## 5 Conclusions and Outlook  
The article introduced an enhanced image processing method based on PCA for the thermographic flow visualization of wind turbine rotor blades. The goal was to increase the distinguishability between the laminar and turbulent flow regimes and finally enabling a flow transition localization with minimal random and systematic error. The PCA results are compared to two classical image processing methods for evaluating a thermographic image series: the temporal mean value and the temporal standard deviation. Furthermore, two flow transition localization methods were introduced based on the approximation of the intensity profile and the intensity gradient profile in flow direction with a Gaussian error function and Gaussian function, respectively. The evaluation was applied to a cylinder in cross-flow condition and a DU96W180 airfoil. This allowed the study on a well-known geometry in fluid dynamics by means of the cylinder as well as on a more application-oriented object. The experiments were conducted in a wind tunnel with a closed test section and Reynolds numbers oriented on the application on real wind turbines. For the airfoil, two different fluid temperature situations were tested. A steady-state test case with an almost constant fluid temperature and a transient state with a positive temperature gradient were evaluated during the experiment. In this way, it was possible to analyze the PCA results on thermographic images with little to no distinguishability between the laminar and turbulent flow regimes and images with a high initial distinguishability. Thus, all possibilities from the real application were covered, and the potential of PCA regarding the application on wind turbines in operation was studied. In the cylinder experiment, the PCA enables a more robust localization of the flow transition compared to the classical image processing methods. The effects of systematic gradients and artefacts are minimized, and the CNR is increased by 47%. The random error of the localization is reduced by a factor of 20.7 and the systematic error by 57%. In test case 1 with a low contrast between flow regimes, the classical methods are not able to increase the CNR. Applying the PCA, however, a flow visualization with an increased CNR by a factor of 7.5 is created, and the systematic and random measurement errors of the flow transition localization are reduced by 3.98% and 5.26% of the chord length \(c\), respectively. If the distinguishability is already high, the PCA achieves a CNR on the same order of magnitude as the classical methods. However, the PCA enables a more robust flow transition localization by means of the intensity profile approximation with a Gaussian error function, as spatial systematic gradients in the flow regimes are reduced. The first airfoil test case also shows that the existence of a laminar separation bubble highly influenced the localization in the classical image processing methods, while the PCA result is unaffected and allows for a more robust flow transition localization. The increase in the flow regimes' distinguishability for measurement conditions with a low initial temperature difference between fluid and surface minimizes the requirement of the thermographic flow visualization technique with regard to the necessary solar energy input. Being less dependent on the strong blade heating, measurements during cloudy days, early in the morning or late in the day become possible. Additionally, a flow transition localization with reduced errors improves the assessment of the aerodynamic condition of the wind turbine and the decision regarding the necessity of maintenance work. Note that the findings of this work are applicable to any similar setup with an airfoil in cross-flow condition and comparable Reynold's numbers, for instance aircraft wings and helicopter blades [21]. Even if the introduced image processing methods evaluate different information in the image series, the combination of the methods does not prove to be advantageous.