Küppers, Jan-Philipp
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Publication Access status: Open Access , A WaveNet-based fully stochastic dynamic stall modelAccurate modeling of the dynamic stall remains a challenge for the design and construction of turbine blades and helicopter rotors. At the same time, wind turbines, for instance, are becoming steadily larger, further increasing the demands on their structure and necessitating even more detailed modeling of the forces at hand. The primarily used (semi-)empirical models today have a long research history and are invariably based on phase-averaged data from oscillating blade pitch experiments. However, much potential for more accurate modeling of uncertainties and force peaks is wasted here, since averaging blurs many features of the response signals. Even computational fluid dynamics can help little in this regard, since the Reynolds-averaged Navier–Stokes equations used in practice cannot account for cycle variations, and scale-resolving models require extremely large amounts of computational resources. This paper presents an approach for a fully stochastic machine learning model that can nevertheless simulate these critical properties. Aerodynamic coefficients are compared with experimental data for different test cases. It is shown that synthetic force profiles which cannot be distinguished from the experimental data visually and are very close to them in the frequency spectrum can be generated. Additionally, attention is drawn to the difficulty of evaluating such a model, as traditional error metrics are of little use. A combination of dynamic time warping and the Earth mover's distance provides a robust solution for this problem.Source Type:Article345 102 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , On the Design and Simulation of Novel Vertical Axis Turbine Concepts(2025)This dissertation provides a comprehensive investigation and advancement of methods for the design of vertical axis turbines (VAT), focusing on novel concepts and advanced simulation techniques. The starting point is a detailed analysis of the Kirsten-Boeing turbine, an almost unknown design from the 1920s characterized by a combination of lift- and drag-driven rotor features. Despite its unique operational flexibility, this turbine poses a major challenge in terms of efficiency and modeling complexity. To overcome these challenges, a new tool, the Actuator Surface Model (ASM), is developed based on the Navier-Stokes equations and blade element methods. This tool bridges the gap between high-resolution computational fluid dynamics (CFD) and simpler momentum models and enables near real-time simulations of VATs. Further progress in the work includes the development of a neural network based dynamic stall model that is integrated into the ASM to improve the prediction accuracy of turbine performance under different operating conditions. Based on these tools, a new turbine design, the Linear Turbine, is being developed and tested. This design adds linear sections to the classic Darrieus turbine scheme and achieves an efficiency of about 42% in field tests, which is a significant increase over the first prototypes and shows the potential for efficient kinetic energy conversion in free-flowing waters. This cumulative work documents the development from the Kirsten-Boeing turbine to the Linear Turbine and illustrates the synergy between scientific research, methodical development and practical engineering work in the field of hydropower. Each turbine is not just a theoretical concept, but a hand-made technical solution that has been tested and validated under real conditions. The knowledge gained from each development step reflects the overarching goal of increasing efficiency, minimizing environmental impact and exploiting the full potential of hydropower.Source Type:Doctoral Thesis2 1