Generally, the wake behind a wind turbine is characterized as a reduction in wind velocity and an increase in turbulence level compared to the free stream condition. In wind farms where wind turbines are grouped in arrays, under unfavorable conditions, downstream wind turbines will operate in the wakes of upstream turbines, and thus will harm the overall efficiency of wind farms. Accurately predicting the performance of downstream turbines and the interactions between multiple turbine wakes are crucial to the design of more efficient wind farms because it forms the cornerstone of wind farm layout optimization algorithms.

In the present study, we perform CFD simulations for the NTNU Blind Test 2 experiment in which two turbines were placed in a closed-loop wind tunnel and operating in line. The Reynolds-Averaged Navier Stokes (RANS) equations with the k-ω SST turbulence model are adopted in the simulations. For each of the two wind turbines, geometries including the blades, hub, nacelle, and tower are fully resolved. The Moving-Grid-Formulation (MVG) approach with a sliding interface technique is leveraged to handle the relative motion between the rotating and stationary portions of the wind turbines. In the simulations, the values of tip-speed ratio (TSR) for the upstream and downstream turbines are 4 and 6, respectively. The CFD-predicted thrust and power coefficients are obtained under an inlet velocity of 10 m/s and are compared against the experiment results. In addition, the wake structures of the two wind turbines are also visualized and discussed.


The wake generated by a horizontal-axis wind turbine (HAWT) is characterized by a decrease in wind velocity and an increase in the turbulence level compared to the free stream condition. Grouped in clusters in modern onshore and offshore wind farms, wind turbines will unavoidably be operating in the wake of upstream turbines. Therefore, the power generation efficiency of the downstream wind turbines in a wind farm will decline, and as a result, the overall power generated by a wind farm will be affected (Vanderwende et al., 2016). Researchers estimated that the overall power loss of a large wind farm is 10% - 25%. (Wu and Porté-Agel, 2015). To fulfill the potential of wind power as a major source of clean energy in the future, higher-efficiency wind turbines and wind farms need to be designed. Therefore, as the premise of the wind farm layout optimization algorithms, accurate prediction of the wind turbine wakes and wake interactions is of great importance.

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