Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements
This addresses the lack of adequate experimental methods for capturing wall-shear stress dynamics, with potential impacts on sustainability and healthcare, though it appears incremental as it builds on existing deep learning and physical modeling techniques.
The paper tackles the problem of accurately predicting instantaneous wall-shear stress dynamics, which is crucial for applications like civil aviation and medical treatments, by presenting a holistic approach that derives velocity and wall-shear stress fields from flow measurements using a deep optical flow estimator with physical knowledge, demonstrating validity with synthetic and real-world data.
Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology, and human health. As a direct measure of the shear-induced friction forces, an accurate prediction of the wall-shear stress can contribute to sustainability, conservation of resources, and carbon neutrality in civil aviation as well as enhanced medical treatment of vascular diseases and cancer. Despite such importance for our modern society, we still lack adequate experimental methods to capture the instantaneous wall-shear stress dynamics. In this contribution, we present a holistic approach that derives velocity and wall-shear stress fields with impressive spatial and temporal resolution from flow measurements using a deep optical flow estimator with physical knowledge. The validity and physical correctness of the derived flow quantities is demonstrated with synthetic and real-world experimental data covering a range of relevant fluid flows.