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Summary: Over the last decades, pulsed light detection and ranging (LiDAR) anemometry has gained growing attention in probing the marine atmospheric boundary layer (MABL) due to its ease of use combined with compelling spatio‐temporal resolution. Among several scanning strategies, fixed scans represent the most prominent choice when high‐frequency resolution is required; however, no information is provided about the spatial heterogeneity of the wind field. On the other hand, volumetric scans allow for the characterization of the spatial variability of the wind field with much lower temporal resolution than fixed scans. In this work, the recently developed "LiDAR Statistical Barnes Objective Analysis" (LiSBOA) algorithm for the optimal design of LiDAR scans and retrieval of wind velocity statistics is tailored for applications in the MABL. The LiDAR data, collected during a recent experimental campaign over Lake Lavon in Texas, show a good consistency of mean velocity profiles between fixed and LiSBOA‐interpolated volumetric data, thus further encouraging the use of coupled fixed and volumetric scans for simultaneous characterizations of wind turbulence statistics along the vertical direction and volumetric heterogeneity of the wind field. [ABSTRACT FROM AUTHOR]

The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models. [ABSTRACT FROM AUTHOR]

The American WAKE ExperimeNt (AWAKEN) is a multi-institutional field campaign focused on gathering critical observations of wind farm–atmosphere interactions. These interactions are responsible for a large portion of the uncertainty in wind plant modeling tools that are used to represent wind plant performance both prior to construction and during operation and can negatively impact wind energy profitability. The AWAKEN field campaign will provide data for validation, ultimately improving modeling and lowering these uncertainties. The field campaign is designed to address seven testable hypotheses through the analysis of the observations collected by numerous instruments at 13 ground-based locations and on five wind turbines. The location of the field campaign in Northern Oklahoma was chosen to leverage existing observational facilities operated by the U.S. Department of Energy Atmospheric Radiation Measurement program in close proximity to five operating wind plants. The vast majority of the observations from the experiment are publicly available to researchers and industry members worldwide, which the authors hope will advance the state of the science for wind plants and lead to lower cost and increased reliability of wind energy systems. [ABSTRACT FROM AUTHOR]

Neglecting the velocity reduction in the induction zone of wind turbines can lead to overestimates of power production predictions, and, thus, of the annual energy production for a wind farm. An experimental study on the induction zone associated with wind turbine operations is performed in the boundary‐layer test section of the BLAST wind tunnel at UT Dallas using stereo particle image velocimetry. This experiment provides a detailed quantification of the wind speed decrease associated with the induction zone for two different incoming flows, namely, uniform flow and boundary layer flow. Operations of wind turbines in different regions of the power curve are modeled in the wind tunnel environment with two porous disks with a solidity of 50.4% and 32.3%, which correspond to thrust coefficients of 0.71 and 0.63, respectively. The porous disks are designed to approximate the wake velocity profiles previously measured for utility‐scale wind turbines through scanning wind LiDARs. The results show that the streamwise velocity at one rotor diameter upwind of both disks decreases 1% more for the boundary layer flow than for the uniform flow. Further, the effect of shear in front of the disk with a higher thrust coefficient can be observed until 1.75 rotor diameter upwind of the disk, whereas for the disk with a lower thrust coefficient, the effect of shear becomes negligible at 1.25 rotor diameter upwind. It is found that at one rotor diameter upwind, for both incoming flows, the disk having a higher thrust coefficient has 2% more velocity reduction than the lower‐thrust‐coefficient disk. The results suggest that the variability in wind shear and rotor thrust coefficient, which is encountered during typical operations of wind turbines, should be considered for the development of improved models for predictions of the rotor induction zone, the respective cumulative effects in the presence of multiple turbines, namely, wind farm blockage, and more accurate predictions of wind farm power capture. [ABSTRACT FROM AUTHOR]

Quantification of the performance degradation on the annual energy production (AEP) of a wind farm due to leading‐edge (LE) erosion of wind turbine blades is important to design cost‐effective maintenance plans and timely blade retrofit. In this work, the effects of LE erosion on horizontal axis wind turbines are quantified using infrared (IR) thermographic imaging of turbine blades, as well as meteorological and SCADA data. The average AEP loss of turbines with LE erosion is estimated from SCADA and meteorological data to be between 3% and 8% of the expected power capture. The impact of LE erosion on the average power capture of the turbines is found to be higher at lower hub‐height wind speeds (peak around 50% of the turbine rated wind speed) and at lower turbulence intensity of the incoming wind associated with stable atmospheric conditions. The effect of LE erosion is investigated with IR thermography to identify the laminar to turbulent transition (LTT) position over the airfoils of the turbine blades. Reduction in the laminar flow region of about 85% and 87% on average in the suction and pressure sides, respectively, is observed for the airfoils of the investigated turbines with LE erosion. Using the observed LTT locations over the airfoils and the geometry of the blade, an average AEP loss of about 3.7% is calculated with blade element momentum simulations, which is found to be comparable with the magnitude of AEP loss estimated through the SCADA data. [ABSTRACT FROM AUTHOR]

Continuous advancements in LiDAR technology have enabled compelling wind turbulence measurements within the atmospheric boundary layer with range gates shorter than 20m and sampling frequency of the order of 10Hz. However, estimates of the radial velocity from the back-scattered laser beam are inevitably affected by an averaging process within each range gate, generally modeled as a convolution between the actual velocity projected along the LiDAR line-of-sight and a weighting function representing the energy distribution of the laser pulse along the range gate. As a result, the spectral energy of the turbulent velocity fluctuations is damped within the inertial sub-range with respective reduction of the velocity variance, and, thus, not allowing to take advantage of the achieved spatio-temporal resolution of the LiDAR technology. In this article, we propose to correct this turbulent energy damping on the LiDAR measurements by reversing the effect of a low-pass filter, which can be estimated directly from the LiDAR measurements. LiDAR data acquired from three different field campaigns are analyzed to describe the proposed technique, investigate the variability of the filter parameters and, for one dataset, assess the procedure for spectral LiDAR correction against sonic anemometer data. It is found that the order of the low-pass filter used for modeling the energy damping on the LiDAR velocity measurements has negligible effects on the correction of the second-order statistics of the wind velocity. In contrast, its cutoff frequency plays a significant role in the spectral correction encompassing the smoothing effects connected with the LiDAR gate length. [ABSTRACT FROM AUTHOR]

A numerical framework for simulations of wake interactions associated with a wind turbine column is presented. A Reynolds-averaged Navier-Stokes (RANS) solver is developed for axisymmetric wake flows using parabolic and boundary-layer approximations to reduce computational cost while capturing the essential wake physics. Turbulence effects on downstream evolution of the time-averaged wake velocity field are taken into account through Boussinesq hypothesis and a mixing length model, which is only a function of the streamwise location. The calibration of the turbulence closure model is performed through wake turbulence statistics obtained from large-eddy simulations of wind turbine wakes. This strategy ensures capturing the proper wake mixing level for a given incoming turbulence and turbine operating condition and, thus, accurately estimating the wake velocity field. The power capture from turbines is mimicked as a forcing in the RANS equations through the actuator diskmodel with rotation. The RANS simulations of the wake velocity field associated with an isolated 5-MW NREL wind turbine operating with different tip speed ratios and turbulence intensity of the incoming wind agree well with the analogous velocity data obtained through high-fidelity large-eddy simulations. Furthermore, different cases of columns of wind turbines operating with different tip speed ratios and downstream spacing are also simulated with great accuracy. Therefore, the proposed RANS solver is a powerful tool for simulations of wind turbine wakes tailored for optimization problems, where a good trade-off between accuracy and low-computational cost is desirable. [ABSTRACT FROM AUTHOR]