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LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes
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Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive and limited to specific coordinates. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.more » « less
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This study presents the first 3D two-way coupled fluid structure interaction (FSI) simulation of a hybrid anechoic wind tunnel (HAWT) test section with modeling all important effects, such as turbulence, Kevlar wall porosity and deflection, and reveals for the first time the complete 3D flow structure associated with a lifting model placed into a HAWT. The Kevlar deflections are captured using finite element analysis (FEA) with shell elements operated under a membrane condition. Three-dimensional RANS CFD simulations are used to resolve the flow field. Aerodynamic experimental results are available and are compared against the FSI results. Quantitatively, the pressure coefficients on the airfoil are in good agreement with experimental results. The lift coefficient was slightly underpredicted while the drag was overpredicted by the CFD simulations. The flow structure downstream of the airfoil showed good agreement with the experiments, particularly over the wind tunnel walls where the Kevlar windows interact with the flow field. A discrepancy between previous experimental observations and juncture flow-induced vortices at the ends of the airfoil is found to stem from the limited ability of turbulence models. The qualitative behavior of the flow, including airfoil pressures and cross-sectional flow structure is well captured in the CFD. From the structural side, the behavior of the Kevlar windows and the flow developing over them is closely related to the aerodynamic pressure field induced by the airfoil. The Kevlar displacement and the transpiration velocity across the material is dominated by flow blockage effects, generated aerodynamic lift, and the wake of the airfoil. The airfoil wake increases the Kevlar window displacement, which was previously not resolved by two-dimensional panel-method simulations. The static pressure distribution over the Kevlar windows is symmetrical about the tunnel mid-height, confirming a dominantly two-dimensional flow field.more » « less
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Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/we.2430
