During wind turbine installation or idling, the blades often operate at large angles of attack, where vortex-induced vibration (VIV) can occur. This study experimentally investigates the aerodynamic characteristics of a plunging NACA0021 airfoil at a fixed angle of attack of 90∘ and amplitude of one chord length, focusing on vortex dynamics, lock-in effect, and unsteady force generation. Phase-locked particle image velocimetry (PIV) was conducted at two reduced frequencies of 0.19 and 0.38. At the lower reduced frequency, asymmetric vortex shedding prevents synchronization between shedding and plunge motion frequencies, whereas at the higher reduced frequency, lock-in occurs with periodic shedding of separated leading- and trailing-edge vortices. Compared with previously studied surging motion under identical conditions, plunging requires a higher frequency to achieve lock-in and produces weaker wakes that break down more quickly downstream. Additionally, the aerodynamic load is extracted from the PIV flow field. For the plunging motion, the aerodynamic loads are dominated by pressure forces, with a maximum streamwise coefficient of approximately four times the static value at 90∘ angle of attack. This contrasts with the surging motion, where higher force variations are observed, and both pressure and mean momentum convection play comparable roles in the overall force. These results indicate that lock-in behavior depends strongly on both motion frequency and kinematics, where the effective angle of attack variation and the resulting vortex dynamics also determine whether synchronization can occur.

Yaw engineering models are commonly used as add-ons to the industrial blade element momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression （SR） approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds-averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5-MW wind turbine, covering a range of yaw angles ((Formula presented.)) and thrust coefficients ((Formula presented.)) over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade-off between accuracy and complexity, with complexity constrained to remain comparable with Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared with the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the nonrestoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce.

Particle-laden turbulent flows occur in various natural and industrial processes, highlighting the need for a comprehensive understanding of the interaction between the dispersed phase and the carrier flow. This study investigates the fluid dynamics of a simplified car side-view mirror model, experimentally analyzed using the particle tracking velocimetry technique in the laboratories of Delft University of Technology. Experimental measurements are used to validate a computational fluid dynamics model developed in the OpenFOAM environment. The overall objective of the research is to establish a benchmark for validating Eulerian–Lagrangian numerical models in terms of both flow dynamics and particle trajectories. In this study, the validation is limited to the Eulerian fields; validation of the discrete-phase modeling will be addressed in future work.

The aerodynamics of the multi-rotor system with lifting-devices (MRSL), an innovative concept of wind energy harvesting machine, is preliminary investigated using Large Eddy Simulation (LES) with actuator techniques. In the current setup, turbulent inflow conditions are considered, but inflow wind shear is excluded. Consistent with previous studies, the results demonstrate faster wake recovery of the MRSL compared to its conventional counterpart, namely the wind turbine system without the lifting-devices. Additionally, a set of high-fidelity simulations further reveals that the enhanced wake recovery is robust under both laminar and turbulent inflow conditions, remaining largely unaffected by variations in the ambient turbulence level. The present work provides proof-of-concept evidence that the effectiveness of MRSLs is not significantly hindered by ambient turbulence, motivating future research to evaluate their performance within a realistic atmospheric boundary layer.

This study presents the experimental validation of regenerative wind farms (RGWFs), a novel wind farm concept designed to enhance overall wind farm performance. RGWFs employ multi-rotor systems with lifting devices (MRSLs), an innovative wind energy harvester engineered to stimulate strong vertical energy entrainment, thereby accelerating wake recovery. In the experiments, MRSLs are scaled for wind tunnel testing, with their rotors modeled using porous disks and their lifting devices represented by wings. The tested RGWFs comprise up to 3 × 3 MRSLs. Flow quantities within RGWFs and aerodynamic loads on MRSLs are measured using volumetric particle tracking velocimetry and strain gauges. Compared to conventional wind farms, flow analysis indicates that vertical energy entrainment is significantly enhanced in RGWFs, as evidenced by a more than 200 % increase in thrust on the second-row MRSLs and so on. These experimental results, which are in line with the previous numerical predictions, highlight the promising potential of RGWFs.

The airfoil DU91-W2-150 was investigated in the Low Speed Low Turbulence Tunnel at the Delft University of Technology to study unsteady aerodynamics. This experimental study tested the airfoil under a wide range of angles of attack (AoA) from 0° to 310° at three Reynolds numbers ((Formula presented.)) from (Formula presented.) to (Formula presented.). Pressure on the airfoil surface was measured and particle image velocimetry (PIV) measurements were conducted to capture the flow field in the wake. By examining the force coefficient and comparing the wake contours, it shows that an upwind concave surface provides a higher load compared to a convex surface upwind case, highlighting the critical role of surface shape in aerodynamics. When comparing separation at specific locations along the chord for all three (Formula presented.) values, it is observed that as (Formula presented.) increases, separation tends to occur at lower AoA, both for positive stall and negative stall. The examination of the aerodynamic force variation indicates that, during reverse flow, fluctuations are more pronounced compared to forward flow. This is owing to separation occurring at the aerodynamic leading edge (geometric trailing edge) in reverse flow. In terms of vortex shedding frequency, the study found a nearly constant normalized Strouhal number ((Formula presented.)) of 0.16 across various (Formula presented.) and AoA values in fully separated regions, indicating a consistent pattern under these conditions. However, a slight increase in (Formula presented.), between 0.16 and 0.20, was observed for AoA values exceeding 180°, possibly due to the convex curvature of the airfoil in the upwind direction. In conclusion, this research not only corroborates previous findings for small AoA values but also adds new data on the aerodynamic behavior of the DU91-W2-150 airfoil under large AoA values, offering various perspectives on the effects of surface curvature, (Formula presented.), and flow conditions on key aerodynamic parameters.

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.

Within the framework of the European Union Horizon 2020 project HOMER (Holistic Optical Metrology for Aero-Elastic Research), data assimilation (DA) algorithms for dense flow field reconstructions developed by different research teams, hereafter referred to as the participants, were comparatively assessed. The assessment is performed using a synthetic database that reproduces the turbulent flow in the wake of a cylinder in ground effect, placed at the distance of one diameter from a lower wall. Downstream of the cylinder, this wall continues either in the form of a flat steady wall, or of a flexible panel undergoing periodic oscillations; these two situations correspond to two different test cases, the latter being introduced to extend the evaluation to fluid–structure interaction problems. The input data for the data assimilation algorithms were datasets containing the particle locations and their trajectories identification numbers, at increasing tracer concentrations from 0.04 to 1.4 particles/mm³ (equivalent image density values between 0.005 and 0.16 particles per pixel, ppp). The outputs of the DA algorithms considered for the assessment were the three components of the velocity, the nine components of the velocity gradient tensor and the static pressure, defined in the flow field on a Cartesian grid, as well as the static pressure on the wall surface, and its position in the deformable wall case. The results were analysed in terms of errors of the output quantities with respect to the ground-truth values and their distributions. Additionally, the performances of the different DA algorithms were compared with that of a standard linear interpolation approach. The velocity errors were found in the range between 3 and 11% of the bulk velocity; furthermore, the use of the DA algorithms enabled an increase of the measurement spatial resolution by a factor between 3 and 4. The errors of the velocity gradients were of the order of 10–15% of the peak vorticity magnitude. Accurate pressure reconstruction was achieved in the flow field, whereas the evaluation of the surface pressure revealed more challenging. As expected, lower errors were obtained for increasing seeding concentration. The difference of accuracy among the results of the different data assimilation algorithms was noticeable especially for the pressure field and the compliance with governing equations of fluid motion, and in particular mass conservation. The analysis of the flexible panel test case showed that the panel position could be reconstructed with micrometric accuracy, rather independently of the data assimilation algorithm and the seeding concentration. The accurate evaluation of the static pressure field and of the surface pressure proved to be a challenge, with typical errors between 3 and 20% of the free-stream dynamic pressure.

Accurate modelling of wind turbine wakes is critical for optimizing wind farm performance, but the complexity of wake interactions poses significant challenges. This study presents a two-phase blind test campaign, part of the Horizon Europe TWEET-IE project, designed to benchmark numerical models and investigate wake control strategies using wind tunnel experiments. Conducted with tandem wind turbine models at the Technical University of Munich and the National Technical University of Athens, the tests include inflow, load, power, and wake velocity measurements under controlled conditions. Phase I serves as an open-data benchmarking exercise for a baseline scenario without wake control, while Phase II introduces active individual blade pitch control to the upstream turbine, challenging participants to simulate advanced wake dynamics. This paper reviews Phase I results and details the experimental framework for Phase II, providing a foundation for advancing wake modelling and control in wind energy research.

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.

Abstract: This paper presents the first full-scale particle image velocimetry (PIV) measurements to analyze the flow field of a car under real driving conditions. The Ring of Fire (RoF) measurement concept, introduced by Terra et al. (Exp Fluids 58:83, 2017. https://doi.org/10.1007/s00348-017-2331-0), is adapted to automotive demands to validate CFD simulations for further improvements of vehicle aerodynamics. The experiment consists of a tunnel setup, where neutrally buoyant helium-filled soap bubbles are used as flow tracers and are illuminated by two high-speed lasers. Four high-speed cameras captured the particles motion in two separate Stereo-PIV configurations with fields of view of 1.3 × 0.6 m² and 2.8 × 2.2 m². Data for a Volkswagen up!, while driving on a test track at a constant speed of 33.33 m/s, was acquired for the wake and the side mirror region and processed with standard multi-pass PIV algorithms, in order to quantify the flow field and estimate limits of the described measurement principle for on-road car aerodynamics. The resulting ensemble averaged velocity fields are compared with CFD simulations, showing agreement for the here considered cases within 7.0–9.7%, based on the root-mean-square error between the experimental and the numerical results. Furthermore, drag calculation from the obtained velocity fields based on moment conservation is performed and the percent difference to wind tunnel measurements reaches values below 3.0%.