Most icing research focuses on the high Reynolds number regime and manned aviation. Information on icing at low Reynolds numbers, as it is encountered by wind turbines and unmanned aerial vehicles, is less available, and few experimental datasets exist that can be used for validation of numerical tools. This study investigated the aerodynamic performance degradation on an S826 airfoil with 3D-printed ice shapes at Reynolds numbers Re = 2 × 105, 4 × 105, and 6 × 105. Three ice geometries were obtained from icing wind tunnel experiments, and an additional three geometries were generated with LEWICE. Experimental measurements of lift, drag, and pressure on the clean and iced airfoils have been conducted in the low-speed wind tunnel at the Norwegian University of Science and Technology. The results showed that the icing performance penalty correlated to the complexity of the ice geometry. The experimental data were compared to computational fluid dynamics (CFD) simulations with the RANS solver FENSAP. Simulations were performed with two turbulence models (Spalart Allmaras and Menter’s k-ω SST). The simulation data showed good fidelity for the clean and streamlined icing cases but had limitations for complex ice shapes and stall.

Lift, drag and surface pressure measurements are performed on a wing section of the NREL S826 wind turbine airfoil at eight Reynolds numbers ranging from 0.5 × 10 5 to 6.0 × 10 5 . Alongside with the measurements two types of Reynolds averaged Navier-Stokes (RANS) simulations are performed, one of which includes a laminar to turbulent transition model. The lift and drag characteristics are observed to be dominated by low Reynolds number effects for Re < 0.7×10 5 , related to the presence of laminar separation bubbles (LSBs) on the suction side of the profile. For Re ≥ 0.7 × 10 5 the airfoil's performance is rather independent of the Re-number for the present free stream turbulence intensities, while significantly higher peak lift is measured than in earlier experiments on the same airfoil. At high angles of attack, strong three-dimensional spanwise surface flow distribution reminiscent of a single stall cell is observed. The RANS simulations in a two-dimensional domain including the Langtry-Menter γ − Re θ transition model accurately predict lift and drag coefficients as long as the flow is fairly attached. Further, the γ − Re θ model simulations are observed to predict the location and average size of the LSBs in this region.

Due to their motion, floating wind lidars overestimate turbulence intensity ( T I ) compared to fixed lidars. We show how the motion of a floating continuous-wave velocity–azimuth display (VAD) scanning lidar in all six degrees of freedom influences the T I estimates, and present a method to compensate for it. The approach presented here uses line-of-sight measurements of the lidar and high-frequency motion data. The compensation algorithm takes into account the changing radial velocity, scanning geometry, and measurement height of the lidar beam as the lidar moves and rotates. It also incorporates a strategy to synchronize lidar and motion data. We test this method with measurement data from a ZX300 mounted on a Fugro SEAWATCH Wind LiDAR Buoy deployed offshore and compare its T I estimates with and without motion compensation to measurements taken by a fixed land-based reference wind lidar of the same type located nearby. Results show that the T I values of the floating lidar without motion compensation are around 50 % higher than the reference values. The motion compensation algorithm detects the amount of motion-induced T I and removes it from the measurement data successfully. Motion compensation leads to good agreement between the T I estimates of floating and fixed lidar under all investigated wind conditions and sea states.

Abstract

In this study, the performance, drag, and horizontal midplane wake characteristics of a vertical‐axis Savonius wind turbine are investigated experimentally. The turbine is drag driven and has a helical configuration, with the top rotated 180° relative to the bottom. Both performance and wake measurements were conducted in four different inflow conditions, using Reynolds numbers of ReD≈1.6×105 and ReD≈2.7×105 and turbulence intensities of 0.6% and 5.7%. The efficiency of the turbine was found to be highly dependent on the Reynolds number of the incoming flow. In the high Reynolds number flow case, the efficiency was shown to be considerably higher, compared with the lower Reynolds number case. Increasing the incoming turbulence intensity was found to mitigate the Reynolds number effects. The drag of the turbine was shown to be independent of the turbine's rotational speed over the range tested, and it was slightly lower when the inflow turbulence was increased. The wake was captured for the described inflow conditions in both optimal and suboptimal operating conditions by varying the rotational speed of the turbine. The wake was found to be asymmetrical and deflected to the side where the blade moves opposite to the wind. The largest region of high turbulent kinetic energy was on the side where the blade is moving in the same direction as the wind. Based on the findings from the wake measurements, some recommendations on where to place supplementary turbines are made.