Abstract
Defects on rotor blade leading edges of wind turbines can lead to premature laminar–turbulent transitions, whereby the turbulent boundary layer flow forms turbulence wedges. The increased area of turbulent flow around the blade is of interest here, as it can have a negative effect on the energy production of the wind turbine. Infrared thermography is an established method to visualize the transition from laminar to turbulent flow, but the contrast-to-noise ratio (CNR) of the turbulence wedges is often too low to allow a reliable wedge detection with the existing image processing techniques. To facilitate a reliable detection, a model-based algorithm is presented that uses prior knowledge about the wedge-like shape of the premature flow transition. A verification of the algorithm with simulated thermograms and a validation with measured thermograms of a rotor blade from an operating wind turbine are performed. As a result, the proposed algorithm is able to detect turbulence wedges and to determine their area down to a CNR of 2. For turbulence wedges in a recorded thermogram on a wind turbine with CNR as low as 0.2, at least 80% of the area of the turbulence wedges is detected. Thus, the model-based algorithm is proven to be a powerful tool for the detection of turbulence wedges in thermograms of rotor blades of in-service wind turbines and for determining the resulting areas of the additional turbulent flow regions with a low measurement error.
Highlights
Both of the wedge detection algorithms result in a flow transition line which includes all premature transitions across the width of the thermogram, see Figure 8d
To determine the area of the turbulence wedges with the gradient-based algorithm (GBA), the y-coordinates of the natural transition line are subtracted from the y-coordinates of the detected premature transition line
(a) shows the simulated thermogram with the turbulence wedges numbered with roman numerals. (b) shows the reference transition line as well as the contrast-to-noise ratio (CNR) value of the wedges noted above the respective wedge. (c) shows the result of applying the gradient-based algorithm (GBA) of Gleichauf et al [14] to the image and (d) shows the result of the application of the model-based algorithm (MBA) on the simulated thermogram
Summary
Electrical energy created by wind turbines has become an increasingly important part in providing clean power. Defects influence the geometry and surface quality of the rotor blade and may lead to premature transitions from laminar to turbulent flow in the boundary layer. A temperature difference exists between different boundary layer flow regimes due to varying local heat transfer coefficients Using this technique, areas with turbulent flow such as the turbulence wedges can be visualized. Often, no active heating is available when operating wind turbines and the blade is only heated passively by sunlight For this reason, an image processing algorithm is required which is capable of reliably detecting turbulence wedges under low contrast conditions. An image processing algorithm is required which is capable of reliably detecting turbulence wedges under low contrast conditions This would enable extensive field studies of the boundary layer flow state around the blade of operating turbines. Features such as position and size with a low uncertainty to quantify the resulting total area with turbulent flow
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