Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms - Porté-Agel, Fernando; Wu, Yu-Ting; Lu, Hao; Conzemius, Robert J. (Journal of wind engineering and industrial aerodynamics, 2011)
Accurate prediction of atmospheric boundary layer (ABL) flow and its interactions with wind turbines and wind farms is critical for optimizing the design (turbine siting) of wind energy projects. Large-eddy simulation (LES) can potentially provide the kind of high-resolution spatial and temporal information needed to maximize wind energy production and minimize fatigue loads in wind farms. However, the accuracy of LESs of ABL flow with wind turbines hinges on our ability to parameterize subgrid-scale (SGS) turbulent fluxes as well as turbine-induced forces. This paper focuses on recent research efforts to develop and validate an LES framework for wind energy applications. SGS fluxes are parameterized using tuning-free Lagrangian scale-dependent dynamic models. These models optimize the local value of the model coefficients based on the dynamics of the resolved scales. The turbine-induced forces (e.g., thrust, lift and drag) are parameterized using two types of models: actuator-disk models that distribute the force loading over the rotor disk, and actuator-line models that distribute the forces along lines that follow the position of the blades. Simulation results are compared to wind-tunnel measurements collected with hot-wire anemometry in the wake of a miniature three-blade wind turbine placed in a boundary layer flow. In general, the characteristics of the turbine wakes simulated with the proposed LES framework are in good agreement with the measurements in the far-wake region. Near the turbine, up to about five rotor diameters downwind, the best performance is obtained with turbine models that induce wake-flow rotation and account for the non-uniformity of the turbine-induced forces. Finally, the LES framework is used to simulate atmospheric boundary-layer flow through an operational wind farm.