Characterization of coherent vortical structures in boundary layer turbulence based in progressive structure identification and extraction

Coherent vortical structures (e.g., streamwise, spanwise, and horseshoe vortices) play an important role in the dynamics of boundary layer turbulence. Characterizing these vortices can be challenging due to the considerable variations among different vortices. In this work, we have developed a toolset for identification and extraction of individual coherent structures. We first extract vortical regions as unstructured grids with a region-growing strategy based on certain vortex identification criteria (e.g., the λ₂ method), and then separate those vortices with the help of progressive extraction of (λ₂) iso-surfaces in a top-down fashion. This leads to a hierarchical tree representing the spatial proximity and merging relation of vortices. After separating individual vortices, we further extract their centerlines using mean curvature skeletons to obtain their geometric information such as shape and orientation. Based on both physical attributes (e.g., vorticity, enstrophy, velocity, etc.) and geometric information, we categorize these vortices into their corresponding types and quantify their statistics. We demonstrate this toolset by applying it to analyze the Channel Flow DNS datasets in the Johns Hopkins Turbulence Database (JHTDB).