Flow Around Thick Airfoils at Very High Reynolds Number. Stall and Dynamic Stall Applications
With the increase of the power and rotor diameter of modern wind turbines, blade loads must be predicted with high confidence in order to optimize accurately the complex blade internal structure. Unsteady aerodynamic loadings such as dynamic stall are the main challenges for state-of-the-art numerical tools (Leishman, Challenges in modeling the unsteady aerodynamics of wind turbines, 2002, ). Dynamic stall can appear on horizontal-axis wind turbines (HAWT) in several operating conditions: misalignment with the wind direction, free-stream turbulence, fast pitch maneuvers... Wind tunnel experiments and RANS or URANS simulations are the state-of-the-art tools to obtain estimations of aerodynamic forces, specifically in stall and dynamic stall cases. The present work aims at getting a better insight into the dynamics of the flow around thick wind turbines airfoils thanks to Large-Eddy Simulation (LES), which resolves a broader range of turbulent scales. These thick airfoils operate at very high Reynolds number because of the dimensions of the rotor. In order to perform LES with realistic CPU time, a Wall-Modeled LES (WMLES) strategy is considered. Several simulations are carried out at Reynolds number of \(1.6\cdot 10^6\) on the FFA-W3-241 profile, a \(24.1\%\) relative thickness profile. Attached flow is first investigated, then detached flow in steady and oscillating conditions are studied. The impact of spanwise length is considered, in particular for stalled cases.
This work was granted access to the HPC resources of IDRIS, CCRT and CRIANN, under the allocations GENCI/x20172b6186 and CRIANN/2008013, respectively. This study is part of the MOQUA project managed by IRT Jules Verne (French Institute in Research and Technology in Advanced Manufacturing Technologies for Composite, Metallic and Hybrid Structures). The authors wish to associate the industrial and academic partners of this project: IRT Jules Verne, Adwen, Loiretech, UBS, Nenuphar, ECN and CNRS.
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