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This study compared the efficacy of the actuator line and actuator surface models in carrying out large-eddy simulations of a utility-scale wind turbine. A large-eddy simulation with the actuator surface and line models was employed to study the wake flow and power production of the turbine. While both the actuator models were employed for the blade representation, the nacelle was modeled using the actuator surface approach. Both of the actuator models demonstrated agreement in the mean velocity field, power production, and turbulence kinetic energy of the wake flow. Comparing the wake flow, power production, and turbulence kinetic energy results, it was found that the mean discrepancy between the two models was 0.6 % , 0.3 % , and 2.3 % , respectively. Despite the minor discrepancies, both actuator models accurately captured the hub vortex in the wake of the nacelle, evidenced by an energy peak in wind speed spectra at f / f ω ≈ 0.34 . [ABSTRACT FROM AUTHOR]

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES. [ABSTRACT FROM AUTHOR]