Deep-learning-based assessment of skin friction in wall-bounded turbulence

This study examines the role of classically coherent structures in wall-bounded turbulence using DNS data and explainable deep learning (XDL). In turbulent channel flow, sweeps—regions of low streamwise velocity moving toward the wall—emerge as the structures most strongly associated with both energy dissipation and wall-shear stress. Interestingly, their volume lies within a narrow range, allowing for more targeted identification of the most impactful events. These findings lay the foundation for efficient, structure-based turbulence-control strategies aimed at drag reduction. In a second phase of the work, we are directly analyzing which flow regions contribute most significantly to skin-friction generation, advancing toward localized flow manipulation.