Abstract
Abstract. Monitoring the flow features over wind turbine blades is a challenging task that has become more and more crucial. This paper is devoted to demonstrate the ability of the e-TellTale sensor to detect the flow stall–reattachment dynamics over wind turbine blades. This sensor is made of a strip with a strain gauge sensor at its base. The velocity field was acquired using time-resolved particle image velocimetry (TR-PIV) measurements over an oscillating 2D blade section equipped with an e-TellTale sensor. PIV images were post-processed to detect movements of the strip, which was compared to movements of flow. Results show good agreement between the measured velocity field and movements of the strip regarding the stall–reattachment dynamics.
Highlights
Wind turbines are placed in the low layers of the atmospheric boundary layer where the wind is strongly influenced by the surface roughness and the thermal stability which creates turbulence and vertical gradients of the wind (Emeis, 2018)
Smart blades and/or fluidic actuators are considered (Pechlivanoglou, 2013; Jaunet and Braud, 2018; Batlle et al, 2017). For this last strategy or to perform blade remote monitoring, one key issue is the development of robust technologies able to provide an instantaneous detection of the state of the flow on the blade aerodynamic surface
Results of the detection of the strip are compared to all detection methods to evaluate the ability of the sensor to detect the flow stall–reattachment dynamic
Summary
Wind turbines are placed in the low layers of the atmospheric boundary layer where the wind is strongly influenced by the surface roughness and the thermal stability which creates turbulence and vertical gradients of the wind (Emeis, 2018). Offshore turbines are arranged in an array layout and not just in line, which induces additional sheared inflow conditions and additional small turbulent structures (Chamorro et al, 2012) This results in strong and local variations in speed and directions on the wind turbine rotor blades. These variations lead to unsteady aerodynamic effects with turbulent inflows responsible for more than 65 % of fatigue loads (Rezaeiha et al, 2017). This resulting identified profile curve was fit to the theoretical suction side profile curve to extract the best Euclidean transformation (i.e. only rotation, translation and uniform scaling considered for the transformation) going from the measured curve to the theoretical profile This was done using a function of OpenCV which primarily uses the RANSAC algorithm to detect spurious points and the Levenberg–Marquardt algorithm to fit the profile.
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