Dynamic Stall Modeling for Wind Turbines

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Abstract

Modern wind turbines frequently operate at off-design conditions during their life cycle. They undergo dynamic loads characterized by unsteady aerodynamics. Predicting these unsteady aerodynamic loads has been very difficult due to the non-linear nature of unsteady aerodynamics. Especially when operating near the stall region, these turbine are prone to increased loads because of dynamic stall. Dynamic stall is typically observed when there is a turbulent inflow, yaw misalignment, or severe wind shear causing periodic variations in angle of attack. Nonetheless, the nature of dynamic stall phenomenon is still a topic under investigation. The aim of this research was to investigate the performance of dynamic stall models in yawed and standstill conditions by using current state-of-the-art engineering models and validating the results with the New MEXICO (Model Rotor Experiments under Controlled Conditions) measurement campaigns. The first part of the research dealt with a detailed analysis of the New MEXICO experiments in standstill and yawed flow conditions. This part also encompassed extracting 3D polars from pressure measurements and a spectral analysis to characterize any vortex shedding phenomenon in standstill conditions. The second part of the research was concerned with validating dynamic stall models implemented in ECN’s in-house aeroelastic tool Aero-Module. Three different dynamic stall models namely: Snel, ONERA, and Beddoes Leishman model, were extensively validated and improved using New MEXICO measurements in standstill and yawed flow conditions. Finally, a case study was performed on the AVATAR rotor, using afore-mentioned dynamic stall models, to access their effect on aerodynamic damping and, consequently, in predicting the onset of aeroelastic instabilities. The research was able to shed light on our current understanding of dynamic stall phenomenon and the way we model it, hoping to improve the dynamic stall modeling capabilities in the future.

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