Identification and Characterization of Hairpin Vortices in Turbulent Channel Flow using Contour Tree-Based Segmentation

Accurate identification and characterization of coherent vortical structures is crucial for advancing our understanding of turbulence dynamics. In this study, we employ a contour tree (CT) based segmentation method enhanced with a layering procedure and vorticity line augmentation to effectively separate intertwined vortices in turbulent flow and identify individual vortices. We apply this method to analyze the hairpin vortices in the direct numerical simulation turbulent channel flow dataset from the Johns Hopkins Turbulence Database (JHTDB). With the individual vortical structures identified and extracted from the turbulent flow field, we quantify various statistics of the hairpin vortices (e.g., intensity, height, length, width, etc.) as a function of the normal distance to the wall boundary. We also utilize the geometric information of individual hairpin vortices to reduce the false sampling of non-hairpin vortices in the conventional conditional average approaches.