Wind turbines play a crucial role in the worldwide effort to embrace sustainable energy, utilizing sophisticated aerodynamic principles to efficiently capture wind energy. A thorough comprehension of deep dynamic stall, a phenomenon that greatly impacts wind turbine performance, is essential for maximizing efficiency, maintaining structural integrity, and propelling the development of wind energy. This study presents an experimental exploration of deep dynamic stall phenomena through wind tunnel experiments carried out on a NACA643418 airfoil at TU Delft.

The study commences with the development of a comprehensive test matrix drawing from existing literature, with a focus on angles of attack of 40, 50, and 90 degrees. Through precise experimentation, the research team meticulously measures and corrects for wind tunnel effects, uncovering crucial trends in lift and drag coefficients. Significantly, the study identifies laminar separation bubbles and trailing edge separation as the main stall mode before the deep stall regime.

The analysis of static and dynamic pressure data offers valuable insights into the aerodynamic characteristics of the airfoil in deep stall conditions. Notably, significant variations in aerodynamic performance between the upstroke and downstroke are evident, particularly at high angles of attack surpassing 25 degrees. The data underscores the intricate interplay of pitching frequency, amplitude, and airflow separation, highlighting the pivotal influence of shedding frequency on dynamic stall phenomena.

Moreover, measurements of dynamic pressure reveal the intricate relationship between the frequency and amplitude of pitching and the patterns of vortex shedding. The research shows that vortex shedding is most significant at angles of attack close to 90 degrees, even during dynamic pitching. These discoveries emphasize the significance of comprehending deep dynamic stall to enhance the design and performance of wind turbines in various operational conditions.

The phase-averaged PIV images serve as a valuable complement to the pressure data, offering a visual confirmation of how flow dynamics impact aerodynamic performance in deep stall conditions. These images unveil clear disparities in airflow behaviour during upstroke and downstroke motions, shedding further light on the aerodynamic obstacles encountered by wind turbine blades during dynamic operation.

With the increased use of Advanced Driver Assistance Systems (ADAS) in modern cars, the  field of surface contamination saw a resurgence. To keep the various sensors working properly it is of utmost importance to keep them clean and free of contamination. One way to achieve this is to ensure they are mounted in areas where they can be kept free of water droplets that can cause issues themselves, or by depositing contaminants that they are carrying. Current research focuses on the resulting contami- nation pattern after a drive or test on the car, this thesis however aims to see whether state-of-the-art Particle Image Velocimetry (PIV) techniques can be used to track the water droplets themselves, and with it open up new ways of researching surface contamination. To achieve this two experiments were performed. The first one was a simple experiment to see whether the droplets are being able to be imaged with the PIV cameras. The second experiment introduced a car side mirror model to the flow and used the Shake-the-Box (STB) algorithm to track the individual water droplets. Both of these ex-periments were successful, and showed that this combination of water droplets and STB PIV has great potential for being used in surface contamination research. The main issues found were with experi-mental setups, especially in the windtunnel. As the droplets do not follow the airflow accurately, extra care needs to be taken to ensure the droplets are operating at the right conditions, and are present at the measurement domain. The actual behaviour of the droplets as tracked by the STB algorithm, seems to be accurate with respect to the expectations indicating that the tracking of the droplets is accurate. Further research should try to increase the scale of the experiments, and/or introduce contaminants to the droplets to see how, if at all, this affects the traceability of the droplets.