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Wake interactions between wind turbines have a significant impact on the performance of downstream turbines and the overall efficiency of wind farms. In this study, wind tunnel experiments were carried out to investigate the wake characteristics of multiple wind turbines under different inflow conditions, upstream yaw angles, and turbine arrangements. The applicability of a previously proposed blade optimization method for reduced-scale wind turbine wake experiments was further assessed, and several wake velocity superposition models were evaluated. The results indicate that inflow turbulence intensity has a greater influence on wake recovery than inflow velocity and that increased turbulence intensity accelerates wake mixing and velocity recovery. Moreover, an appropriate upstream yaw angle and a staggered turbine arrangement can alleviate the wake deficit experienced by the downstream turbine. Additionally, the experimental data confirm that the optimized blade design method is effective for multi-turbine wake experiments. Among the models considered, the geometric sum model shows the best agreement with the experimental data under non-yaw conditions with small turbine spacing. The present study provides useful reference data for wind farm layout optimization and wake model development.

As the scale of onshore wind farms are increasing, the influence of wake behavior on power production becomes increasingly significant. Wind turbines sittings in onshore wind farms should take terrain into consideration including height change and slope curvature. However, optimized wind turbine (WT) placement for onshore wind farms considering both topographic amplitude and wake interaction is realistic. In this paper, an approach for optimized placement of onshore wind farms considering the topography as well as the wake effect is proposed. Based on minimizing the levelized production cost (LPC), the placement of WTs was optimized considering topography and the effect of this on WTs interactions. The results indicated that the proposed method was effective for finding the optimized layout for uneven onshore wind farms. The optimization method is applicable for optimized placement of onshore wind farms and can be extended to different topographic conditions.

Autonomous aerial refueling is a typical close formation flight process, in which the tanker wake has a strong aerodynamic influence to the receiver. In order to develop the accurate aerodynamic models in refueling simulations and design the control laws for autonomous aerial refueling, the tanker wake effects on an Unmanned Aerial Vehicle (UAV) are investigated through ANSYS CFX 15.0. A simplified boom-equipped tanker and a tailless delta wing UAV (named ZD-X) are used in this study. The aerodynamic characteristics of ZD-X in single flight are calculated first and the results are used as the benchmark for comparison. Then the aerodynamic characteristics of ZD-X under the effects of tanker wake are calculated, the final results are given in incremental form to facilitate comparative analysis. Numerical results are obtained from the tanker and receiver at varying lateral, vertical and longitudinal spacings. It is observed that the tanker wake effects on the receiver mostly come from wingtip vortices of the tanker wing and horizontal tail, and the lateral and vertical spacings have significant effects on the aerodynamic characteristics of the receiver, while the longitudinal spacing has almost no effect.

Measurements taken before and after the commissioning of three wind farms reveal that the wind speeds just upstream of each wind farm decrease relative to locations farther away after the turbines are turned on. At a distance of two rotor diameters upstream, the average derived relative slowdown is 3.4%; at seven to ten rotor diameters upstream, the average slowdown is 1.9%. Reynolds-Averaged Navier-Stokes (RANS) simulations point to wind-farm-scale blockage as the primary cause of these slowdowns. Blockage effects also cause front row turbines to produce less energy than they each would operating in isolation. Wind energy prediction procedures in use today ignore this effect, resulting in an overprediction bias that pervades the entire wind farm.

As the worlds first floating wind farm, one of the main technical challenges in the design phase of Hywind Scotland was the uncertainty related to wake interaction effects between the floating wind turbines. In this paper, we address this challenge by presenting an analysis of full‐scale measurements from the Hywind Scotland wind farm. Measurements of the floaters' roll, pitch and yaw motions are presented, both in the form of statistical data and response spectra. The floater responses of a turbine in free wind and a turbine in wake are compared, and the influence of different atmospheric stabilities on the floater motions is investigated. Despite the large distance between the turbines of 9 rotor diameters, wake effects in the measured floater motions are observed at the downstream turbine. Furthermore, both the wake responses and the free wind responses show a clear dependency on atmospheric stability. However, overall motions are small for all wind speeds, showing that the design performs satisfactorily in both free wind and wake conditions.

Optimal wind farm locations require a strong and reliable wind resource and access to transmission lines. As onshore and offshore wind energy grows, preferred locations become saturated with numerous wind farms. An upwind wind farm generates ‘wake effects’ (decreases in downwind wind speeds) that undermine a downwind wind farm’s power generation and revenues. Here we use a diverse set of analysis tools from the atmospheric science, economic and legal communities to assess costs and consequences of these wake effects, focusing on a West Texas case study. We show that although wake effects vary with atmospheric conditions, they are discernible in monthly power production. In stably stratified atmospheric conditions, wakes can extend 50+ km downwind, resulting in economic losses of several million dollars over six years for our case study. However, our investigation of the legal literature shows no legal guidance for protecting existing wind farms from such significant impacts. Wakes from upwind wind farms can reduce energy generation at downwind farms. Here, using power production data and atmospheric simulations, researchers quantify the economic impacts of wakes, explain the physics of wake variability and highlight that no legal framework exists to protect downwind farms.

A Lagrangian framework is used to evaluate aerosol–cloud interactions in the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) for measurements taken at Graciosa Island in the Azores where a U.S. Department of Energy Atmosphere Radiation Measurement (ARM) site is located. This framework uses direct measurements of cloud condensation nuclei (CCN) concentration (instead of relying on satellite retrievals of aerosol optical depth) and incorporates a suite of ground-based ARM measurements, satellite retrievals, and meteorological reanalysis products that when applied to over a 1500 trajectories provides key insights into the evolution of low-level clouds and aerosol radiative forcing that is not feasible from a traditional Eulerian analysis framework. Significantly lower concentrations (40 %) of surface CCN concentration are measured when precipitation rates in 48 h back trajectories average above 1.2 mm d−1 in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product. The depletion of CCN concentration when precipitation rates are elevated is nearly twice as large in the ARM observations compared to E3SMv1 simulations. The model CCN concentration bias remains significant despite modifying the autoconversion and accretion rates in warm clouds. As the clouds in trajectories associated with larger surface-based CCN concentration advect away from Graciosa Island, they maintain higher values of droplet number concentrations (Nd) over multiple days in observations and E3SM simulations compared to trajectories that start with lower CCN concentrations. The response remains robust even after controlling for meteorological factors such as lower troposphere stability, the degree of cloud coupling with the surface, and island wake effects. E3SMv1 simulates a multi-day aerosol effect on clouds and a Twomey radiative effect that is within 30 % of the ARM and satellite observations. However, the mean cloud droplet concentration is more than 2–3 times larger than in the observations. While Twomey radiative effects are similar amongst autoconversion and accretion sensitivity experiments, the liquid water path and cloud fraction adjustments are positive when using a regression model as opposed to negative when using the present-day minus pre-industrial aerosol emissions approach. This result suggests that tuning the autoconversion and accretion alone is unlikely to produce the desired aerosol susceptibilities in E3SMv1.

This article introduces the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. In this article, we present the new data set which underwent regional tuning and performance validation against available observations and has data available from 2000 through, depending on the region, 2019–2022. We also provide a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404 .

Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine and (2) by changing the overall flow resistance in the farm and thus the so‐called farm blockage effect. To better predict these effects with low computational costs, we develop data‐driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient CT∗ as a function of turbine layout. We train the model using a multi‐fidelity Gaussian process (GP) regression with a combination of low (engineering wake model) and high‐fidelity (large eddy simulations) simulations of farms with different layouts and wind directions. A large set of low‐fidelity data speeds up the learning process and the high‐fidelity data ensures a high accuracy. The trained multi‐fidelity GP model is shown to give more accurate predictions of CT∗ compared to a standard (single‐fidelity) GP regression applied only to a limited set of high‐fidelity data. We also use the multi‐fidelity GP model of CT∗ with the two‐scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimization in the future.

Wind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.