To validate this approach and also to quantify the error implied by ignoring the wind speed variability, the simulation results have been compared to measurement data from the SWiFT Benchmarks. Specifically, one of the 10-minutes measurement runs with neutral atmospheric conditions (number #00) was selected. To filter out the turbulence from the measured wind speed, the data was filtered using a 10 s window, and this filtered profile was used as input for the simulation (red curve “U input” in the plot at the top right corner of the animation). The blue curve “U with turbulence” shows the inflow velocity with the added synthetic turbulence. The position of the wind turbine blades is represented using an isosurface of the forcing term of the actuator line model. The variation of the wind turbine rotation speed with the incoming wind is also seen in the animation.

The effect of the variable inflow wind speed can be clearly seen when comparing the yellowish contour color at 250 s, when the wind speed is at its minimum, and the dark red color at 300 or 525 s, when the wind speed approaches its maximum. As it is expected, the turbine power follows the same trend, having its minimum value at 250 s and its maximum at 300 and 525 s.

More importantly, the simulation using a constant velocity profile, equivalent to the mean wind speed in this 10-minutes period, predicted a turbine power that is 3% below the measurements. Using the variable incoming wind speed the results improved significantly, and the predicted turbine power deviation dropped to around 1%. Both numbers appear to be low, but the fact that even a high fidelity simulation of a wind turbine’s power can carry a 2% error only by assuming a mean wind speed over a period of 10 minutes, as it is usual, is of great importance to the wind industry.