Visualizations of turbulent boundary layers show an abundance of characteristic arcshaped structures whose apparent similarity suggests a common origin in a coherent dynamic process. While the structures have been likened to the hairpin vortices observed in the late stages of transitional flow, a consistent description of the underlying mechanism has remained elusive. Detailed studies of coherent turbulent processes are complicated by the chaotic nature of the flow which modulates each manifestation and renders the isolation of representative samples a challenging task.
The present study harnesses methods developed in the field of computer vision to identify and analyze turbulent flow processes. The algorithm employs morphological operations to distill the topology of the turbulent flow field into a discrete graph. The low-dimensional structural information is stored in a database and enables the identification and analysis of equivalent dynamical processes across multiple scales. Application of the scheme to direct simulations data allows for the first time the time-resolved sampling of the turbulent hairpin process. The analysis attributes the hairpin process to an inviscid instability mechanism related to the inflection points introduced by turbulent streaks.