Application of clustering and the Hungarian algorithm to the problem of consistent vortex tracking in incompressible flowfields

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.