Wind turbine plants have grown in size in recent years, making an efficient structural health monitoring of all of their structures ever more important. Wind turbine towers deform elastically under the loads applied to them by wind and inertial forces acting on the rotating rotor blades. In order to properly analyze these deformations, an earthbound system is desirable that can measure the tower’s movement in two directions from a large measurement working distance of over 150 m and a single location. To achieve this, a terrestrial laser scanner (TLS) in line-scanning mode with horizontal alignment was applied to measure the tower cross-section and to determine its axial (in the line-of-sight) and lateral (transverse to the line-of-sight) position with the help of a least-squares fit. As a result, the proposed measurement approach allowed for analyzing the tower’s deformation. The method was validated on a 3.4 MW wind turbine with a hub height of 128 m by comparing the measurement results to a reference video measurement, which recorded the nacelle movement from below and determined the nacelle movement with the help of point-tracking software. The measurements were compared in the time and frequency domain for different operating conditions, such as low/strong wind and start-up/braking of the turbine. There was a high correlation between the signals from the laser-based and the reference measurement in the time domain, and the same peak of the dominant tower oscillation was determined in the frequency domain. The proposed method was therefore an effective tool for the in-process structural health monitoring of tall wind turbine towers.

Initial defects, for example, those occurring during the production of a rotor blade, encourage early damages such as rain erosion at the leading edge of wind turbine rotor blades. To investigate the potential that initial defects have for early damage, long-pulse thermography as a non-destructive and contactless measurement technique is applied to a strongly curved and coated test specimen for the first time. This specimen is similar in structural size and design to a rotor blade leading edge and introduced with sub-surface defects whose diameters range between $2\mathrm{mm}$ and $3.5\mathrm{mm}$ at depths between $1.5\mathrm{mm}$ and $2.5\mathrm{mm}$ below the surface. On the curved and coated test specimen, sub-surface defects with a depth-to-diameter ratio of up to $1.04$ are successfully detected. In particular, defects are also detectable when being observed from a non-perpendicular viewing angle, where the intensity of the defects decreases with increasing viewing angle due to the strong surface curvature. In conclusion, long-pulse thermography is suitable for the detection of sub-surface defects on coated and curved components and is therefore a promising technique for the on-site application during inspection of rotor blade leading edges.

Purpose An accurate measurement of intraocular pressure (IOP) is still essential for detecting, following-up, and treating glaucoma. The objective of the interdisciplinary project GlauPhon was to prove a new noncontact tonometry principle that analyzes the acoustic oscillation of the eye. Method Three enucleated porcine eyes were infused via the optic nerve with a saline solution. The IOP was adjusted by varying the height of the infusion bottle. A speaker closed one end of a cylindrical pressure chamber and an eye was fixed to the other side. A PC sound card induced the speaker to oscillate by generating a rectangular signal (20 Hz). A pressure sensor recorded the oscillating pressure within the chamber. For each IOP a calculation was performed that characterizes the attenuation profile. Results Each series of measurements revealed an evident dependency between the amplitude difference and the IOP. The highest signal belonged to low IOP levels and it decreased with increasing IOP. The correlation of the mean acoustical signal with the given IOP showed a highly significant correlation coefficient (r=−0.98). As a result, the measured oscillation parameters are strongly dependent on the exerted IOPs. Conclusions The experiments verified the presumed relation between the acoustic oscillation of the eye and the IOP. Nevertheless, further developments are necessary for converting the oscillation parameters into reliable IOP values, to construct a tonometry device for clinical trials.

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.

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.