ABSTRACT Offshore wind farms are growing worldwide and increase the source of renewable energy. However, as the extent and density of offshore wind farms increase, there is growing importance to understand the interactions with physical systems and the marine environment, in particular, the sea state. This study presents an analysis of airborne data from the flight projects WIPAFF and X‐Wakes which took place in the German Bight, examining the effects of wind farm wakes on the sea surface and the possible interaction between atmospheric and sea surface wakes. By clustering the data, this study identifies patterns for the sea surface wake. For stable atmospheric stratification, the sea surface wake extends up to several tens of kilometers, while during unstable conditions, the sea surface wake is very short or absent. In all analyzed cases, the length of the atmospheric wake was longer than the length of the sea surface wake.

ABSTRACT Reliability, safety, and the lifetime of wind energy systems are mainly driven by the quality of each single component. Especially, large structures made of cast material or welded structures, such as the nacelle, the machine carrier, or the tower, are subjected to cyclic loading. In these cases, various factors determine the fatigue strength and thus the reliability. For castings, local material defects and the local microstructure are the relevant properties; for welded joints, the weld geometry and weld imperfections. While new and practical methods to assess the influence of defects resp. imperfections on fatigue strength are needed, wind energy components can profit from new cast materials and welding procedures with improved properties to enable a lightweight and more reliable component design. The paper discusses reliability aspects and the lifetime assessment of cast and welded wind energy components based on production quality and connected size effects. Therefore, new methods based on the quality assurance process are presented coupling digital nondestructive test results with local fatigue strength of cast and welded components. Moreover, newer achievements in improved materials and production quality and their benefit for lifetime and lightweight are discussed based on modern cast iron materials like high‐silicon nodular cast iron.

ABSTRACT Statistical models to wind speed allow understanding accurately the wind potential of a location, which is significant when deciding where to locate new wind farm installations and sometimes for the strategy of buildings and their structural design. The intention of this research is to fit three probability distribution functions (PDFs) named Weibull, generalized extreme value (GEV), and Gumbel (GUM) to average wind speed data from the metropolises of both São Paulo (SP) and Rio de Janeiro (RJ). Moreover, the objective encompassed calculating the anticipated average wind speeds for these metropolises, taking into account the return periods of 10, 20, 30, 40, 50, and 100 years. The assessment and appropriateness for chosen PDF were evaluated using statistical measures such as mean absolute percentage errors (MAPEs), Ljung–Box test, and Kolmogorov–Smirnov (KS) test. Regarding the evaluation of the accuracy of wind speed predictions in RJ, the Weibull distribution showed the best performance. In SP, the GUM and Weibull distributions showed the same performance. The results presented in this article can help in understanding the wind potential of southeastern Brazil and serve as a source of wind energy projects. This study is the first complete probabilistic analysis of extreme winds in SP and RJ, addressing a significant gap in wind potential assessment in Southeast Brazil. Using high‐resolution data spanning 61 years, the study analyzes extreme events. Unlike previous regional studies that focused on annual averages, this research aims to calculate monthly variability and return periods (10–100 years).

ABSTRACTThe conventional Savonius wind turbine's power coefficient is comparatively low, which limits its efficiency. Because the power coefficient depends significantly on the blade design, this study investigates the potential of improving the turbine performance by modifying the rotor blade designs. Two modified models are proposed, which are investigated numerically using CFD methods in ANSYS Fluent. Improving the efficiency of Savonius turbines could offer substantial benefits for energy generation, given the significance of wind energy in pursuing renewable energy solutions. The shear stress transport (SST) k‐omega model, which allows a detailed analysis of the turbine's aerodynamic performance, is utilized here under transient conditions. At different tip speed ratios (TSRs), the torque coefficient and power coefficients are calculated. Additionally, the flow structure around the rotor is evaluated. The study demonstrates the impact of rotor blade design on power and torque coefficients, revealing that at a TSR of 1, the modified models demonstrated better performance than the base conventional model, with increases of power coefficient by 9.37% and 12.5%. These results suggest that better blade designs have the potential to greatly improve Savonius wind turbine performance and increase their efficiency for real‐world applications.

ABSTRACT This study explores a novel passive flow control strategy combining a microcylinder and slot configuration to mitigate flow separation over an S809 wind turbine airfoil under stall conditions at a Reynolds number of 10 6 . Numerical simulations are conducted using the Reynolds‐averaged Navier–Stokes (RANS) approach. The primary objectives are to (i) reduce or eliminate the flow separation region, (ii) enhance aerodynamic performance, and (iii) assess the effectiveness of combining two passive control techniques. The study evaluates the effects of microcylinder diameter, relative position to the leading edge, and the gap‐to‐diameter ratio (G/D). Results indicate that placing the microcylinder too close to the suction surface can harm aerodynamic performance. However, an optimized microcylinder position effectively suppresses flow separation and improves aerodynamic coefficients for angles of attack (AoA) between 16° and 24°. When combined with strategically positioned slots, the optimized configuration achieves a 97.47% reduction in the separation region and a 16.83% improvement in lift‐to‐drag ratio compared to the microcylinder alone at 24° AoA. The study also highlights the underlying flow control mechanisms contributing to separation suppression. Although this method proves effective at high AoAs, it incurs a drag penalty at lower AoAs, leading to a reduced lift‐to‐drag ratio compared to the baseline airfoil. These findings demonstrate the potential and limitations of the proposed microcylinder‐slot combination for aerodynamic performance enhancement in wind turbine applications.

ABSTRACTDriven by the challenges in measuring blade deformations, this study presents a novel machine learning methodology to predict blade tip deformation using inflow wind data and operational parameters. Using a long short‐term memory (LSTM) model and a novel feature selection approach based on mutual information and recursive feature addition, this study presents a robust framework for multivariate time series prediction. The developed model offers significant computational cost reductions compared to full‐dynamic simulations and also allows virtual sensing. This work empowers efficient and reliable wind turbine operation by providing an accurate and computationally efficient blade response prediction tool that can assist in improved wind turbine management, site‐specific analysis and fatigue assessment.

ABSTRACT To validate the second‐by‐second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time‐resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral‐based models of the atmosphere. Here, we develop a technique for time‐resolved inflow reconstruction that is rooted in a large‐eddy simulation model of the atmosphere. Our “large‐eddy reconstruction” technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10‐min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large‐eddy simulation. We verify the second‐by‐second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values () between ground‐truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real‐world case studies by driving large‐eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second‐by‐second behavior ().

ABSTRACTThe structural stiffness of a blade changes when it is defective, which affects its modal parameters. Consequently, modal parameters can be crucial to investigating defects. Therefore, developing a model that can accurately analyze the modal parameters of healthy and defective blades is essential. This study proposes a high‐fidelity finite element (FE) model development method based on multiobjective optimization. Three 10‐kW‐class wind turbine blades were manufactured, and the modal parameter changes due to debonding damage were measured. The initial FE model was constructed using structural drawings and considering specific material properties. Subsequently, the modal analysis results were compared with the experimental results. The analysis errors of normal and abnormal blades were expressed as cost functions and multiobjective optimization was performed. Machine learning–based predictive models facilitated time‐ and cost‐effective optimization, enabling modal updating that simultaneously reflects modal parameter changes for various blade models. Because accurately predicting the dynamic characteristics of blades by various defect locations and sizes is crucial for blade damage detection, the proposed method is significant for damage detection research.

ABSTRACT We present AMR‐Wind, a verified and validated high‐fidelity computational‐fluid‐dynamics code for wind farm flows. AMR‐Wind is a block‐structured, adaptive‐mesh, incompressible‐flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high‐performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR‐Wind is spatially and temporally second‐order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12‐turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.

ABSTRACTThis paper reports the results of a validation experiment on dual and single scanning light detection and ranging (LiDAR) systems for near‐shore wind measurements. This experiment was conducted from November 2020 to August 2021 at the Mutsu Ogawara test site for offshore wind measurement in northern Japan. The accuracy of the wind speed and direction measured by dual and single scanning LiDAR systems (DSL and SSL, respectively) was investigated by comparing them with in situ observations by a 60‐m meteorological mast located approximately 1.5 km offshore. The accuracy of the SSL was found to be strongly influenced by the wind direction, whereas the 10‐min wind speeds and directions obtained by the DSL exhibited good agreement with the meteorological mast observations for all wind directions. In addition, a comparison of the turbulence intensity (TI) showed that the TI obtained by the SSL was significantly lower than that obtained by the cup anemometers, while that obtained by the DSL was in line with the cup observations. The accuracy of the potential annual energy production estimates was finally investigated. The results showed no apparent difference between the DSL and SSL. This long‐term experiment indicated that the SSL is suitable for assessing wind resources due to reduced technical and financial constraints in comparison to installing a meteorological mast offshore. However, the DSL would be useful for assessing not only wind resources but also site‐specific conditions, as it performed well for the TI measurements.