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.

Acoustic liners, passive devices to mitigate engine noise, operate under high-speed grazing flow and grazing acoustic waves. To investigate the complex physics governing this interaction, high-fidelity numerical simulations of a spatially evolving turbulent boundary layer grazing a multi-orifice acoustic liner at a bulk Mach number of 0.32 are performed. The simulations replicate conditions from a reference experiment. Grazing tonal plane acoustic waves with amplitudes of 130 dB and 145 dB and propagating in the same direction and the direction opposite to the mean flow are analyzed. The results show that the boundary layer displacement thickness doubles in the presence of the liner and its growth rate is affected by the amplitude and propagation direction of the acoustic wave. The acoustic liner also promotes the formation of an outer hump in both the logarithmic region of the streamwise and wall-normal velocity variance, with these effects becoming more pronounced under acoustic forcing. Furthermore, impedance estimation, using Dean’s method, reveals that near-wall flow modifications, quantified through the displacement thickness, influence the local value of the computed impedance.

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.

Turbulent separating and reattaching flows are known to exhibit low-frequency fluctuations manifested in a large-scale contraction and expansion of the reverse-flow region. Previous experimental investigations have been restricted to planar measurements, while the computational cost to resolve the low-frequency spectrum with high-fidelity simulations currently appears to be unaffordable. In this article, we make use of volumetric measurements to reveal the low-frequency dynamics of a turbulent separation bubble (TSB) formed in the fully turbulent flow past a smooth backward-facing ramp. The volumetric velocity field measurements cover the entire separated flow region over a domain with a spanwise extent of. Spectral proper orthogonal decomposition (SPOD) of the velocity fluctuations reveals low-rank low-frequency behaviour at Strouhal numbers, which was also observed in previous planar measurements. However, in contrast with the interpretation of a two-dimensional contraction/expansion motion, the low-frequency dynamics is shown to be inherently three-dimensional, and governed by large elongated structures with a spanwise wavelength of approximately. A low-order model constructed with the leading SPOD mode confirms substantial changes of the TSB extent in the centre plane, linking it to the modal pattern that is strongly non-uniform in the spanwise direction. The findings presented in this study promote a more complete understanding of the low-frequency dynamics in turbulent separated flows, thereby enabling novel modelling and control approaches.

Measuring the velocity field around a complex object by volumetric PIV is hindered by shadow formation (illumination) as well as camera occlusion (imaging). These have been recently dealt with by multiplying illumination and imaging directions (redundancy) and by the integration of ray-tracing techniques to include the effect of visual blockage caused by the object (object-aware particle reconstruction, Wieneke & Rockstroh, 2024). The problem of light reflections blinding regions of the images has not been afforded yet. The latter pertains to interactions between illumination and imaging through the object surface and it poses additional challenges to ghost particle formation, particle detection and tracking in general. This study proposes a method to effectively detect such regions, and measures to modify the particle triangulation algorithm.
The viability of this novel reflection-aware Lagragian particle tracking (RA-LPT) approach is examined by application to two experiments of varying complexity. The first case is the flow around a stationary wall-mounted cube as imaged with a redundant number of cameras. The second experiment tackles an elite runner sprinting across the measurement region obtained with the Ring-of-Fire technique. A considerable reduction of ghost particles (false positives) is attained, while the formation of voids (false negatives) is also minimized. The overall result of the method maximizes the measurement region around and in proximity of the object of interest.

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.

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.

In this study, the macroscopic properties of kerosene-H₂ blended flames are investigated in a multi-phase, multi-fuel combustor, focusing on the effects of increasing H₂ blending fractions. The non-reacting flow field of the swirl-stabilized combustor is characterized using PIV, and macro-structures in the flow and spray-swirl interactions are analyzed. Kerosene atomizers are tested to estimate variations in spray quality across different fuel blends. The changes in the optical properties of the flames are recorded using broadband chemiluminescence imaging while the changes in the acoustic emissions are recorded using a microphone. Results show that H₂ addition significantly alters the flame topology, transitioning from a lobed flame for pure kerosene to a single contiguous swirling flame for blended or pure H₂ cases. The flame luminosity decreases, with the emission color shifting from bright yellow (pure kerosene case) to dull yellow (multi-fuel cases) to a red-blue hue (pure H₂ case). These changes are attributed to variations in fuel distribution, heat release patterns, combustion mode, flame speed, and soot formation tendencies. The acoustic analysis reveals that a strong tonal behavior is observed under pure fuel conditions (prominent peaks at higher harmonics of 150 Hz) while broadband characteristics are exhibited under blended fuel conditions. The overall acoustic emissions in multi-fuel cases are reduced by ~80% compared to pure H₂ and ~55% compared to pure kerosene. This study highlights the effects of high levels of H₂ blending on flame dynamics and acoustic behavior in a multi-phase, multi-fuel combustor, offering valuable insights for the development of fuel-agnostic combustion systems.

This computational study investigates the impact of manufacturing inaccuracies of face sheet orifice geometries on acoustic liners’ impedance and flow dynamics. Normal Impedance Tube (NIT) lattice-Boltzmann very-large eddy simulations at 130 and 145 dB and 800, 1400, and 2000 Hz reveal that sharp-edged geometries present increased acoustic resistance and absorption than geometries with smoother edges. Rounded and double-chamfered edge shapes, mimicking real-world imperfections, reduce the resistance component of impedance by up to 28%, thus reducing the absorption coefficient. The inspection of the velocity field shows the flow features that cause these differences. Results demonstrate that minor edge imperfections, potentially due to manufacturing, may alter liner performance. This underscores the need to account for geometric imperfections in industrial design and quality control.

With symmetric rotors, tip vortex helices develop regularly before interacting, following the leapfrogging instability. This instability can occur earlier when the helices are radially offset by using blades of different lengths. This study investigates the spatio-temporal development of near-wake behavior for rotors with a significant blade length difference. Large eddy simulations with an actuator line model were conducted on a modified NREL 5MW wind turbine under both laminar and turbulent inflow conditions, to evaluate the impact of blade length differences ranging from 5 to 30 %. The study analyzed the development of tip vortex helices, the onset of leapfrogging, vortex merging, and, ultimately, their three-dimensional breakdown. The analysis is corroborated using a simplified two-dimensional point vortex model. The results show that the leapfrogging process begins immediately downstream of the vortex release when blades of different lengths are considered. The instability growth rate obtained from the 2D vortex model agrees with the LES results. Although the rotor asymmetry accelerates the leapfrogging and, in some conditions, also the vortex merging process, it proves insufficient to cause a large-scale breakdown of the helix system and, therefore, enhance wake recovery. Inflow turbulence, however, plays a larger role in wake recovery, promoting the breakdown of tip helical vortices regardless of rotor symmetry.

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.

Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here regularized by the Navier-Stokes equations. It is shown that the assimilation yields a higher spatial resolution, and the process remains robust, even at low particle densities ( 𝑝𝑝𝑝 < 0.001). This is achieved by enforcing compliance with the governing equations, thus leveraging the spatiotemporal evolution of the measured flow field. The results indicate that the PINN reconstructs unambiguously velocity, vorticity and pressure fields with a level of detail not attainable with conventional methods (binning) or more advanced data assimilation techniques (vortex-in-cell). The results of this article support the findings of Clark di Leoni (2023) suggesting that the PINN methodology is inherently suited to the assimilation of PTV data, in particular under conditions of severe sparsity or during experiments with limited control of seeding concentration.

Recording multiple exposures of the tracer particles onto a single digital image has the potential to simplify the hardware needed for 3D PTV measurements. A sequence with equal time intervals leads to the well-known directional ambiguity problem. Instead, a sequence with irregular time-intervals (two or more) allows resolving the ambiguity. Two fundamental criteria are introduced that determine whether a set of particle samples corresponds to a physical trace for a given neighborhood: the kinematic similarity criterion imposes similarity (i.e. proportionality) between the length travelled by the particle and time elapsed. Additionally, the trace regularity criterion favors sets with minimum fluctuations of the velocity vector. A numerical algorithm, based on combinatorial analysis is presented, whereby all possible sets are scrutinized and the set of particles yielding the minimum disparity from the above criteria corresponds to the physical trace. The algorithm is demonstrated using 3D experimental data and results are benchmarked against the state-of-the-art time-resolved analysis Shakethe-Box (single-frame, single-exposure).

As of present the Urban Air Mobility market has been dominated by fully electric aircraft. However, hydrogen vehicles have remained relatively undeveloped in this segment, also because hydrogen poses additional design complexities and uncertainties concerning crashworthiness, fuel cell cooling, and low volumetric density. Nevertheless, hydrogen might yield advantages in mission performance owing to its superior gravimetric energy density and greater sustainability when compared to batteries. In this paper, the design procedure of a four-passenger long-range hydrogen eVTOL using Multidisciplinary Analysis and Design Optimization (MADO) is presented. Using MADO, the mission energy of the eVTOL was minimized while abiding by the constraints rooting from the use of hydrogen. Based on this design, the conclusion can be made that the implementation of hydrogen eVTOLs in urban air mobility is feasible whilst taking into account constraints resulting from the use of hydrogen at the preliminary design stage. This led to an aircraft which excels at longer range due to the increased scalability of hydrogen fuel, but having a weight penalty due to auxiliary equipment which hampers its performance and results in a large fuselage and maximum takeoff weight.

Image based 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), whereby the information of the occluded lines of sight can be incorporated into the particle tracking algorithm. The approach, however, necessitates of 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 (ICP) 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. Three operating regimes are studied and compared: monolithic, partitioned and object-aware (OA) monolithic. The results indicate that object registration enables a correct reconstruction of particle tracers and strongly reduces the domain clipping typical of the monolithic approach. Furthermore, the dynamical use of all views in the OA monolithic method offers clear benefits compared to the partitioned approach, namely a lower occurrence of ghost particles. 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 PIV data assimilation at the fluid-solid interface.

The scalability of experiments using PIV relies upon several parameters, namely illumination power, camera sensor and primarily the tracers light scattering capability. Given their larger cross section, helium-filled soap bubbles (HFSB) allow measurements in air flows over a significantly large domain compared to traditional oil or fog droplets. Controlling their diameter translates into scalability of the experiment. This work presents a technique to extend the control of HFSB diameter by geometrical variations of the generator. The latter expands the more limited range allowed by varying the relative helium-air mass flow rates. A theoretical model predicts the bubble size and production rate, which is verified experimentally by high-speed shadow visualization. The overall range of HFSB produced in a stable (bubbling) regime varies from 0.16 to 2.7 mm. Imaging by light scattering of such tracers is also investigated, in view of controversies in the literature on whether diffraction or geometrical imaging dominate the imaging regime. The light scattered by scaled HFSB tracers is imaged with a high-speed camera orthogonal to the illumination. Both the total energy collected on the sensor for a single tracer, as well as its peak intensity, are found to preserve scaling with the square of the diameter at object magnification of 10–1 or below, typical of PIV experiments. For large-scale volumetric applications, it is shown that varying the bubble diameter allows increasing both the measurement domain as well as the working distance of the imagers at 10 m and beyond. A scaling rule is proposed for the latter.

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) spatial locations. When performing robotic volumetric Particle Image Velocimetry (PIV) (Jux et al., 2018), the large-scale mean velocity fields are estimated merging several measurements performed at different (adjacent) locations covered by the robot sequence. The proposed methodology leverages the definition of POD modes as eigenvectors of the spatial correlation matrix to obtain also large-scale modes. Performing measurements over overlapping (50-75%) regions allows to approximate the correlation matrix in the two adjacent domains. When applied over a sequence of views, this method has the potential to deliver full-domain POD modes spanning the volume covered by the robot sequence, even if different regions are covered asynchronously. This methodology is particularly well-suited for applications that seek to investigate large-scale flow structures, whenever the dynamic spatial range (DSR) of the measurement system does not allow to capture the whole domain at once. The methodology is validated using a 2D experimental dataset of a turbulent boundary layer, where patches are artificially created from splitting the PIV measurements, later used as ground truth to assess the results. Furthermore, we apply the technique to a 3D robotic volumetric PIV experiment of the flow around a wall-mounted cube.

Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here regularized by the Navier-Stokes equations. It is shown that the assimilation remains robust even at low particle densities ( ppp < 10 − 3 ) where the mean particle distance is larger than 10% of the outlet diameter. This is achieved by enforcing compliance with the governing equations, thereby leveraging the spatiotemporal evolution of the measured flow field. Thus, the PINN reconstructs unambiguously velocity, vorticity, and pressure fields, enabling a robust identification of vortex structures with a level of detail not attainable with conventional methods (binning) or more advanced data assimilation techniques (vortex-in-cell). The results of this article suggest that the PINN methodology is inherently suited to the assimilation of PTV data, in particular under conditions of severe data sparsity encountered in experiments with limited control of the seeding concentration and/or distribution.

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.