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Machine learning-enhanced PIV for analyzing microfiber-wall turbulence interactions
Summary
Researchers used a machine learning-enhanced imaging technique to track how a single plastic microfiber spins and moves inside a fast-moving turbulent water channel, finding that the fiber aligns and rotates more when caught in swirling vortex structures. This detailed understanding of fiber behavior in turbulent flow is key to predicting how microplastic fibers travel and spread in rivers and waterways.
A machine learning-based approach, RAFT-PIV, is used to measure with single-pixel resolution the flow field around a microplastic fiber in a turbulent channel flow at a Shear Reynolds number of 1000. The results reveal the interaction of the fiber with a hairpin vortex. The fiber rotation rate is correlated with slip velocity distributions along the fiber length, demonstrating higher rotation rates with increased slip velocity gradients. The fiber’s alignment with the spanwise direction during its trajectory is explained through its progressive alignment with the head of a hairpin vortex, characterized by the swirling strength, shear strain rate, and local flow velocity. Higher fiber rotation rates were found likelier in the presence of a vortical structure. These findings highlight the potential of machine learning-enhanced PIV techniques to deepen our understanding of fiber-turbulence interactions, essential for applications such as microplastic pollution mitigation. • Machine learning enhances the resolution of Particle Image Velocimetry measurements. • The flow field around fibers can be reconstructed with single-pixel resolution. • Machine learning reconstructs typical topological features of wall turbulence. • High fiber rotation rates are likelier when a vortical structure is present nearby.