing a machine learning clustering algorithm to identify and locate vortical structures in complex flow fields. Specifically, the proposed approach firstly chooses an objective, physically based metric that describes the vortex-like behavior of intricate flow and then normalizes the metric for applying on different flow fields. After that, it performs the clustering on normalized metric to automatically determine vortex regions. Our method requires relatively few flow variables as inputs, making it suitable for vortex extraction in large-scale datasets. Moreover, this approach detects all vortices in the flow simultaneously, thereby showing great potential for automated vortex tracking. Extensive experimental results demonstrate the efficiency and accuracy of our proposed method in comparison with existing approaches. Graphic