A novel superposition method for streamwise turbulence intensity of wind-turbine wakes

The prediction of the streamwise turbulence intensity distribution in a wind farm is of great significance for wind turbine micrositing. However, most studies only focus on the superposition methods of velocity deficit in turbine wakes, and high-precision superposition models for turbulence intensity are still lacking. To address this issue, wakes of a column of aligned wind turbines with different ground roughness heights are studied via high-fidelity large-eddy simulation. It is found that the variation of added streamwise turbulence intensity with streamwise direction is very similar to that of velocity deficit; however, there are distinct differences regarding recovery rate and incidence. Four common superposition methods extended from wake velocity are examined for turbulence intensity, and they all fail to accurately predict the superposition effect of turbulence intensity. To remedy this, a normalized superposition formula of turbulence intensity is proposed. In this formula, the parameter can be adjusted to alter the superposition ratio for downstream turbines, and it can encompass existing common superposition models. Both large-eddy simulations and wind tunnel experiments demonstrate that the 2.5 times superposition (model parameter = 2.5) method can accurately predict the distribution of streamwise turbulence intensity for aligned turbines, providing superior prediction accuracy compared to existing models.