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Journal of Visualization

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28-03-2020 | Regular Paper

A clustering-based approach to vortex extraction

Authors: Liang Deng, Yueqing Wang, Cheng Chen, Yang Liu, Fang Wang, Jie Liu

Published in: Journal of Visualization | Issue 3/2020

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Abstract

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.

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Title: A clustering-based approach to vortex extraction
Authors: Liang Deng
Yueqing Wang
Cheng Chen
Yang Liu
Fang Wang
Jie Liu
Publication date: 28-03-2020
Publisher: Springer Berlin Heidelberg
Published in: Journal of Visualization / Issue 3/2020
Print ISSN: 1343-8875
Electronic ISSN: 1875-8975
DOI: https://doi.org/10.1007/s12650-020-00636-z

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