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Christian J Kähler, Stefano Discetti and Ivan Marusic 1 Institute of Fluid Mechanics and Aerodynamics, Universität der Bunderwehr München, 85577 Neubiberg, Germany 2 Aerospace Engineering Research Group, Universidad Carlos III de Madrid, Leganés, Spain 3 Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia E-mail: [email protected] The term ‘Particle Image Velocimetry’ (PIV) made its appearance for the first time in the literature in 1984 [1]. Since then, PIV has become a robust and versatile technique for quantitative investigations in fluid mechanics, being often the method of choice for characterization of flow phenomena. In the last decades, efforts have been directed towards extending the reach of PIV in several directions, including (though not limited to): improving accuracy and robustness, enhancing its versatility; expanding the range of applications (from microfluidics to large-scale flows, spanning very-low-speed to hypersonic regimes); quantifying the measurement uncertainty; increasing the dimensionality of the observation space (e.g. aiming towards a full 3D description, measuring derived quantities such as pressure and forces, etc). The International Symposium on PIV has been established since 1999 as the reference dissemination event for the flourishing scientific debate of the ever-growing PIV community. The latest symposium of this series, ISPIV 2019, was held in Munich (Germany) on 22–24 July 2019, and featured 178 lectures over four parallel sessions. This special section on the 13th International Symposium on PIV collects 15 selected contributions from the conference, sampling the breadth of current research areas on PIV. Particle tracking velocimetry is one of the most active lines of research in the PIV community. Ehlers et al [2] proposed an improvement of the FlowFit method for Lagrangian particle tracking data, feeding it with additional artificial Lagrangian tracers (virtual particles), advected according to velocity and acceleration provided within the data-assimilation framework of FlowFit. Rossi and Barnkob [3] developed a robust and efficient algorithm for generalized defocusing particle tracking, based on a fast, segmentation-free particle 3D location identification procedure, which makes the algorithm suitable for automatized applications. Saredi et al [4] introduced a multi-∆t method based on Reynolds decomposition to combine sets of dual-frame images with different pulse separation, thus exploiting the higher precision of large-∆t frames and the higher robustness of images with small ∆t. The rising popularity of data-mining and machine-learning techniques is opening interesting new scenarios for processing and analysis of PIV data. König et al [5] tested a three-stage cascaded convolutional neural network to measure volumetric velocity fields in microfluidics with astigmatic particle tracking velocimetry. Mendez et al [6] reported an application of a novel data-driven decomposition method for time-resolved PIV data, which combines multi-resolution analysis with standard proper orthogonal decompositions, and does not require assumptions of linear dynamics or stationary data. Pan et al [7] developed a data-assimilation algorithm to estimate the friction velocity and the wall-shear stress in turbulent boundary layers; the proposed method, based on an unscented Kalman filter, fuses velocity profiles, measured with stereo-PIV, Preston tube and MEMS shear stress data, including information on their corresponding uncertainty.