Wind Turbine Wakes in Active Yaw Control: Numerical and Theoretical Studies

In this thesis, we explore the best practice of simulating the wakes of the turbines under active yaw control (AYC) using large-eddy simulation (LES). In the first study, we validate the blade-element actuator disk model (ADM-BE) for a yawed wind turbine. LES using the ADM-BE are compared with wind-tunnel measurements and analytical models. The results using the ADM-BE are in good agreement with both measurements and analytical models. We also find significant improvements in the power prediction of the ADM-BE over the standard actuator disk model (ADM-std). In the second study, we compare the LES of a turbine array in the non-yawed/yawed and full/partial-wake conditions with wind-tunnel measurements. The turbine forces are parametrised by three models: the ADM-std, the ADM-BE, and the actuator line model (ALM). LES results using the ADM-BE and ALM are in good agreement with wind-tunnel measurements. In contrast, LES with the ADM-std shows discrepancies with the measurements in the yawed and partial-wake conditions due to the uniform force assumption of the ADM-std, which fails to reproduce the inherently inhomogeneous distribution of the turbine forces. LES using the ADM-BE also yields better power predictions than the ADM-std and the ALM in the considered cases. In the third study, we investigate the power and blade fatigue of a turbine array under AYC in the full/partial-wake configurations using a two-way coupled framework of LES and aeroelastic simulation. In the full-wake configuration, the local power-optimal AYC strategy with positive yaw angles endures less flapwise blade fatigue and more edgewise blade fatigue than the global optimal strategy with negative yaw angles. In the partial-wake configuration, in certain inflow wind directions, applying positive AYC achieves higher optimal power gains than that in the full-wake scenario while reducing blade fatigue from the non-yawed benchmark. Using the blade-element momentum (BEM) theory, we show that the aforementioned differences in flapwise blade fatigue are caused by the differences in the azimuthal distributions of the local relative velocity on blade sections, resulting from the combined effects of vertical wind shear and blade rotation. Furthermore, the difference in the blade force between the positively and negatively yawed front-row turbine induces different wake velocity and turbulence distributions, which causes different fatigue loads on the downwind turbine exposed to the wake. Finally, in the fourth study, we use LES to investigate the wake-meandering of a wind turbine array under dynamic yaw control (DYC) and the effects on turbine power and fatigue. Based on spectral and dynamic mode decomposition (DMD) analyses of the flow fields, the wake meandering of the turbine array is significantly amplified when the turbine yaw frequency coincides with the natural wake meandering frequency of the turbine array in the static zero-yaw condition. The resonance of wake meandering accelerates wake recovery and helps the turbine array achieve optimal power production. We also find that the fatigue of the turbine array increases overall with the yaw frequency of the first turbine, highlighting the necessity of jointly considering power production and fatigue when applying DYC.