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We describe the implementation of a 3d Lagrangian particle tracking (LPT) system based on event-based vision (EBV) sensors and demonstrate its application for the near-wall characterization of a turbulent boundary layer (TBL) in air. The viscous sublayer of the TBL is illuminated by a thin light sheet that grazes the surface of a glass window inserted into the flat wall of the wind tunnel wall. The data simultaneously captured by three synchronized EBV-cameras is used to reconstruct the 3d particle tracks within 0.4 mm of the wall on a field of view of 12.0 mm by 7.5 mm. The velocity and position of particles within the viscous sub-layer permit the estimation of the local, unsteady wall shear stress vector under the assumption of linearity between particle velocity and wall shear stress. Thereby, 2d distributions and higher order statistics of the unsteady wall shear stress are obtained. The systematic underestimation of the spanwise shear stress fluctuation can be explained on the basis of DNS data; a methodology for its mitigation is proposed. The employed EBV hardware coupled with suited LPT algorithms provide data quality on par with currently used, considerably more expensive, high-speed framing cameras.
Willert et al. (Mon,) studied this question.