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.