Data-Driven Scaling of Turbulent Drag Response to Streamwise-Periodic Wall Transpiration

Modern advances in metamaterials and near-surface actuation strategies offer promising opportunities to reduce turbulent drag on bodies. A key obstacle to enabling these technologies is an understanding of what surface actuation is beneficial to drag reduction. In that vein, this study investigates changes in turbulent drag in channel flow through time-harmonic, streamwise-periodic wall-normal blowing and suction for varying actuation amplitude, wavenumber, and frequency. Direct numerical simulations reveal that such actuation is able to reduce skin friction under specific parameter combinations. We identify a scaling law for the drag response using data-driven analysis, which highlights that the complex dependence on a high-dimensional control parameter space can be reduced to a subset of behaviorally meaningful dimensionless parameters. This enables preliminary estimation of the change in turbulent drag without costly simulations. We also perform flow structure analysis, which highlights changes in near-wall turbulence consistent with the drag-altering behavior.