Recent advances in particle image velocimetry (PIV) have taken it far beyond its original role as a two-dimensional velocity measurement technique. The combination of improved hardware, algorithmic innovations and interdisciplinary synergies positions PIV as a highly flexible and powerful tool supporting modern aerospace science applications. In this article, important current developments will be presented in detail to demonstrate their capabilities for volumetric measurement, time-resolved data sequences, large-scale applications, Lagrangian particle tracking, overcoming optical access restrictions, incorporation of machine-learning technologies, and adoption of emerging camera technologies.

A method is proposed to obtain full-domain spatial modes based on proper orthogonal decomposition (POD) of particle image velocimetry (PIV) measurements taken at different (overlapping) spatial locations. This situation occurs when large domains are covered by multiple non-simultaneous measurements and yet the large-scale flow field organization is to be captured. The proposed methodology leverages the definition of POD spatial modes as eigenvectors of the spatial correlation matrix, where local measurements, even when not obtained simultaneously, provide each a portion of the latter, which is then analyzed to synthesize the full-domain spatial modes. The measurement domain coverage is found to require regions overlapping by 50–75% to yield a smooth distribution of the modes. The procedure identifies structures twice as large as each measurement patch. The technique, referred to as Patch POD, is applied to planar PIV data of a submerged jet flow where the effect of patching is simulated by splitting the original PIV data. Patch POD is then extended to 3D robotic measurement around a wall-mounted cube. The results show that the patching technique enables global modal analysis over a domain covered with a multitude of non-simultaneous measurements.

Recording onto a single-frame multiple exposures of the tracer particles has the potential to simplify the hardware needed for 3D PTV measurements, especially when dealing with high-speed flows. The analysis of such recordings, however, is challenged by the unknown time tag of each particle exposure, alongside their unknown organization into physical trajectories (trajectory tag). Using a sequence of two or more illumination pulses with a constant time separation leads to the well-known directional ambiguity problem, whereby it is not possible to distinguish the direction of motion of the tracer particles. Instead, an irregular and asymmetric sequence of time separation for the illumination pulses allows recognizing the time tag of the unique sequence of positions in the image, composing the trace. A criterion is formulated here that recognizes unambiguously the trace pattern, based upon the principle of kinematic similarity. A combinatorial algorithm is proposed whereby a signal-to-noise ratio is introduced for every candidate trace. The approach is combined with an additional criterion that favors trace regularity (minimum velocity fluctuations). The algorithm is illustrated making use of particle motion examples. Furthermore, it is assessed using 3D experimental data produced with time-resolved analysis (single-frame, single-exposure) using the Shake-the-Box method. Traces with a three-pulse sequence yield a detection rate of 85%. The latter declines with the number of pulses. Conversely, the error rate rapidly vanishes with the samples number, which confirms the reliability of trace detection criterion when more pulses are comprised in the sequence.

Leading-edge protuberances on airfoils have been shown to soften the onset of aerodynamic stall and to increase lift in the post-stall regime. The present study examines the effect of tubercles during dynamic stall. Pitching airfoils with tubercles of different amplitudes are studied by wind-tunnel experiments, where the three-dimensional time-resolved velocity field is determined using large-scale particle-tracking velocimetry. Computational fluid dynamics simulations are carried out that complement the experimental observations providing pressure distribution and aerodynamic forces. The dynamic stall is dominated by a vortex formed at the leading edge; we characterize the vorticity, circulation, and advection path of this dynamic-stall vortex (DSV). The presence of the tubercles profoundly modifies the boundary layer from the leading edge. The roll-up of the vorticity sheet is significantly delayed compared to a conventional airfoil, resulting in a weaker DSV. The vortex formation is shifted downstream, with the overall effect of a weaker and shorter lift overshoot, in turn enabling a quicker transition to deep stall. Regions of flow separation (stall cells) are visibly compartmentalized with a stable spacing of two tubercles wavelengths.

A semi-analytical model is proposed that incorporates aerodynamic interactions between the rotor-and winginduced flowfields. Predictions are validated through experiments performed with an array of five rotors above an airfoil, where the angle of attack, advance ratio, and chordwise rotor position are varied. At moderate angles of attack, the propulsive thrust is reduced due to the acceleration induced by the wing’s circulation. Around the stall angle of the isolated wing, the rotors re-energize the boundary layer when operated in low-thrust conditions. By increasing the thrust, a pronounced region of reverse flow between the rotors and wing adversely affects the leadingedge separation delay over the wing that occurs for lower thrust settings. However, in this condition, the wing–rotorarray system exhibits increased thrust compared to the attached flow condition due to the rotors ingesting low-momentum flow. In addition, the rotor-induced flow over the wing augments suction, while the pressure side is subjected to a pressure increase, ascribed to flow entrainment from the rotors. After comparison with the experimental observations, it is confirmed that the model predictions accurately describe the lift and thrust performance trends, aside from a discrepancy in the lift force when the rotors are operated in low-thrust conditions.

Image based three-dimensional (3D) particle tracking is currently the most widely used technique for volumetric velocity measurements. Inspecting the flow-field around an object is however, hampered by the latter, obstructing the view across it. In this study, the problem of measurement limitations due to the above is addressed. The present work builds upon the recent proposal from Wieneke and Rockstroh (2024 Meas. Sci. Technol. 35 055303), whereby the information of the occluded lines of sight can be incorporated into the particle tracking algorithm. The approach, however, necessitates methods that accurately evaluate the shape and position of the object within the measurement domain. Methods of object marking and the following 3D registration of a digital object model (CAD) are discussed. For the latter, the iterative closest point registration algorithm is adopted. The accuracy of object registration is evaluated by means of experiments, where marking approaches that include physical and optically projected markers are discussed and compared. Three objects with growing level of geometrical complexity are considered: a cube, a truncated wing and a scaled model of a sport cyclist. The registered CAD representations of the physical objects are included in aerodynamic experiments, and the flow field is measured by means of large-scale particle tracking using helium filled soap bubbles. Results indicate that object registration enables a correct reconstruction of flow tracers within regions otherwise affected by domain clipping as a consequence of obstructed camera lines-of-sight. Finally, the combined visualization of the object and the surrounding flow pattern offers means of insightful data inspection and interpretation, along with posing a basis for particle image velocimetry data assimilation at the fluid-solid interface.

We investigate the impact of a single miniature Helmholtz resonator on wall-bounded turbulence using time-resolved planar particle image velocimetry. A particular aim is to explain the mechanism by which a resonator alters the turbulent velocity fluctuations of different scales. A grazing flow configuration is studied in which the resonator is embedded in the wall beneath a turbulent boundary layer at a friction Reynolds number of Reτ≈2300; the resonator is designed so that its resonance frequency matches the peak frequency of the wall-pressure spectrum. It is found that the resonator amplifies velocity fluctuations near its resonance frequency, while it attenuates the energy of subresonance scales. Underlying mechanisms responsible for these changes in energy are discussed in view of the resonator's local impedance condition. It is posited that large-scale velocity fluctuations in the wall-normal velocity, at temporal frequencies below resonance, are subject to a phase-opposed wall-normal velocity perturbation when the TBL flow convects over the resonator's orifice. This yields a decrease of large-scale energy in u′u′¯,-u′v′¯, and v′v′¯. In addition, modifications of the wall-shear stress field downstream of the resonator are addressed. Insights from this research will contribute to the development of surface designs for passive skin-friction control using arrays of miniature resonators.

A method to reconstruct the dense velocity field from relatively sparse particle tracks is introduced. The approach leverages the properties of proper orthogonal decomposition (POD), and it iteratively reconstructs the detailed spatial modes from a first, coarse estimation thereof. The initially coarse Cartesian representation of the velocity field is obtained by local data averaging, where POD is applied. The spatial resolution of the POD modes is enhanced by reprojecting them onto the sparse particle velocity to iteratively improve the reconstruction of the temporal coefficients. Finally, the enhanced velocity field is represented at high-resolution with a reduced order model using the dominant POD modes. The method is referred to as iterative modal reconstruction (IMR), as an extension of the recently proposed data-enhanced particle tracking velocimetry algorithm, introduced for cross correlation-based velocity data. Experiments in the wake of a cylinder at R e D = 27 000 are used to assess the suitability of the method to resolve the turbulent Kármán-Benard wake. The approach is benchmarked against traditional as well as state-of-the-art reconstruction methods, illustrating the capability of IMR of enhancing the spatial resolution of sparse velocity data.