Search for: All records

Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale (𝒪(mm)) up to large energy-containing structures (𝒪(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system.

 

Abstract A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions. 

A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of the velocity statistical moments is proposed. LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional space, and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning lidars. LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for the optimal design of lidar experiments and efficient application of LiSBOA for the postprocessing of lidar measurements. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the lidar scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme. LiSBOA is assessed against a numerical data set generated using the virtual lidar technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind lidar are retrieved through LiSBOA, and then the estimated statistics are compared with those of the original LES data set, showing a maximum error of about 4 % for both mean velocity and turbulence intensity. 

Abstract. The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in Letizia et al. (2021), is a procedure for the optimal design of lidar scans and calculations over a Cartesian grid of the statistical moments of the velocity field. Lidar data collected during a field campaign conducted at a wind farm in complex terrain are analyzed through LiSBOA for two different tests. For both case studies, LiSBOA is leveraged for the optimization of the azimuthal step of the lidar and the retrieval of the mean equivalent velocity and turbulence intensity fields. In the first case, the wake velocity statistics of four utility-scale turbines are reconstructed on a 3D grid, showing LiSBOA's ability to capture complex flow features, such as high-speed jets around the nacelle and the wake turbulent-shear layers. For the second case, the statistics of the wakes generated by four interacting turbines are calculated over a 2D Cartesian grid and compared to the measurements provided by the nacelle-mounted anemometers. Maximum discrepancies, as low as 3 % for the mean velocity (with respect to the free stream velocity) and turbulence intensity (in absolute terms), endorse the application of LiSBOA for lidar-based wind resource assessment and diagnostic surveys for wind farms. 

Abstract. A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of the velocity statistical moments is proposed. LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional space, and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning lidars. LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for the optimal design of lidar experiments and efficient application of LiSBOA for the postprocessing of lidar measurements. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the lidar scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme. LiSBOA is assessed against a numerical data set generated using the virtual lidar technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind lidar are retrieved through LiSBOA, and then the estimated statistics are compared with those of the original LES data set, showing a maximum error of about 4 % for both mean velocity and turbulence intensity. 

Wind turbine wakes are responsible for power losses and added fatigue loads of wind turbines. Providing capabilities to predict accurately wind-turbine wakes for different atmospheric conditions and turbine settings with low computational requirements is crucial for the optimization of wind-farm layout, and for improving wind-turbine controls aiming to increase annual energy production (AEP) and reduce the levelized cost of energy (LCOE) for wind power plants. In this work, wake measurements collected with a scanning Doppler wind Li- DAR for broad ranges of the atmospheric static stability regime and incoming wind speed are processed through K-means clustering. For computational feasibility, the cluster analysis is performed on a low-dimensional embedding of the collected data, which is obtained through proper orthogonal decomposition (POD). After data compression, we perform K-means of the POD modes to identify cluster centers and corresponding members from the LiDAR data. The different cluster centers allow us to visualize wake variability over ranges of atmospheric, wind, and turbine parameters. The results show that accurate mapping of the wake variability can be achieved with K-means clustering, which represents an initial step to develop data-driven wake models for accurate and low-computational-cost simulations of wind farms. 

Abstract. Engineering wake models provide the invaluable advantage to predict wind turbine wakes, power capture, and, in turn, annual energy production for an entire wind farm with very low computational costs compared to higher-fidelity numerical tools. However, wake and power predictions obtained with engineering wake models can be insufficiently accurate for wind farm optimization problems due to the ad hoc tuning of the model parameters, which are typically strongly dependent on the characteristics of the site and power plant under investigation. In this paper, lidar measurements collected for individual turbine wakes evolving over a flat terrain are leveraged to perform optimal tuning of the parameters of four widely used engineering wake models. The average wake velocity fields, used as a reference for the optimization problem, are obtained through a cluster analysis of lidar measurements performed under a broad range of turbine operative conditions, namely rotor thrust coefficients, and incoming wind characteristics, namely turbulence intensity at hub height. The sensitivity analysis of the optimally tuned model parameters and the respective physical interpretation are presented. The performance of the optimally tuned engineering wake models is discussed, while the results suggest that the optimally tuned Bastankhah and Ainslie wake models provide very good predictions of wind turbine wakes. Specifically, the Bastankhah wake model should be tuned only for the far-wake region, namely where the wake velocity field can be well approximated with a Gaussian profile in the radial direction. In contrast, the Ainslie model provides the advantage of using as input an arbitrary near-wake velocity profile, which can be obtained through other wake models, higher-fidelity tools, or experimental data. The good prediction capabilities of the Ainslie model indicate that the mixing-length model is a simple yet efficient turbulence closure to capture effects of incoming wind and wake-generated turbulence on the wake downstream evolution and predictions of turbine power yield.