Laminar-Turbulent Transition Localization in Thermographic Flow Visualization by Means of Principal Component Analysis  
[PERSON]  
\({}^{\copyright}\), [PERSON]  
\({}^{\copyright}\), [PERSON]  
\({}^{\copyright}\)  
\({}^{\copyright}\)  
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]. One possibility of visualizing the boundary layer flow on an airfoil is given by the thermographic flow visualization that makes use of the relation between the heat transfer coefficient and the local skin friction between the fluid and the surface [2]. The technique is an already long established method in wind tunnel experiments to visualize the boundary layer flow [3; 4; 5] and enables the analysis of the laminar-turbulent flow transition [6; 7], the laminar separation bubble [8; 9] and turbulent separation [10]. For wind turbines in operation, the thermographic flow visualization is particularly suitable because it is a non-invasive, contactless approach without the need for surface preparation [11]. In addition to the required infrared camera located on the ground in a distance of 100 m to 300 m, image processing is essential to enable a thermographic flow analysis. 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 finally enables the localization of the laminar-turbulent flow transition with a minimal measurement error. Influencing factors reducing the global distinguishability between different flow regimes in thermographic flow visualizations exist as a result of the flow characteristics and external interference. Flow characteristics cause systematic temperature gradients within a flow regime region due to a non-constant heat flux as well as random temperature fluctuations due to flow fluctuations. External interferences are systematic temperature gradients due to reflections and random measurement noise. To cope with the effects of these influencing factors, the distinguishability between different flow regimes is usually maximized by increasing the initial temperature difference between fluid and surface with an active heating or cooling [5; 6; 7; 12; 13], respectively, while the influence of reflections is further minimized by subtracting a reference image that was acquired by prior measurements with no flow [7]. Since the temperature difference between fluid and surface cannot be altered in free-field applications on wind turbines in operation without excessive effort, and reference images cannot be acquired, an image processing of the raw thermographic images is desired that is able to cope with the effects of the influence factors and maximizes the flow regimes' distinguishability. Classical image processing methods for the thermographic flow visualization automatically result in a single output image. For instance, the averaging of a series of thermographic images of a steady flow situation leads to a minimized measurement noise and thus increases the signal-to-noise ratio. [PERSON] et al. [14] introduced an automatic evaluation with a spatial low-pass filter to increase the signal-to-noise ratio in single images of in-flight experiments. Both methods increase the distinguishability between flow regimes by minimizing the random image inhomogeneity. However, systematic influences remain present. Another image processing introduced by [PERSON] et al. [15] focuses on temperature fluctuations and evaluates the temporal standard deviation of an image series to increase the distinguishability between flow regimes by reducing random and systematic inhomogeneities. These classical methods enable a straightforward, reproducible image processing, but systematic image inhomogeneities are either not or only partially corrected, which still limits the flow regimes' distinguishability.