Uncovering Dynamically Significant Coherent Structures in Wing Turbulence through Explainable Deep Learning

This study applies explainable deep learning (XDL) to uncover dynamically significant coherent structures in the turbulent boundary layer on the suction side of a NACA 4412 wing section subjected to strong adverse pressure gradients (APG). A short-horizon U-Net surrogate is trained using high-resolution large-eddy simulation (LES) data, and gradient-SHAP feature attribution is utilized to quantify the local contribution of flow features to the short-time evolution of velocity fluctuations. Unlike classical kinematic detectors, this approach isolates regions based strictly on their importance for reconstructing future events. Comparisons with Q events, streaks and vortex clusters indicate that SHAP-identified structures provide a more complete description of the turbulent flow than traditional definitions: they correspond primarily to ejection events near the wall but evolve into broad, complex outer-layer structures under strong APG, encompassing multiple classical features. In contrast, vortex clusters are shown to possess marginal dynamical importance. These findings demonstrate that gradient SHAP provides a physically interpretable, data-driven perspective on non-equilibrium turbulence, offering a robust alternative to standard structure-identification methods for aerodynamic applications.