Bulletin of the American Physical Society
72nd Annual Meeting of the APS Division of Fluid Dynamics
Volume 64, Number 13
Saturday–Tuesday, November 23–26, 2019; Seattle, Washington
Session G42: Boundary Layers: Wind Turbine Interactions I |
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Chair: Nicholas Hamilton, National Renewable Energy Laboratory Room: 6e |
Sunday, November 24, 2019 3:48PM - 4:01PM |
G42.00001: Data Assimilation for the Prediction of Wake Trajectories Within Wind Farms Maxime Lejeune, Marion Coquelet, Nicolas Coudou, Maud Moens, Philippe Chatelain Wind turbine wake physics is by nature unsteady and highly sensitive to the local wind characteristics. While Large Eddy Simulations (LES) allow to accurately capture the flow at the wind farm scale for a wide range of atmospheric conditions, they still come at a prohibitive computational cost when it comes to online control. Based on the Dynamic Wake Meandering model, the presented model couples a meandering model and a wake speed deficit model in order to estimate the velocity field downstream the wind turbine. Focus is laid on limiting the number of input parameters by building on recent advances in flow sensing to feed the wake model with estimated inflow conditions. The remaining parameters are fine-tuned through online data assimilation techniques, thus adapting to inflow conditions. The performances of the resulting wake model are assessed using data recovered from high fidelity LES simulations. [Preview Abstract] |
Sunday, November 24, 2019 4:01PM - 4:14PM |
G42.00002: Analytical model for yawed turbine asymmetric wakes. Michele Guala, Bingzheng Dou, Liping Lei, Pan Zeng Due to large scale atmospheric variability and slow nacelle adjustments, wind turbines are predominantly operating in yawed conditions. In addition, to minimize turbine-turbine interactions and maximize spatially averaged energy production in wind power plant arrays, the yaw angle becomes a control variable for operators to steer the wake of front row units away from downwind rotors. Both mechanisms are expected to be relevant also for Marine Hydrokinetic (MHK) Turbines, experiencing large scale variability due to migrating bedforms in fluvial or tidal flows. We present a new wake model able to predict the location of the maximum velocity deficit, and the asymmetric distribution of the mean velocity about the wake center, for different downwind distances (Dou et al., 2019). The model has been calibrated using wind tunnel and open channel flow experiments with yawed miniature turbines of different shape, size and operating conditions. The key advantages of the proposed wake model are: i) the model requires only the thrust coefficient in un-yawed conditions, ii) the required parameter describing the wake expansion has been observed to be weakly depending on specific incoming flow condition, iii) the parameter describing the wake asymmetry can be estimated based on the yaw angle and the thrust coefficient. Thus, input parameters of the proposed model are for the most part limited to the turbine geometry, its operating conditions, and the associated thrust coefficient. [Preview Abstract] |
Sunday, November 24, 2019 4:14PM - 4:27PM |
G42.00003: A generalized top down model for Equivalent Roughness of Fully Developed Wind Farm Huan Zhang, Mingwei Ge, Yongqian Liu, Xiang I. A. Yang In mesoscale numerical simulations, wind farms are often parameterized as surface roughness using the top-down model. The top-down model (Phys. Fluids 22 (1), 46--56) works well for fully developed wind farms with moderate turbine spacing but its performance deteriorates for farms with large streamwise or spanwise turbine spacing. In the present study, we propose a parameterization to characterize flow non-uniformity at the hub height. This parameterization is subsequently incorporated in the top-down model, and solved by coupling the Jensen model and the wake layer model to match the mean flow from the two models at the hub-height. Predictions of our model are compared with other models and a suite of large-eddy simulation (LES) data. We find that the new model is able to predict the equivalent roughness height of fully developed wind farms more accurately than the conventional top-down model. [Preview Abstract] |
Sunday, November 24, 2019 4:27PM - 4:40PM |
G42.00004: On the evaluation of Taylor's frozen-flow hypothesis in modeling the meandering of turbine wakes Xiaolei Yang, Guowei He Fast and accurately predicting the meandering motion of turbine wakes is crucial to the design and control of large wind farms. Engineering wake models developed for the mean velocity deficit and the turbulence intensity cannot predict the meandering motion of turbine wakes. Large-eddy simulation (LES), which is able to predict the energetic coherent structures of turbine wakes, on the other hand, cannot be directly applied to the design of wind farms because of its high computational cost. To fast predict the meandering motion of turbine wakes, the dynamic wake meandering (DWM) model has been developed in the literature (Ris{\o} National Laboratory Technical Report, 2007, Ris{\o}-R-1607). In the DWM model, the meandering motion is taken into account by modeling wakes as passive scalars based on Taylor's frozen-flow hypothesis. To the best of our knowledge, the validity of Taylor's frozen-flow hypothesis for modeling wake meandering has not been fully evaluated. In this work, we evaluate Taylor's frozen-flow hypothesis using the LES data of turbine wakes. The space-time correlations are examined. The predictions from a simplified DWM model are compared with the LES results. [Preview Abstract] |
Sunday, November 24, 2019 4:40PM - 4:53PM |
G42.00005: Numerical Perspectives on Wind Turbine Wakes Rubel Das, Sang Lee Rapid advances in high-fidelity simulations of wind farms have been feasible due to a recent establishment of advanced atmospheric boundary layer modeling capability coupled with wind turbine models that mimic the turbine wakes behind the rotor. The wake modeling is an essential component of wind farm simulation as it informs the researchers on the dynamics of the fluid physics within the wind farms and their implications on the wind farm performance in terms of power production and longevity. Actuator disk and line methods are the mainstream empirical models that represent the presence of a wind turbine which generate the associated downstream velocity deficit fields. While with proper modifications the power predictions are in close agreement with actual data, there is often a mismatch in fluid dynamic behavior in the wake deficit region which we attempt to identify the dominant causes and seek possible remedies. [Preview Abstract] |
Sunday, November 24, 2019 4:53PM - 5:06PM |
G42.00006: Assessment of wake superposition models through wind tunnel tests and LiDAR measurements. Stefano Letizia, Lu Zhan, Emmanouil Nanos, Carlo Bottasso, Mario A. Rotea, Giacomo Valerio Iungo Wake superposition models have been developed for low-computational-cost estimates of the velocity field in the wake generated by a turbine affected by an upstream wake. For this work, data collected through two different experiments have been leveraged to assess accuracy of wake superposition models. The first set of measurements was collected at the BLAST wind tunnel of UT Dallas were two turbine models were tested for isolated operations and under occurrence of wake interactions. The second experiment consists in a LiDAR campaign performed for a wind farm in Colorado to collect velocity measurements in the wake of utility-scale wind turbines. Clustered LiDAR measurements have been analyzed to characterize single-wake evolution and wake velocity fields in presence of wake interactions. This study shows that accuracy of wake superposition models can be not sufficient for wind farm optimization and control studies. Therefore, for the mentioned-applications, more accurate models based on the solution of the Navier-Stokes equations, such as RANS models, are deemed necessary, yet entailing larger computational costs. [Preview Abstract] |
Sunday, November 24, 2019 5:06PM - 5:19PM |
G42.00007: Impact of utility-scale wind turbine wakes on surface fluxes Aliza Abraham, Jiarong Hong Added turbulence in the wake of a wind turbine can influence the flux of momentum, heat, and moisture at the ground surface, affecting agriculture and ecology in the surrounding environment. However, this effect is not well understood at the field scale because of the lack of techniques available to analyze this phenomenon in detail. In the current study, super-large-scale particle image velocimetry (SLPIV) using natural snowfall is employed with a field-of-view spanning from the ground to 49 m above and covering the region 87 m downwind of a utility-scale turbine. This dataset reveals strong interaction between coherent tip vortex structures in the wake and the ground surface. The strength of these interactions is found to depend on the turbine operation, including the tip-speed-ratio, blade pitch, and yaw error. Additionally, the effect of the interaction on the fluxes at the surface is quantified. This improved understanding of the physical phenomena causing this behavior provides insights into discrepancies between previously published field-scale observations of wind farm induced effects on surface fluxes. These effects can be considered in the future for the optimization of wind farm siting and operation with respect to impact on the environment and plant efficiency. [Preview Abstract] |
Sunday, November 24, 2019 5:19PM - 5:32PM |
G42.00008: UAS Swarming for Three-Dimensional Wake Measurements of Building and Turbine Wakes Jamey Jacob, Rakshit Allamraju, Taylor Mitchell, Victoria Natalie Three dimensional measurements of the wake upstream and downstream of buildings and turbines utilizing UAS are presented. Multirotor aircraft have been outfitted with sonic anemometers to measure windspeed and direction of winds aloft. Various studies have demonstrated using a fixed wing unmanned aircraft systems (UAS) to measure the wake of turbines. However, a novel approach is taken to measure multiple points simultaneously in the wake using highly coordinated and autonomous quadrotors UAS, aka swarming. The ability to perform such measurements is made possible due to the integration of fast response aerodynamic sensors with compact hardware that enables the UAS to be used in this operation. A proposed experimental testing scheme is to measure a three-dimensional profile of the boundary layers of wakes upstream and down of buildings and wind turbines. This insight provides a real-time full-scale measurements of the effects of building and turbines at high Re and determine the impacts of the boundary layer. The Oklahoma Mesonet and DOE ARM SGP Site are used for validation, and ground mounted ultrasonic anemometer is used for 3D validation of the wind vector estimates. Initial results are presented and discussed. [Preview Abstract] |
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