In this 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.