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Register a new userOperational-dependent wind turbine wake impact on surface momentum flux revealed by snow-powered flow imaging
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As wind energy continues to expand, increased interaction between wind farms and their surroundings can be expected. Using natural snowfall to visualize the air flow in the wake of a utility-scale wind turbine at unprecedented spatio-temporal resolution, we observe intermittent periods of strong interaction between the wake and the ground surface and quantify the momentum flux during these periods. Significantly, we identify two turbine operational-dependent pathways that lead to these periods of increased wake-ground interaction. Data from a nearby meteorological tower provides further insights into the strength and persistence of the enhanced flux for each pathway under different atmospheric conditions. These pathways allow us to resolve discrepancies between previous conflicting studies on the impact of wind turbines on surface fluxes. Furthermore, we use our results to generate a map of the potential impact of wind farms on surface momentum flux throughout the Continental United States, providing a valuable resource for wind farm siting decisions. These findings have implications for agriculture in particular, as crop growth is significantly affected by surface fluxes.
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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.
Several theories for weakly damped free-surface flows have been formulated. In this paper we use the linear approximation to the Navier-Stokes equations to derive a new set of equations for potential flow which include dissipation due to viscosity. A viscous correction is added not only to the irrotational pressure (Bernoullis equation), but also to the kinematic boundary condition. The nonlinear Schrodinger (NLS) equation that one can derive from the new set of equations to describe the modulations of weakly nonlinear, weakly damped deep-water gravity waves turns out to be the classical damped version of the NLS equation that has been used by many authors without rigorous justification.
Understanding wind turbine wake mixing and recovery is critical for improving the power generation and structural stability of downwind turbines in a wind farm. In the field, where incoming flow and turbine operation are constantly changing, wake recovery can be significantly influenced by dynamic wake modulation, which has not yet been explored. Here we present the first investigation of dynamic wake modulation in the near wake of a utility-scale turbine and quantify its relationship with changing conditions. This investigation is enabled using novel super-large-scale flow visualization with natural snowfall, providing unprecedented spatiotemporal resolution to resolve instantaneous changes of the wake envelope. These measurements reveal the significant influence of dynamic wake modulation on wake recovery. Further, our study uncovers the direct connection of dynamic wake modulation with operational parameters readily available to the turbine, paving the way for more precise wake prediction and control under field conditions for wind farm optimization.
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.
Conventionally neutral atmospheric boundary layers (CNBLs), which are characterized with zero surface potential temperature flux and capped by an inversion of potential temperature, are frequently encountered in nature. Therefore, predicting the wind speed profiles of CNBLs is relevant for weather forecasting, climate modeling, and wind energy applications. However, previous attempts to predict the velocity profiles in CNBLs have had limited success due to the complicated interplay between buoyancy, shear, and Coriolis effects. Here, we utilize ideas from the classical Monin-Obukhov similarity theory in combination with a local scaling hypothesis to derive an analytic expression for the stability correction function $psi = -c_psi (z/L)^{1/2}$, where $c_psi = 4.2$ is an empirical constant, $z$ is the height above ground, and $L$ is the local Obukhov length based on potential temperature flux at that height, for CNBLs. An analytic expression for this flux is also derived using dimensional analysis and a perturbation method approach. We find that the derived profile agrees excellently with the velocity profile in the entire boundary layer obtained from high-fidelity large eddy simulations of typical CNBLs.
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