Simon Nietiedt

Spatio-temporal data of objects are crucial for scientific research and technical analysis in various dynamic applications. Particularly relevant is the measurement of deformations by photogrammetric deformation analyses, which are carried out in a dense and non-intrusive manner. However, the observation of fast-moving phenomena, such as in wind tunnel experiments, is often affected by kinematics and specific experimental circumstances, which can reduce the quality of the results. In addition, acquired sensor data, which provide information about rotational velocities and accelerations, are often not sufficiently integrated into the photogrammetric analysis. This paper presents a generic framework to integrate kinematic information into the photogrammetric deformation analysis. Based on this, we extend object-based image matching to a sub-pixel accurate spatio-temporal matching method, integrating kinematic data as prior knowledge and simultaneously determining unknown (geometric and kinematic) parameters. We applied the approach in wind tunnel tests with a miniaturized wind turbine, where additional kinematic information about the rotation was acquired. The results demonstrate that the new method generates highly accurate and dense information about blade deformation using photogrammetric and kinematic data. The approach is applicable to a variety of dynamic photogrammetric applications.

The design of rotor blades is based on information about aerodynamic phenomena. An important one is fluid-structure interaction (FSI) which describes the interaction between a flexible object (rotor blade) and the surrounding fluid (wind). However, the acquisition of FSI is complex, and only a few practical concepts are known. This paper presents a measurement setup to acquire real information about the FSI of rotating wind turbines in wind tunnel experiments. The setup consists of two optical measurement systems to simultaneously record fluid (PIV system) and deformation (photogrammetry system) information in one global coordinate system. Techniques to combine both systems temporally and spatially are discussed in this paper. Furthermore, the successful application is shown by several experiments. Here, different wind conditions are applied. The experiments show that the new setup can acquire high-quality area-based information about fluid and deformation.