Abstract
Thermographic flow visualization is a contactless, non-invasive technique to visualize the boundary layer flow on wind turbine rotor blades, to assess the aerodynamic condition and consequently the efficiency of the entire wind turbine. In applications on wind turbines in operation, the distinguishability between the laminar and turbulent flow regime cannot be easily increased artificially and solely depends on the energy input from the sun. State-of-the-art image processing methods are able to increase the contrast slightly but are not able to reduce systematic gradients in the image or need excessive a priori knowledge. In order to cope with a low-contrast measurement condition and to increase the distinguishability between the flow regimes, an enhanced image processing by means of the feature extraction method, principal component analysis, is introduced. The image processing is applied to an image series of thermographic flow visualizations of a steady flow situation in a wind tunnel experiment on a cylinder and DU96W180 airfoil measurement object without artificially increasing the thermal contrast between the flow regimes. The resulting feature images, based on the temporal temperature fluctuations in the images, are evaluated with regard to the global distinguishability between the laminar and turbulent flow regime as well as the achievable measurement error of an automatic localization of the local flow transition between the flow regimes. By applying the principal component analysis, systematic temperature gradients within the flow regimes as well as image artefacts such as reflections are reduced, leading to an increased contrast-to-noise ratio by a factor of 7.5. Additionally, the gradient between the laminar and turbulent flow regime is increased, leading to a minimal measurement error of the laminar-turbulent transition localization. The systematic error was reduced by 4% and the random error by 5.3% of the chord length. As a result, the principal component analysis is proven to be a valuable complementary tool to the classical image processing method in flow visualizations. After noise-reducing methods such as the temporal averaging and subsequent assessment of the spatial expansion of the boundary layer flow surface, the PCA is able to increase the laminar-turbulent flow regime distinguishability and reduce the systematic and random error of the flow transition localization in applications where no artificial increase in the contrast is possible. The enhancement of contrast increases the independence from the amount of solar energy input required for a flow evaluation, and the reduced errors of the flow transition localization enables a more precise assessment of the aerodynamic condition of the rotor blade.
Highlights
Flow visualization on wind turbines in operation enables an evaluation of the actual aerodynamic condition of a rotor blade
The following section presents the results of the principal component analysis (PCA) and compares the resulting flow visualizations with the two classical image processing methods
The article introduced an enhanced image processing method based on PCA for the thermographic flow visualization of wind turbine rotor blades
Summary
Flow visualization on wind turbines in operation enables an evaluation of the actual aerodynamic condition of a rotor blade. The position of the boundary layer flow transition between laminar and turbulent is of interest for the efficiency of the wind turbine because it correlates directly with the lift and drag of the airfoil [1]. For wind turbines in operation, the thermographic flow visualization is suitable because it is a noninvasive, contactless approach without the need for surface preparation [11]. The image processing is needed to automatically extract the image information that provides a flow visualization with a high distinguishability between the different flow regimes and that enables the localization of the laminar-turbulent flow transition with a minimal measurement error
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