• Energy Research
• 2025-2025close
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Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.

Abstract. This study investigates the potential of regenerative wind farming using multirotor systems equipped with paired multirotor-sized wings, termed atmospheric boundary layer control (ABL-control) devices, positioned in the near-wake region of the multirotor. These ABL-control devices generate vortical flow structures that enhance vertical momentum flux from the flow above the wind farm into the wind farm flow, thereby accelerating the wake recovery process. This work presents numerical assessments of a single multirotor system equipped with various ABL-control configurations. The wind flow is modeled using steady-state Reynolds-averaged Navier–Stokes (RANS) computations, with the multirotor and ABL-control devices represented by three-dimensional actuator surface models based on momentum theory. Force coefficient data for the actuator surface models, as well as validation data for the numerical computations, were obtained from a scaled model at TU Delft's Open Jet Facility. The performance of the ABL-control devices was evaluated by analyzing the net momentum entrained from the flow above the wind farm and the total pressure and power available in the wake. The results indicate that, when the ABL-control strategy is employed, vertical momentum flux may become the dominant mechanism for wake recovery. In configurations with two or four ABL-control wings, the total wind power in the wake recovers to 95 % of the free-stream value at positions as early as x/D≈6 downstream of the multirotor system, representing a recovery rate that is approximately an order of magnitude faster than that observed in the baseline wake without ABL-control capabilities. It should be noted, however, that this study employs a simplified numerical setup to provide a proof of concept, and the current findings are not yet directly applicable to real-world scenarios.

Abstract. The development of large wind turbines and airborne wind energy (AWE) systems requires reliable wind speed datasets at heights above the atmospheric surface layer. Traditional measurement approaches relying on met masts (meteorological masts) fall short of addressing these needs. In this study, we validate three different model-based datasets, namely the 3 km Norwegian Hindcast archive (NORA3), the New European Wind Atlas (NEWA), and ECMWF Reanalysis v5 (ERA5), using Doppler wind lidar data from several locations in Norway and the North Sea. The validation focuses on altitudes from 100 to 500 m above ground, covering the operational range of large wind turbines and AWE systems. Our findings indicate that ERA5 and NORA3 perform similarly well in offshore locations in terms of bias, correlation coefficient, root-mean-square error, and Earth mover's distance. The choice of an appropriate wind speed database depends on the topography, altitude and error metrics of interest. However, NORA3 outperforms the other two models in two coastal sites and one complex-terrain site. In most cases, the agreement between the models and lidar measurements increases with height.

Abstract. The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

Abstract. The operational management of offshore wind farms includes inspection and maintenance (I&M) of the wind turbine support structures. These activities are complex and influenced by numerous uncertain factors that affect their costs. The uncertainty in the I&M costs should be considered in decision value analyses performed to optimize I&M strategies for the turbine support structures. In this paper, we formulate a probabilistic parametric model to describe I&M costs for the common case in which a wind farm is serviced and maintained using a workboat-based strategy. The model is developed based on (a) interviews with a wind farm operator, engineering consultants, and operation and maintenance engineers, as well as (b) scientific literature. Our methodology involves deriving the probabilistic models of the cost model parameters based on intervals representing a subjective expert opinion on the foreseeable ranges of the parameter values. The probabilistic cost model is applied to evaluate the total I&M costs, and a sensitivity analysis is conducted to identify the main cost drivers. The model can be utilized to optimize I&M strategies at the component, structural system, and wind farm level. To illustrate its potential use, we apply it in a numerical study in which we optimize I&M strategies at the structural system level and identify and demonstrate a simplified approach of capturing uncertain I&M costs in the optimization. The simplified approach is generalized and made available for maintenance cost optimization of offshore wind turbine structures.

Abstract. This study investigates the relationship between sound quality metrics (SQMs) and noise annoyance caused by airborne wind energy systems (AWESs). In a controlled listening experiment, 75 participants rated their annoyance on the International Commission on Biological Effects of Noise (ICBEN) scale in response to recordings from in-field measurements of two fixed-wing and one soft-wing ground-generation AWES. All recordings were normalized to an equivalent A-weighted sound pressure level of 45 dBA. The results revealed that sharpness was the only SQM predicting participants' annoyance. Fixed-wing kites, characterized by sharper and more tonal and narrowband sound profiles, were rated as more annoying than the soft-wing kite, characterized by higher loudness values. In addition, the effect of some SQMs on annoyance depended on participant characteristics, with loudness having a weaker impact on annoyance for participants familiar with AWESs and tonality having a weaker effect on annoyance for older participants. These findings emphasize the importance of considering psychoacoustic factors in the design and operation of AWESs to reduce noise annoyance.

Abstract. Over the past few years, numerous studies have shown the detrimental impact of flow blockage on wind farm power production. In the present work, we investigate the benefits of a simple collective axial-induction set point strategy for power maximization and load reduction in the presence of blockage. To this end, we perform a series of large-eddy simulations (LESs) over a wind farm consisting of 100 IEA 15 MW turbines and build wind farm power and thrust coefficient curves under three different conventionally neutral boundary layers and one truly neutral boundary layer. As a result of the large-scale effects, we show that the wind farm power and thrust coefficient curves deviate significantly from those of an isolated turbine. We carry out a trade-off analysis and determine that, while the optimal thrust set point is still correctly predicted by the Betz limit under wake-only conditions, it shifts towards lower operating regimes under strong blockage conditions. In such cases, we observe a minor power increase with respect to the Betz thrust set point, accompanied by a load reduction of about 5 %. More interestingly, we show that for some conditions the loads can be reduced by up to 19 %, at the expense of a power decrease of only 1 %.

Abstract. This study shows an extensive analysis of dynamic stall on wind turbine airfoils, preparing for the development of a reduced-order model applicable to thick airfoils (t/c>0.21) in the future. Utilizing unsteady Reynolds-averaged Navier–Stokes (URANS) simulations of a pitching FFA-W3-211 airfoil with a Reynolds number of 15 × 106, our analysis identifies the distinct phases in the course of the evolution of dynamic stall. While the dynamic stall is conventionally categorized into the primary-instability transitioning to the vortex formation stage, we suggest two sub-categories for the first phase and an intermediate stage featuring a plateau in lift prior to entering the full stall region. This delays the inception of deep stall, approximately 3° for a simulation case. This is not predictable with existing dynamic-stall models, which are optimized for applications with a low Reynolds number. These features are attributed to the enhanced flow attachment near the leading edge, restricting the stall region downstream of the position of maximum thickness. The analysis of the frequency spectra of unsteady pressure confirms the distinct characteristics of the leading-edge vortex street and its interaction with large-scale mid-chord vortices in forming the dynamic-stall vortices (DSVs). Examination of the leading-edge suction parameter (LESP) proposed by Ramesh et al. (2014) for thin airfoils with low Reynolds numbers reveals that the LESP is a valid criterion in predicting the onset of the stall for thick airfoils with high Reynolds numbers. Based on the localized separation behavior during a dynamic-stall cycle, we suggest a mid-chord suction parameter (MCSP) and trailing-edge suction parameter (TESP) as supplementary criteria for the identification of each stage. The MCSP exhibits a breakdown in magnitude at the onset of the dynamic-stall formation stage and full stall, while the TESP supports indicating the emergence of a full stall by detecting the trailing-edge vortex.

Abstract. The present paper proposes a comparison of three well-established controllers: a robust proportional–integral–derivative (PID) controller (Conord and Peaucelle, 2021), a model-free control (Fliess and Join, 2013, 2022) and an adaptive sliding-mode control based on the super-twisting algorithm (Shtessel et al., 2023). The benchmark considered is an airfoil section equipped with trailing edge jets, load sensors and a perturbation system. The objective is to track the lift command under external wind perturbations. The outcome of this work is the comparison of performances for three control laws that are suitable when little knowledge is known from the physics. This study quantifies performance not only in terms of load control, but also in the needed implementation effort.