Improving CFD wind farm simulations incorporating wind direction uncertainty

Abstract Accurate quantification of wake losses is crucial in wind farm economics. Computational Fluid Dynamics (CFD) has been proven to be a reliable solution to simulate many complex flows, but several studies showed that its effectiveness in wind farms simulations has not always been consistent. In this work, we investigate the causes for that inconsistency and propose a modeling framework to overcome them. A CFD model was developed using the actuator disk technique to simulate the wind turbines and the surface boundary layer approximation to simulate the ambient conditions. The developed CFD model was implemented for three different wind farms with publicly available experimental measurements. The predictions of CFD model were post-processed with an innovative method that uses a Gaussian-weighted average of a set of CFD results for different wind directions to account for the wind direction uncertainty in the experimental data. Our results show that the proposed method significantly improves the agreement of the CFD predictions with the available experimental observations. These results suggest that the discrepancies between CFD predictions and experimental data reported in previous works, attributed to inaccuracy of the CFD models, can be explained instead by the uncertainty in the wind direction reported in the data sets.