Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

Abstract

Accurate prediction and measurement of wall-shear stress dynamics in fluid flows is crucial in domains as diverse as transportation, public utility infrastructure, energy technology and human health. However, we still lack adequate experimental methods that simultaneously capture the temporal and the spatial behaviour of the wall-shear stress. In this contribution, we present a holistic approach that derives these dynamics from particle-image velocimetry (PIV) measurements using a deep optical flow estimator with physical knowledge. While the experimental measurements resemble state-of-the-art PIV set-ups, the established particle image processing is replaced by a deep neural network specifically tailored to extract velocity and wall-shear stress information. Since this WSSflow framework operates at the original image resolution, it provides the respective flow field information at a much higher spatial resolution compared with state-of-the-art PIV processing. The results show that this per-pixel approach is essential for an accurate wall-shear stress estimation. The validity and physical correctness of the derived flow quantities are demonstrated with synthetic and real-world experimental data of a turbulent channel flow, a wavy turbulent channel flow and an elastic blood vessel flow. Where baseline data are available for comparison, the instantaneous and time-averaged wall-shear stress predictions accurately follow the ground truth data.