Felix Oehme

Thermographic flow visualization is already an established imaging method to localize the laminar-turbulent flow transition on the rotor blades of operating wind turbines, while a steady flow state is assumed. To understand the potential of thermographic flow visualization for the investigation of unsteady flow phenomena, its capability to detect the change of the flow transition position due to a wind gust is studied. Previously laminar flow regions become turbulent with the gust, which means a sudden increase of heat transfer between surface and fluid and, thus, a decrease of surface temperature. The latter is detected by evaluating the difference of thermographic images before and during the wind gust. The achievable sensitivity and the temporal resolution are limited by the thermodynamic properties of the rotor blade and the fluid flow, as well as by the natural rotor blade heating with the sun’s radiation. As a result of theory and experiments on real wind turbines, the feasibility to detect flow state changes in the order of seconds is proven. This opens upthe analysis of unsteady flow phenomena on wind turbines by means of thermographic flow visualization.

A thermographic detection and localization of unsteady flow separation on an operating wind turbine of type GE1.5sl is presented and verified by means of tufts flow visualisation. Unsteady flow separation phenomena such as dynamic stall are an undesired flow state as it causes fatigue failures, limits the turbine efficiency and increases noise emissions from the rotor blades. In comparison to available methods for stall detection on wind turbines, the presented infrared thermographic measurement approach is non-invasive, in-process capable and provides a high spatial resolution. On the basis of the thermodynamic response behaviour of the surface temperature in case of unsteady flow events, a two-step signal processing approach is proposed, to achieve the highest possible spatio-temporal resolution in the detection and localisation of stall. First, the identification of distinct maxima of the spatial standard deviation of difference images, enables to determine potential stall events in time. In the subsequent combined image evaluation with a transient approach and a principal component analysis, unsteady flow separation is detected during the occurrence of a strong wind gust with the maximum time resolution (image exposure time) as well as the maximum spatial resolution (image resolution), respectively, despite the limited signal-to-noise ratio compared to wind tunnel experiments. In addition, a geometric assignment of the image data to the rotor blade geometry is conducted, which enables a localization of the separation point on the 3 days rotor blade geometry with a maximal uncertainty of 2.3% of the chord length.

Model-inspired signal processing approaches with an enhanced detectability of flow separation on thermographic images are presented. Flow separation causes performance loss, structural loads and increasing acoustic emissions on wind turbine rotor blades. However, due to the low thermal contrast between turbulent and separated flow regions, the non-invasive thermographic visualisation of flow separation is currently only possible for wind tunnel measurements, which are characterised by a high thermal contrast and a small measuring distance. The state-of-the-art signal processing approaches evaluate the surface temperature fluctuation of thermographic image series. However, understanding of the signal measurement chain with a distinct consideration of the influences on the dynamic surface temperature is incomplete. Therefore, designing model-inspired signal processing approaches which provide a high interpretability and a maximum contrast is an open task. The proposed signal processing approaches evaluate the surface response selectively, by using the amplitude information of the surface temperature response to an oscillating input signal or gradient-based for a transient input signal. The approaches are applied to wind tunnel measurements on a rotor blade profile at a near thermodynamic steady state and a transient thermodynamic behaviour at Reynolds numbers that are representative for operational wind turbines. The gradient-based evaluation shows an improved contrast for the detection of flow separation, but is only applicable to profiles with transient thermodynamic behaviour. The amplitude evaluation provides a high degree of interpretability of the processed images based on flow-dependent features and enables for an unambiguous identification of flow separation by a global amplitude minimum close to the separation point. Additionally, an increased spatial resolution for surface modifications is shown, while the contrast between flow regions is significantly decreased. Hence, the proposed approaches allow for an improved identifiability of flow separation with regard to future applications on wind turbines in operation.

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.