Experiments offer incredible value to science, but results must always come with an uncertainty quantification to be meaningful. This requires grappling with sources of uncertainty and how to reduce them. In wind energy, field experiments are sometimes conducted with a control and treatment. In this scenario uncertainty due to bias errors can often be neglected as they impact both control and treatment approximately equally. However, uncertainty due to random errors propagates such that the uncertainty in the difference between the control and treatment is always larger than the random uncertainty in the individual measurements if the sources are uncorrelated. As random uncertainties are usually reduced with additional measurements, there is a need to know the minimum duration of an experiment required to reach acceptable levels of uncertainty. We present a general method to simulate a proposed experiment, calculate uncertainties, and determine both the measurement duration and the experiment duration required to produce statistically significant and converged results. The method is then demonstrated as a case study with a virtual experiment that uses real-world wind resource data and several simulated tip extensions to parameterize results by the expected difference in power. With the method demonstrated herein, experiments can be better planned by accounting for specific details such as controller switching schedules, wind statistics, and postprocess binning procedures such that their impacts on uncertainty can be predicted and the measurement duration needed to achieve statistically significant and converged results can be determined before the experiment.
A reduction in wake effects in large wind farms through wake-aware control has considerable potential to improve farm efficiency. This work examines the success of several emerging, empirically derived control methods that modify wind turbine wakes (i.e., the pulse method, helix method, and related methods) based on Strouhal numbers on the (Formula presented.). Drawing on previous work in the literature for jet and bluff-body flows, the analyses leverage the normal-mode representation of wake instabilities to characterize the large-scale wake meandering observed in actuated wakes. Idealized large-eddy simulations (LES) using an actuator-line representation of the turbine blades indicate that the (Formula presented.) and (Formula presented.) modes, which correspond to the pulse and helix forcing strategies, respectively, have faster initial growth rates than higher-order modes, suggesting these lower-order modes are more appropriate for wake control. Exciting these lower-order modes with periodic pitching of the blades produces increased modal growth, higher entrainment into the wake, and faster wake recovery. Modal energy gain and the entrainment rate both increase with streamwise distance from the rotor until the intermediate wake. This suggests that the wake meandering dynamics, which share close ties with the relatively well-characterized meandering dynamics in jet and bluff-body flows, are an essential component of the success of wind turbine wake control methods. A spatial linear stability analysis is also performed on the wake flows and yields insights on the modal evolution. In the context of the normal-mode representation of wake instabilities, these findings represent the first literature examining the characteristics of the wake meandering stemming from intentional Strouhal-timed wake actuation, and they help guide the ongoing work to understand the fluid-dynamic origins of the success of the pulse, helix, and related methods.
A large-scale numerical computation of five wind farms was performed as a part of the American WAKE experimeNt (AWAKEN). This high-fidelity computation used the ExaWind/AMR-Wind LES solver to simulate a 100 km × 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements. The turbines were represented by Joukowski and OpenFAST coupled actuator disk models. Results of this qualitative comparison illustrate the interactions of wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.
The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.
In this study we performed detailed comparisons of numerical computations of single turbine wakes with measured data under neutral and stable atmospheric stability conditions. LES of the ABL inflow and turbine wakes are carried out using the ExaWind/Nalu-Wind simulation codes and compared with the equivalent measurements from the SWiFT research facility at wind speeds of 8.7 m/s and 4.8 m/s. The computed ABL inflow profiles and spectra showed good agreement with measured data in both stratification conditions, and the simulated turbine power and rotor speed also agreed with the measured turbine performance. A comparison of the downstream wake deficit profiles and turbulence distributions with lidar observations also showed that the LES computations generally captured the wake evolution in both neutral and stable conditions, with some possible discrepancies due to uncertainty around the turbine thrust and yaw settings. Finally, an examination of the downstream turbulence spectra showed that the peak frequency of the wake added turbulence corresponds to the characteristic wake shedding frequency, and we show that the turbulent integral lengthscale in the wake region also decreases significantly due to the presence of smaller turbulent features.
Wind turbine wakes are characterized by helical trailing tip vortices that are highly stable initially and act as a shield against mixing with the ambient flow and thereby delay wake recovery until destructive mutual interference of the vortices begins. Delayed wake recovery in turn reduces the power production of downstream turbines that are positioned in the wakes of upstream turbines. The long natural decay length forces wind farms to have large distances between turbines to yield sufficient wake recovery. Herein, we tested a new concept aimed at accelerating the breakdown of wind turbine tip vortices by causing the vortices to interact with one another almost immediately behind the rotor. By adding a spire behind the rotor, essentially a blockage to perturb the paths of the tip vortices, we hypothesized that the altered paths of the tip vortices would cause their destructive interference process to begin sooner. The concept of a nacelle-mounted spire was tested in high-fidelity large-eddy simulations using Nalu-Wind. Four different spires were modeled with wall-resolved meshes behind the rotor of a wind turbine with another turbine five diameters downstream. We compared power and wake data against baseline results to determine whether the spires accelerated wake recovery of the upstream turbine and thereby increased the power of the downstream turbine. The results showed no change in the total power of the two turbines for any spire compared to its respective baseline. These results were further explored by testing at higher spatial resolution and without turbulence in the inflow. The increased spatial resolution increased the apparent stability of the tip vortices while the lack of turbulence did not. We conclude that the spires’ geometry and size were inadequate to alter the helical paths of the trailing tip vortices and that modeling of the formation and decay of tip vortices may be highly sensitive to model parameters.
Wind turbine wakes are characterized by helical trailing tip vortices that are highly stable initially and act as a shield against mixing with the ambient flow and thereby delay wake recovery until destructive mutual interference of the vortices begins. Delayed wake recovery in turn reduces the power production of downstream turbines that are positioned in the wakes of upstream turbines. The long natural decay length forces wind farms to have large distances between turbines to yield sufficient wake recovery. Herein, we tested a new concept aimed at accelerating the breakdown of wind turbine tip vortices by causing the vortices to interact with one another almost immediately behind the rotor. By adding a spire behind the rotor, essentially a blockage to perturb the paths of the tip vortices, we hypothesized that the altered paths of the tip vortices would cause their destructive interference process to begin sooner. The concept of a nacelle-mounted spire was tested in high-fidelity large-eddy simulations using Nalu-Wind. Four different spires were modeled with wall-resolved meshes behind the rotor of a wind turbine with another turbine five diameters downstream. We compared power and wake data against baseline results to determine whether the spires accelerated wake recovery of the upstream turbine and thereby increased the power of the downstream turbine. The results showed no change in the total power of the two turbines for any spire compared to its respective baseline. These results were further explored by testing at higher spatial resolution and without turbulence in the inflow. The increased spatial resolution increased the apparent stability of the tip vortices while the lack of turbulence did not. We conclude that the spires’ geometry and size were inadequate to alter the helical paths of the trailing tip vortices and that modeling of the formation and decay of tip vortices may be highly sensitive to model parameters.
The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University's National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.