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.
Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.
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.
Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.
This paper describes the methodology of designing a replacement blade tip and winglet for a wind turbine blade to demonstrate the potential of additive-manufacturing for wind energy. The team will later field-demonstrate this additive-manufactured, system-integrated tip (AMSIT) on a wind turbine. The blade tip aims to reduce the cost of wind energy by improving aerodynamic performance and reliability, while reducing transportation costs. This paper focuses on the design and modeling of a winglet for increased power production while maintaining acceptable structural loads of the original Vestas V27 blade design. A free-wake vortex model, WindDVE, was used for the winglet design analysis. A summary of the aerodynamic design process is presented along with a case study of a specific design.
Turbine generator power from simulations using Actuator Line Models and Actuator Disk Models with a Filtered Lifting Line Correction are compared to field data of a V27 turbine. Preliminary results of the wake characteristics are also presented. Turbine quantities of interest from traditional ALM and ADM with the Gaussian kernel (ϵ) set at the optimum value for matching power production and that resolve the kernel at all mesh sizes are also presented. The atmospheric boundary layer is simulated using Nalu-Wind, a Large Eddy Simulation code which is part of the ExaWind code suite. The effect of mesh resolution on quantities of interest is also examined.
Advances in wind-plant control have often focused on more effectively balancing power between neighboring turbines. Wake steering is one such method that provides control-based improvements in a quasi-static way, but this does little to fundamentally change the wake recovery process, and thus, it has limited potential. This study investigates use of another control paradigm known as dynamic wake control (DWC) to excite the mutual inductance instability between adjacent tip-vortex structures, thereby accelerating the breakdown of the structures. The current work carries this approach beyond the hypothetical by applying the excitation via turbine control vectors that already exist on all modern wind turbines: blade pitch and rotor speed control. The investigation leverages a free-vortex wake method (FVWM) that allows a thorough exploration of relevant frequencies and amplitudes of harmonic forcing for each control vector (as well as the phase difference between the vectors for a tandem configuration) while still capturing the essential tip-vortex dynamics. The FVWM output feeds into a Fourier stability analysis working to pinpoint candidate DWC strategies suggesting fastest wake recovery. Near-wake length reductions of >80% are demonstrated, although without considering inflow turbulence. Analysis is provided to interpret these predictions considering the presence of turbulence in a real atmospheric inflow.
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.
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.