Abstract
The ability to track vortices spatially and temporally is of great interest for the study of complex and turbulent flows. A methodology to solve the problem of vortex tracking by the application of machine learning approaches is investigated. First a well-known vortex detection algorithm is applied to identify coherent structures. Hierarchical clustering is then conducted followed by a unique application of the Hungarian assignment algorithm. Application to a synthetic flowfield of merging Batchelor vortices results in robust vortex labelling even in a vortex merging event. A robotic PIV experimental dataset of a canonical Ahmed body is used to demonstrate the applicability of the method to three-dimensional flows.Graphic abstract
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