Using LES to Understand Wake Evolution During the Diurnal Cycle

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Numerical study using actuator-line method based Large Eddy Simulation (LES) has been performed to understand the role of atmospheric stability on the wake effects of horizontal-axis full-scale 5-MW wind turbine (WT). The paper will specifically focus on using specific instances in the diurnal cycle corresponding to stable, neutral and unstable ABL state to gain understanding on the transient aerodynamics of a wind turbine throughout the diurnal cycle. Capturing accurate Atmospheric Boundary Layer (ABL) characteristics is key factor in improving the accuracy of WT model predictions as turbulence developed in the ABL has potential to adversely affect the fatigue lifetime and performance of wind turbines. ABL simulations for the diurnal cycle are performed to isolate the key ABL metrics such as surface momentum flux, boundary layer height, surface temperature flux, wind shear, and temperature gradient that influence the wake evolution of WT. Precursor ABL inflow is generated for the WT simulations. The positive heat flux on the surface causes high vertical velocity fluctuations described with streaks and updraft motions during the day while surface cooling rates result in increased shear and strong temperature gradients during the night. The surface temperature, geostrophic wind velocity, heating/cooling rates, and period of the diurnal cycle are varied in different simulations to compare turbulent statistics and the helical vortices of the wind turbine wake. The results have revealed surface temperature and surface flux are the important ABL metrics that have a strong effect on altering the turbulence in the WT wake. In addition, instabilities related to WT blade rotation exhibit sensitivity to ABL metrics. The positive heat flux shows higher mixing and causes large wake movement in the day-time conditions. The results aid in quantifying the movement of the wake at different times of the diurnal cycle. During night-time conditions mixing is low, causing slower wake recovery times. This is the first study to clearly isolate the key ABL metrics that influence the full-scale WT near-wake effects The study has implications in improving the predictions of WT power loss due to wake deficits. Further, this study sets an important direction on future modeling studies in identifying the ABL conditions in a diurnal cycle that influence the WT wake evolution.