This paper presents an open-access database of aerodynamic polars for a wide range of airfoils, relevant for wind turbine design and simulation. It includes lift, drag, and moment coefficients for multiple Reynolds and Mach numbers. Coefficients are computed with computational fluid dynamics for fully turbulent and free transition boundary layers, as well as a blend of the two. Beyond-stall extrapolation models are calibrated via a number of high-fidelity calculations at various angles of attack in separated flow.

This study shows that buoyancy of the atmosphere has a large impact on the lifetime of a wind turbine. We use measurements from one of the largest wind turbines in the world to show that this feature of the atmosphere must be considered while in the design process. Our work is also motivated by the need to update the current models of the atmosphere. Indeed, as turbines increase in size, there is a concern that the deficiencies of our models might become exposed.

This paper presents a sensor-error-robust methodology for failure prediction in wind turbines. Sensor malfunctions pose a significant challenge for data-driven prognostic approaches. The proposed method employs a masked autoencoder that enables selective deactivation of signals. Evaluation is done using data from several operational offshore wind farms. Results demonstrate that the model effectively mitigates the impact of sensor errors while maintaining high accuracy in failure prediction.

This study explores how artificial intelligence can improve the maintenance of offshore wind farms. By reviewing recent research, we show that learning-based methods can predict failures, plan repairs more efficiently, and reduce costs compared to traditional strategies. We identify key gaps in current approaches and suggest ways to make these models more realistic and practical for real-world wind energy operations.

This paper presents an improved Bayesian uncertainty quantification framework for calibrating and comparing wind farm flow models. It quantifies the parameter uncertainty in a joint posterior distribution as well as the model uncertainty. Applied to a large-eddy simulation dataset for wind farm blockage, it compares a standard wake model and an atmospheric perturbation model, showing the latter yields lower model uncertainty. The framework is available in the open-source Python package UMBRA (Uncertainty Modeling toolbox for Bayesian data Re-Analysis).

This study investigates how finite-element (FE) models of different fidelity affect the damage identification in a 31 m wind turbine rotor blade tested under edgewise fatigue loading. Different design variable configurations are compared, whereby the FE model updating is based on modal parameters identified from measured vibration data.

Estimating the energy production of a wind plant before construction is a historically difficult task. We build on prior research comparing the actual energy production of wind plants and their estimated energy production using owner-provided energy data. Similar to prior studies, we found an increasing bias of overestimating annual energy production. In general, estimates before construction are not conservative enough, suggesting room for improvements in the energy yield assessment process.

For modern wind turbines, the effects of inflow wind fluctuations on loads are becoming increasingly critical. Based on field measurements and simulations, we identify “bump” events responsible for high damage equivalent loads. In this article, we introduce a new characteristic of a wind field: the virtual center of wind pressure, which highly correlates to the identified load events observed in the operational measured data.

This study compares circular and figure-of-eight flight patterns for airborne wind energy systems, evaluating power output, stability, and system lifespan. Findings indicate that circular paths maximise energy generation in confined spaces, while figure-of-eight trajectories, particularly those ascending through the centre, provide smoother, more consistent power and extend kite longevity. These insights contribute to the improved design and performance of kite energy systems.

This study shows that conventional methods in rotor blade fatigue testing can lead to substantial under-testing across major blade areas because material fatigue behavior is not well represented. An improved approach, based on strain-proportional loads with mean load correction, is proposed to define loads that produce sufficient fatigue damage across all blade areas. The results suggest that this can require up to 16 % higher uniaxial fatigue test loads than needed by conventional methods.

We developed a new modeling system that combines air, ocean, and wave processes to better understand hurricanes. Using Hurricane Henri as a test case, we found that including ocean and wave interactions improves the accuracy of storm intensity and wind patterns. These results show that accounting for these interactions is important for assessing risks to offshore energy systems and coastal regions.

Low-level jets are wind maxima that frequently occur at wind turbine rotor heights. This work investigated their properties and their impact on wind turbines using 3 years of wind measurements in Dunkerque, a coastal city in the North Sea. Results showed that jets are frequent (15 % of the time), could decrease current turbines' production, and could expose them to unfavorable structural conditions, whereas future turbines will be less impacted and will see an increase in energy production.

We show that simple vibration sensors on wind turbines can reveal how each machine is operating without relying on control system data. By learning patterns from short acceleration segments, our model identifies turbine behavior, detects changes in operation, and tracks events over time. These patterns also support estimating fatigue, providing a new way to understand turbine performance using only vibration measurements.

This study presents a novel method to characterize the aerodynamic flow state on wind turbine blades using eTellTale (eTT) sensors. Deployed on two Vestas V27 turbines, the sensors show that attached flow increases energy production by 15 %, while detached flow reduces it by 30 %. Results indicate that 15 % of potential power is lost during 33 % of operating time, highlighting opportunities for improved real-time turbine control.

We present a method to forecast inertia that accounts for wake effects in a wind farm. The approach is based on mapping forecasted site conditions to each single wind turbine in the farm through a wake model. The resulting inflow conditions are used to predict the inertia that the wind farm can provide to the grid, taking the wind turbine control strategies and operational limits into account.

This paper presents gigawatt-scale floating wind farm reference designs tailored to the site conditions of three regions. The array designs use a 15 MW semisubmersible floating turbine and include the array layout, mooring systems, dynamic power cables, array cable routing, and floating substations. The layout methodology includes spatial constraints for each subsystem. The resulting arrays provide open-source baseline designs to facilitate future floating wind research at the array scale.