We describe the implementation of a 3d Lagrangian particle tracking (LPT) system based on event-based vision (EBV) 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 thin glass window inserted into the wind tunnel wall. The data simultaneously captured by three synchronized event cameras are used to reconstruct the 3d particle tracks within \(400\,\upmu \text{m}\) of the wall on a field of view of \(12.0\,\text{mm} \times 7.5\,\text{mm}\) . The velocity and position of particles within the viscous sublayer permit the estimation of the local vector of the unsteady wall shear stress (WSS) under the assumption of linearity between particle velocity and WSS. Thereby, time-evolving maps of the unsteady WSS and higher-order statistics are obtained that are in agreement with DNS data at matching Reynolds number. Near-wall particle acceleration provides the rate of change of the WSS which exhibits fully symmetric log-normal superstatistics. Two-point correlations of the randomly spaced WSS data are obtained by a bin-averaging approach and reveal information on the spacing of near-wall streaks. The employed compact EBV hardware coupled with suited LPT tracking algorithms provides data quality on par with currently used, considerably more expensive, high-speed framing cameras.
