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Register a new userWind Turbine Large-Eddy Simulations on Very Coarse Grid Resolutions using an Actuator Line Model
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In this work the accuracy of the Actuator Line Model (ALM) in Large Eddy Simulations of wind turbine flow is studied under the specific conditions of very coarse spatial resolutions. For finely-resolved conditions, it is known that ALM provides better accuracy compared to the standard Actuator Disk Model (ADM) without rotation. However, we show here that on very coarse resolutions, flow induction occurring at rotor scales can affect the predicted inflow angle and can adversely affect the ALM predictions. We first provide an illustration of coarse LES to reproduce wind tunnel measurements. The resulting flow predictions are good, but the challenges in predicting power outputs from the detailed ALM motivate more detailed analysis on a case with uniform inflow. We present a theoretical framework to compare the filtered quantities that enter the Large-Eddy Simulation equations as body forces with a scaling relation between the filtered and unfiltered quantities. The study aims to apply the theoretical derivation to the simulation framework and improve the current results for an ALM, especially in the near wake where the largest differences are observed.
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The atmospheric incoming flow of a wind turbine is intimately connected to its power production as well as its structural stability. Here we present an incoming flow measurement of a utility-scale turbine at the high spatio-temporal resolution, using super-large-scale particle image velocimetry (SLPIV) with natural snowflakes. The datasets include over a one-hour duration of incoming flow with a field of view of 85 m (vertical) x 40 m (streamwise) centered at 0.2 rotor diameter upstream of the turbine. The mean flow shows the presence of the induction zone and a distinct region with enhanced vertical velocity. Time series of nacelle sonic anemometer and SLPIV measured streamwise velocity outside the induction zone show generally matched trends with time-varying discrepancies potentially due to the induction effect and the flow acceleration around the nacelle. These discrepancies between the two signals, characterized by the sonic-SLPIV velocity ratio, is normally distributed and is less than unity 85% of the time. The velocity ratio first decreases with increasing wind speed up to around the rated speed of the turbine, then plateaus, and finally rises with a further increase in wind speed. With conditional sampling, the distribution of the velocity ratio shows that larger yaw error leads to an increase in both the mean and the spread of the distribution. Moreover, as the incident angle of the incoming flow changes from negative to positive (i.e. from pointing downward to upward), the velocity ratio first decreases as the angle approaches zero. With further increase of the incidence angle, the ratio then plateaus and fluctuations are augmented. Finally, our results show that the intensity of short-term velocity fluctuation has a limited impact on the sonic-SLPIV velocity ratio.
This paper provides a review of the general experimental methodology of snow-powered flow visualization and super-large-scale particle imaging velocimetry (SLPIV), the corresponding field deployments and major scientific findings from our work on a 2.5 MW utility-scale wind turbine at the Eolos field station. The field measurements were conducted to investigate the incoming flow in the induction zone and the near-wake flows from different perspectives. It has been shown that these snow-powered measurements can provide sufficient spatiotemporal resolution and fields of view to characterize both qualitatively and quantitatively the incoming flow, all the major coherent structures generated by the turbine (e.g., blade, nacelle and tower vortices, etc.) as well as the development and interaction of these structures in the near wake. Our work has further revealed several interesting behaviors of near-wake flows (e.g., wake contraction, dynamic wake modulation, and meandering and deflection of nacelle wake, etc.), and their connections with constantly-changing inflows and turbine operation, which are uniquely associated with utility-scale turbines. These findings have demonstrated that the near wake flows, though highly complex, can be predicted with substantial statistical confidence using SCADA and structural response information readily available from the current utility-scale turbines. Such knowledge can be potentially incorporated into wake development models and turbine controllers for wind farm optimization in the future.
Super-large-scale particle image velocimetry (SLPIV) using natural snowfall is used to investigate the influence of nacelle and tower generated flow structures on the near-wake of a 2.5 MW wind turbine at the EOLOS field station. The analysis is based on the data collected in a field campaign on March 12th, 2017, with a sample area of 125 m (vertical) x 70 m (streamwise) centred on the plane behind the turbine support tower. The SLPIV measurement provides the velocity field over the entire rotor span, revealing a region of accelerated flow around the hub caused by the reduction in axial induction at the blade roots. The in-plane turbulent kinetic energy field shows an increase in turbulence in the regions of large shear behind the blade tips and the hub, and a reduction in turbulence behind the tower where the large-scale turbulent structures in the boundary layer are broken up. Snow voids reveal coherent structures shed from the blades, nacelle, and tower. The hub wake meandering frequency is quantified and found to correspond to the vortex shedding frequency of an Ahmed body (St=0.06). Persistent hub wake deflection is observed and shown to be connected with the turbine yaw error. In the region below the hub, strong interaction between the tower- and blade-generated structures is observed. The temporal characteristics of this interaction are quantified by the co-presence of two dominant frequencies, one corresponding to the blade vortex shedding at the blade pass frequency and the other corresponding to tower vortex shedding at St=0.2. This study highlights the influence of the tower and nacelle on the behaviour of the near-wake, informing model development and elucidating the mechanisms that influence wake evolution.
MHD turbulence is likely to play an important role in several astrophysical scenarios where the magnetic Reynolds is very large. Numerically, these cases can be studied efficiently by means of Large Eddy Simulations, in which the computational resources are used to evolve the system only up to a finite grid size. The resolution is not fine enough to capture all the relevant small-scale physics at play, which is instead effectively modeled by a set of additional terms in the evolution equations, dubbed as sub-grid-scale model. Here we extend such approach, commonly used in non-relativistic/non-magnetic/incompressible fluid dynamics, applying the so-called gradient model to a general set of balance-law equations, that includes the relevant case in which a non-trivial inversion of conserved to primitive fields is needed. In particular, we focus on the relativistic compressible ideal MHD scenario, providing for the first time (and for any equation of state) all the additional sub-grid-scale terms. As an application, we consider box simulations of the relativistic Kelvin-Helmholtz instability, which is also the first mechanism responsible for the magnetic field amplification in binary neutron star mergers and cannot yet be fully captured by the finest-grid and longest simulations available. The performance of our model is numerically assessed by comparing it to the residuals arising from the filtering of high-resolution simulations. We find that the model can fit very well those residuals from resolutions a few times higher. Although the application shown here explicitly considers the Minkowski metric, it can be directly extended to general relativity, thus settling the basis to implement the gradient sub-grid model in a GRMHD binary merger. Our results suggest that this approach will be potentially able to unveil much better the small-scale dynamics achievable in full GRMHD simulations.
The present investigation provides the first field characterization of the influence of turbulent inflow on the blade structural response of a utility-scale wind turbine (2.5MW), using the unique facility available at the Eolos Wind Energy Research Station of the University of Minnesota. A representative one-hour dataset under a stable atmosphere is selected for the characterization, including the inflow turbulent data measured from the meteorological tower, high-resolution blade strain measurement at different circumferential and radiation positions along the blade, and the wind turbine operational conditions. The results indicate that the turbulent inflow modulates the turbine blade structural response in three representative frequency ranges: a lower frequency range (corresponding to modulations due to large eddies in the atmosphere), a higher frequency range (corresponding to flow structures in scales smaller than the rotor diameter), and an intermediate-range in between. The blade structure responds strongly to the turbulent inflow in the lower and intermediate ranges, while it is primarily dominated by the rotation effect and other high-frequency characteristics of wind turbines in the higher frequency range. Moreover, the blade structural behaviors at different azimuth angles, circumferential and radial locations along the blade are also compared, suggesting the comparatively high possibility of structural failure at certain positions. Further, the present study also uncovers the linkage between the turbulent inflow and blade structural response using temporal correlation. The derived findings provide insights into the development of advanced control strategies or blade design to mitigate the structural impact and increase blade longevity for the safer and more efficient operation of large-scale wind turbines.
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