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.

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The topic of vortex-induced vibrations on a wind turbine blade has recently gained much attention due to its growing size and flexibility. To address this concern, a wind tunnel test was conducted to study the forced plunging and surging motion of a NACA0021 airfoil at 90° angle of attack. Results indicate that vortex lock-in occurred for a motion amplitude of one chord length even for a small frequency ratio (between motion frequency and static Strouhal frequency) of 0.39. Analysis of the drag coefficient, derived from the phase-averaged Particle Image Velocimetry (PIV) data, shows that a plunging airfoil experiences higher average loading than a surging airfoil, which is deemed to be more harmful considering the higher loading in the crossflow-direction due to the variation of effective angle of attack.

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Machine learning method has always been popular to solve wind turbine related problems at a data level. However, with the limitation of the availability of relevant data, transfer learning has gained increasing attention. In this study, traditional machine learning method of artificial neural networks (ANN), together with parameter-based transfer learning method has been used to estimate wind turbine load. First, ANN load model was built for DTU 10MW wind turbine as well as NREL 5MW wind turbine. Then, parameter-based transfer learning has been applied to the above-mentioned models to estimate load for a different turbine type or two mixed turbine types. Results indicate that ANN method provides good estimation on wind turbine fatigue load. For DTU 10MW ANN model, the trend of accuracy becomes steady as the number of input samples increases and 1500 samples is deemed as the optimal number of samples for training DTU 10MW. In addition, with transfer learning, it was succeeded in building NREL 5MW model with corresponding DTU 10MW pretrained model but failed in establishing mixed dataset model neither with DTU 10MW nor with NREL 5MW pretrained model. @en