This work aims at assessing the performance of a tip‐loss correction for advanced actuator disk (AD) methods coupled to large eddy simulation and making this correction possible in a wind farm configuration. The classical Glauert tip‐loss factor, commonly used in the blade element momentum method, is added here to correct the tip and the root induced velocities at the rotor. However, it requires a reference upstream velocity, which is problematic to define in complex flows, such as in wind farms. A methodology is proposed here to infer an effective upstream velocity local to each disk element, based on the one‐dimensional momentum theory and using only the local data at the rotor. This estimation is verified through a set of simulations, leading to good results in spite of the crude assumptions of the one‐dimensional momentum theory. This AD supplemented with the tip‐loss correction is compared with a high fidelity vortex particle‐mesh method, through the simulations in uniform wind of a constant circulation wind turbine and of a more realistic machine, the NREL‐5MW rotor. The results show that the AD behavior is clearly improved by the addition of a tip‐loss factor and the potential errors on the effective upstream velocity estimation have a moderate impact on the tip‐loss correction.
In this paper, we formulate a physics-based surrogate wake model in the framework of online wind farm control. A flow sensing module is coupled with a wake model in order to predict the behavior of the wake downstream of a wind turbine based on its loads, wind probe data and operating settings. Information about the incoming flow is recovered using flow sensing techniques and then fed to the wake model, which reconstructs the wake based on this limited set of information. Special focus is laid on limiting the number of input parameters while keeping the computational cost low in order to facilitate the tuning procedure. Once calibrated, comparison with high-fidelity numerical results retrieved from Large Eddy Simulation (LES) of a wind farm confirms the good potential of the approach for online wake prediction within farms. The two approaches are further compared in terms of their wake center and time-averaged speed deficit predictions demonstrating good agreement in the process.
The present study aims at assessing the Actuator Disk (AD) method supplemented with an Individual Pitch Control (IPC) strategy, at a resolution appropriate for the Large Eddy Simulation of large wind farms. The IPC scheme is based on a state-of-the art individual pitch control, generalized to be applied to an AD approach. This procedure also requires an accurate recovery of the flapwise bending moment on each blade, which is not trivial for a disk-type model. In order to compute flapwise moments on each blade, blade trajectories are reproduced through the disk and the AD aerodynamic forces are interpolated onto these virtual blades at each time step. We verify the AD model with IPC in simulations of an isolated wind turbine, for different wind speeds and turbulence intensities, and in a configuration with two rotors. We compare the AD statistics with those obtained using an Actuator Line (AL) method. The comparison done in terms of equivalent moment shows that the AD and AL simulations produce very similar results.
This paper aims at the better understanding of the correlations between rotor loads and the wake meandering inside a wind farm. The flow rotor dynamics are simulated by means of Large Eddy Simulations coupled to Actuator Disk models. The wake meandering is captured through a centerline tracking, itself based on the available power in the flow. Two turbulence intensities are investigated, TIs = 6% and 10%. Results show that the yawing moment is very well correlated to the wake movement, even for wind turbines located deeper into the the wind farm. Correlations can also be found between the wake oscillation and the root bending moments. Wake signatures can also be identified from the amplitude of the high-frequency oscillations of the root bending moments; however, the correlation is sometimes less visible, and deviations can be correlated to wind events or wake decay.
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