Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

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.

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

On the Wind Turbine Wake and Forest Terrain Interaction

Future wind power developments may be located in complex topographic and harsh environments; forests are one type of complex terrain that offers untapped potential for wind energy. A detailed analysis of the unsteady interaction between wind turbines and the distinct boundary layers from those terrains is necessary to ensure optimized design, operation, and life span of wind turbines and wind farms. Here, laboratory experiments were carried to explore the interaction between the wake of a horizontal-axis model wind turbine and the boundary layer flow over forest-like canopies and the modulation of forest density in the turbulent exchange. The case of the turbine in a canonical boundary layer is included for selected comparison. The experiments were performed in a wind tunnel fully covered with tree models of height H/zhub≈0.36, where zhub is the turbine hub height, which were placed in a staggered pattern sharing streamwise and transverse spacing of Δx/dc=1.3 and 2.7, where dc is the mean crown diameter of the trees. Particle image velocimetry is used to characterize the incoming flow and three fields of view in the turbine wake within x/dT∈(2,7) and covering the vertical extent of the wake. The results show a significant modulation of the forest-like canopies on the wake statistics relative to a case without forest canopies. Forest density did not induce dominant effects on the bulk features of the wake; however, a faster flow recovery, particularly in the intermediate wake, occurred with the case with less dense forest. Decomposition of the kinematic shear stress using a hyperbolic hole in the quadrant analysis reveals a substantial effect sufficiently away from the canopy top with sweep-dominated events that differentiate from ejection-dominated observed in canonical boundary layers. The comparatively high background turbulence induced by the forest reduced the modulation of the rotor in the wake; the quadrant fraction distribution in the intermediate wake exhibited similar features of the associated incoming flow.