bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rotational and blockage effects on a wind turbine model based on local blade forces
Authors: Soto-Valle R. , Gualtieri M. , Bartholomay S. , Manolesos M. , Nayeri C.N. , Bianchini A. , Paschereit C.O. 
Published: June 2022

Abstract
This paper describes the results of an extended experimental campaign, reporting surface pressure measurement over one of the blades of the Berlin Research Turbine (BeRT), placed in a closed-loop wind tunnel facility. BeRT is a three-bladed horizontal axis wind turbine with a 3m rotor diameter. The focus is, on the one side, on the three-dimensional effects experienced by the rotating blade, in comparison to 2D approaches by means of XFoil simulations and 2D blade section experiments. On the other side, the blockage effects are investigated between the wind turbine model, placed in the wind tunnel where a 40% blockage ratio is produced, and lifting line free vortex wake simulations, where wind tunnel walls are not considered. Additionally, CFD computations are added in the comparison, with simulations of the far-field and with the wind tunnel walls. The turbine model is studied at several operational conditions such as different blade pitch angles and turbine yaw misalignments. Results are presented in terms of local force components derived from the surface pressure measurements. It is shown that rotational augmentation is evident at the blade mid-span location despite the large blockage. Additionally, the blockage is noticed by means of an offset in both normal and tangential local forces conserving trends and features under axial inflow and yaw misalignments. It is found that the offset in forces can be counteracted by pitching the blades.
Surrogate models for predicting stall-induced vibrations on wind turbine blades
Authors: Santhanam Chandramouli , Riva Riccardo , Knudsen Torben 
Published: June 2022

Abstract
Stall Induced Vibrations (SIV) are an aeroelastic instability that might happen when large portions of the wind turbine blade operate in moderate stall, which leads to high internal loads that can damage the structure. Exploring the inflow space that cause SIV has a high computational cost, which quickly increases with the number of input variables, since it involves high-fidelity aeroelastic simulations. In this work, a surrogate model is used to effectively explore the behavior of SIV in a five variable inflow space consisting of wind speed, yaw angle, vertical wind shear, wind veer, and atmospheric temperature. The surrogate model, trained from the results of a few aeroelastic simulations, predicts the severity of SIV for a given inflow condition. The surrogate model is chosen by comparing the performance of a few common model types, and Artificial Neural Networks (ANN) are found to provide a good balance between accuracy and computational time. Using the ANN, the occurrence and severity of SIV in the inflow space is studied. The analysis shows that for the turbine studied, yaw angle is the most influential variable, while temperature is the least influential variable in terms of conditions that lead to occurrence of SIV. Inflow conditions with a moderate yaw angle (around 10-25 deg), high wind speeds, and high negative veer are found to be the most critical in terms of severity of SIV. The current work is expected to serve as a helping tool to decide the focus of computationally expensive simulations such as high fidelity CFD based aeroelastic simulations.
Experimental Analysis of Planet Carrier Bearing Loads under Torque and Bending Moment in a Four-Point-Suspension Drive Train
Authors: Krause A , Gnauert J , Jacobs G , Bosse D , Schmitz L 
Published: June 2022

Abstract
Bearing failures in wind turbine gearboxes still occur which results in downtimes and hence leads to an increase in the levelized cost of energy. One of these affected bearings are the planet carrier bearings as they are still subject to unexpected fatigue related failure modes, like pitting and spalling, which are mainly influenced by the load distribution in the bearing. Hence it is necessary to determine the influence of torque and bending moment loads onto the planet carrier bearings. In this work the generator sided planet carrier bearing load distribution is investigated. Therefore, strain gauges are applied onto the circumference of that bearing of the gearbox of a 1 MW wind turbine four-point-suspension drive train. The loading is measured while testing at the 4 MW wind turbine test bench at CWD. The relevant loading for the investigated generator sided planet carrier bearing is torque which leads to a tilting of the gearbox leading to two load zones in the bearing. Bending moments at the hub of the drive train do not influence the bearing load distribution as they are absorbed by the main bearing.
A Resource-Efficient Design for a Flexible Hydraulic-Pneumatic Flywheel in Wind Turbine Blades
Authors: Alhrshy L , Jauch C 
Published: June 2022

Abstract
The utilization of renewable energy resources significantly increases in order to reduce the impact of climate change. Wind turbines are one of the most important renewable energy sources and have an important role to play in power generation. They do, however, have to serve the increasingly variable demands of the grid. Some of these demands cannot be satisfied with the standard control mechanisms of state-of-the-art wind turbines. A hydraulic-pneumatic flywheel in a wind turbine rotor is one mechanism which, in addition to its various grid services, can also reduce the mechanical loads on the structure of a wind turbine. However, the installation of such a flywheel into rotor blades increases the weight of the blades. This paper focusses on the development of a design method for reducing the additional mass of the flywheel. This method incorporates the piston accumulators of the flywheel in the blade support structure, which allows for the replacement of parts of the blade spar caps with composite material from the piston accumulators. This enables the flywheel to be installed into the rotor blades without making the wind turbine significantly heavier.
Development of an open-source segmented blade design tool
Authors: Anderson Benjamin , Bortolotti Pietro , Johnson Nick 
Published: June 2022

Abstract
As wind turbines continue to grow ever larger to reduce the cost of energy, their blades follow suit, with the largest commercial offshore blades extending past 100 m. Massive blades such as these raise key transportation and manufacturing challenges, especially for land-based turbines. Segmented blades are one solution and are garnering increased industry and research interest. In this work, a detailed mechanical joint model is integrated into the Wind-Plant Integrated System Design and Engineering Model (WISDEM®), which will facilitate future segmented blade research and optimization. WISDEM is used to design a wind turbine with 100-m segmented blades. This wind turbine design is compared to other machines with 100-m monolithic blades designed for rail-transportability. The designs are compared in terms of blade mass and cost, turbine capital cost, annual energy production, and levelized cost of energy, with monolithic designs being the lightest and most economical. However, this result may vary by wind plant location. A variety of segmentation joint types exist, and they will inevitably vary in parameters such as cost, spanwise location, and physical characteristics. This work examines the sensitivity of wind turbine design drivers and annual energy production to a variety of the aforementioned parameters, using the open-source wind turbine design codes OpenFAST and WISDEM, finding that joint mass, stiffness, and location can have significant effects on design drivers.
A spectral model generalising the surface perturbations from leading edge erosion and its application in CFD
Authors: Meyer Forsting AR , Olsen AS , Gaunaa M , Bak C , Sørensen NN , Madsen J , Hansen R , Veraart M 
Published: June 2022

Abstract
Blade leading edge erosion (LEE) is a major cost driver in the wind energy sector. LEE is caused by the environmental conditions under which the blades operate. The impact energy of the airborne particles striking the leading edge determines the speed of erosion, thus LEE severity grows towards the blade tip and with a turbine’s tip speed. Currently there is no established method for assessing the aerodynamic impact of LEE, either numerically or experimentally caused by a lack of erosion topological data and its stochastic nature. Whilst previous studies investigated specific realisations of real-world erosion—modelling roughness, gouges, pinholes etc.—we propose a novel, reproducible representation of erosion, based on the superposition of waves with different frequencies and directions of propagation. Using lidar surface scans of a LE exposed to a rain erosion test, we demonstrate the possibility of representing surface perturbations from erosion by a simple spectrum, thereby allowing the mathematical representation of eroded surfaces. Furthermore, we demonstrate how the spectral model simplifies the analysis of LEE-affected aerofoils in CFD. Our study thus encompasses the workflow from rain erosion test → surface scan → spectral perturbation model → numerical erosion generation → 2D CFD → performance loss statistics.
On the combined use of Vortex Generators and Gurney Flaps for turbine airfoils
Authors: Chng L , Alber J , Ntouras D , Papadakis G , Kaufmann N , Ouro P , Manolesos M 
Published: June 2022

AbstractWhile Vortex Generators (VGs) and Gurney Flaps (GFs) are commonly used for airfoil flow control, studies of a combination of the two devices are rare. The present investigation aims at examining the combined effect of VGs and GFs on a 20% thick airfoil. To this end, a wind tunnel investigation coupled with a computational study was performed. The present paper presents force, pressure and Stereo Particle Image Velocimetry measurements, along with Unsteady Reynolds Averaged Navier Stokes simulations.
Propagation of wind turbine noise: measurements and model evaluation
Authors: Nyborg C M , Fischer A , Thysell E , Feng J , Søndergaard L S , Sørensen T , Hansen T R , Hansen K S , Bertagnolio F 
Published: June 2022

Abstract
Sound propagation from wind turbines through the atmosphere has previously been analytically examined through numerous studies, but the amount of comparisons to measurements is limited. This paper applies three different sound propagation models; The ISO 9613-2 model, the Nord2000 ray tracing model and the WindSTAR-Pro model, to evaluate the noise at receivers in the near- and far-field of a wind turbine. The evaluation is performed by accounting for different meteorological and terrain parameters. For validation purposes, the study further uses two different data sets from measurement campaigns; Loudspeaker noise and wind turbine noise measurements. The validation evidently shows that especially the WindSTAR-Pro model and the Nord2000 model provide good agreement to the measurements for some of the validation cases, while other results are heavily influenced by the turbine wake, signal to noise ratio in the measurements or directivity of the noise source.
Extreme wind speed ramp events: A measurement-based approach for improving the modelling of ultimate loads for wind turbine design
Authors: Ranka Preeti , Valldecabres Laura , Schafhirt Sebastian , Bierbooms Wim 
Published: June 2022

Abstract
Ramp events, i.e., significant changes in wind speed in a short time period, have become critically important to end-users. However, only a few studies address their impact on wind turbine loads. To the best of the authors’ knowledge, these results have not yet been validated with measurements. Therefore, this paper aims to investigate the impact of extreme wind speed ramps on ultimate wind turbine loads using eight months of offshore measurements. We also compare the measured loads with simulations following the International Electrotechnical Commission (IEC) extreme turbulence model, in order to improve the modelling of ultimate loads. This is because events with a 10-min horizontal wind speed standard deviation higher than the prescribed IEC turbulence class, in line with other research, are primarily associated with ramp events. They are found to be design driving for the blade root flap-wise moments below and beyond rated wind speed, but not in the transition region. The high-frequency analysis of these moments showed a sudden pitch transition from the inactive to the active region. In general, the loads associated with ramp events did not exceed the simulations. In addition, non-ramp related extreme loads around rated wind speed, which exceeded the simulations, were associated with standard deviations slightly above the normal turbulence model (NTM) of IEC for a waked turbine, indicating the impact of wake added turbulence. In conclusion, for the ultimate load analysis, the wind speed time series should include a sudden pitch transition from the inactive to the active region in addition to wake added turbulence.
Validation of crack initiation model by means of cyclic full-scale blade test
Authors: Rosemeier M , Melcher D , Krimmer A , Wroblewski W , Antoniou A 
Published: June 2022

Abstract
Wind turbine rotor blades are subject to highly dynamic loads and designed for life cycles of at least 20 years, which means that materials are subjected to high-cycle fatigue. Fatigue is a design-driving loading for current and future blades. Bond lines of blades are exposed to a multi-axial stress-state due to the anisotropic thin-walled blade structure and curved, tapered, twisted, and airfoil-shaped blade geometry. To eliminate undesirable failure modes and thus increase the reliability of wind turbine rotor blades, standards and guidelines recommend that the multi-axial stress-states be taken into consideration for the limit state analysis. In addition, thermal residual stresses that develop during manufacture can have a significant impact on the fatigue life of the bond line. By means of a cyclic full-scale blade test of a commercial 81.6m long offshore blade, we validate a crack initiation model, which takes into account multi-axial thermal and mechanical stress-states, as well as the probabilistic stress-life, to predict the edge of crack initiation in the adhesive as well as the span-wise position. Both observations agreed well with the simulations. All residual normal stress components and cross-sectional plane shear stress made up the major part of the mean equivalent stress, while the mechanical stress amplitude components - longitudinal, peel, and cross-sectional plane shear stress - made up the major part of the equivalent stress amplitude.
Vortex-model-based Multi-objective Optimization of Winglets for Wind Turbines using Machine Learning
Authors: Leenders Nick , Yu Wei , Gaunaa Mac , Caboni Marco , Ferreira Carlos Simão 
Published: June 2022

Abstract
Different Design Driving Load constraints (DDLs), are explored in this work to determine under which constraints and conditions a winglet can have an added value to the wind turbine blade design. Multi-objective Bayesian optimization is used to maximize the rotor’s power production while minimizing the flapwise DDLs. Surrogate models, created using machine learning techniques such as Gaussian Processes and Bayesian Neural Networks, are used in combination with an acquisition function, to determine what designs should be evaluated by the lifting line model AWSM, with the goal to obtain designs that lie on the Pareto front of two or more objectives. The recent Bayesian Neural Networks as surrogate model were able to find the Pareto-front most effectively in this work. Furthermore, the results show that different DDL constraints led to different winglet designs, with noticeable differences between upwind and downwind winglet designs. Winglet designs were found to be able to increase power without increasing the thrust, root flapwise bending moment and flapwise bending moment at radial locations on the blade. A noticeable increase in power was found when introducing sweep to the winglet design.
Modelling of wind turbine gear stages for Digital Twin and real-time virtual sensing using bond graphs
Authors: Mehlan F C , Pedersen E , Nejad A R 
Published: June 2022

Abstract
In this paper a wind turbine high-speed gear stage model is developed for the purpose of real-time virtual sensing of gear and bearing loads in a Digital Twin framework. The model requirements are: accurate representation of gear meshing and shaft dynamics, high computational efficiency and compatibility with other Digital Twin components, such as physical sensors signals and virtual sensing methods. State equations are derived analytically using the Bond Graph method and implemented in the software 20sim for simulation. As opposed to standard multi-body simulation (MBS) software, 20sim allows for higher flexibility in implementing interfaces to other Digital Twin components. The model fidelity is close to state-of-the-art MBS models considering 6 DOF body motion, however a simplified gear contact formulation is used, which assumes ideal kinematic meshing. Nonetheless, the Bond Graph model is able to accurately reproduce the inhomogeneous load distribution over the tooth flank, as well as the cyclic compression and decompression for each meshing period. The results suggest that the presented model is capable of monitoring fatigue loads in gear contacts and bearings in a Digital Twin framework.
Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes
Authors: Singh D , Dwight R P , Laugesen K , Beaudet L , Viré A 
Published: June 2022

Abstract
Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turbine. Stochasticity in the inflow turbulence and irregular waves results in load responses that are best represented as random variables rather than deterministic values. Moreover, the effect of these stochastic sources on the loads depends strongly on the mean environmental conditions - for instance, at low mean wind speeds, inflow turbulence produces much less variability in loads than at high wind speeds. Statistically, this is known as heteroscedasticity. Deterministic and most stochastic surrogates do not account for the heteroscedastic noise, giving an incomplete and potentially misleading picture of the structural response. In this paper, we draw on the recent advancements in statistical inference to train a heteroscedastic surrogate model on a noisy database to predict the conditional pdf of the response. The model is informed via 10-minute load statistics of the IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with OpenFAST. Its performance is assessed against the standard Gaussian process regression. The predicted mean is similar in both models, but the heteroscedastic surrogate approximates the large-scale variance of the responses significantly better.
Individual pitch control by convex economic model predictive control for wind turbine side-side tower load alleviation
Authors: Pamososuryo AK , Liu Y , Hovgaard TG , Ferrari R , van Wingerden JW 
Published: June 2022

Abstract
The wind turbine side-side tower motion is known to be lightly damped. One viable active damping solution is realized by deploying individual pitch control (IPC) such that counteracting blade forces are created to alleviate the tower fatigue loading caused by this motion. Existing IPC methods for side-side tower damping in the literature, such as linear quadratic regulator and lead-lag controller, cannot accommodate direct optimization and tradeoff tunings of the wind turbine economic performance. In this work, a novel side-side tower damping IPC strategy under a convex economic model predictive control (CEMPC) framework is therefore developed to address these challenges. The main idea of the framework lies in the variable transformation in power and energy terms to obtain linear dynamics and convex constraints, over which the economic performance of the wind turbine is maximized with a globally optimal solution in a receding horizon manner. The effectiveness of the proposed method is showcased in a high-fidelity simulation environment under both steady and turbulent wind cases. Lower fatigue damage on the side-side tower bending moment is attained with an acceptable level of pitch activities, negligible impact on the blade loads, and minor improvement on the power production.
On LiDAR-assisted wind turbine retrofit control and fatigue load reductions
Authors: Lio Wai Hou , Meng Fanzhong , Larsen Gunner Chr. 
Published: June 2022

AbstractThe use of upstream wind speed measurement has motivated the development of LiDAR-assisted control for enhancing rotor speed tracking and fatigue structural load reductions. However, conventional LiDAR-assisted control designs often require altering the existing controller architecture, for example, by sending an additional feed-forward pitch angle signal to the pitch actuator or by incorporating the feed-forward pitch rate into the feedback controller. This work proposes LiDAR-assisted retrofit control solutions that can be implemented without any prior knowledge or modifications of the existing feedback controller. Specifically, the feed-forward pitch action is provoked by modifying the rotor speed measurement. Three retrofit methods are proposed according to the level of retrofit requirement and complexity. Numerical simulation results showed that all three LiDAR-assisted retrofit solutions could achieve good thrust-related load reductions. Thus, the proposed LiDAR-assisted retrofit solutions present a simple control upgrade to extend the lifetime of existing turbines.
Estimation of Damage Equivalent Loads of Drivetrain of Wind Turbines using Machine Learning
Authors: Kamel O , Hauptmann S , Bottasso C L 
Published: June 2022

Abstract
In the recent years machine learning techniques have attracted the attention of wind energy community to make use of the large amount of available data produced from the running wind turbines. These modern wind turbines are typically equipped with measurement systems and sensors that can provide a wealth of information about the operating conditions of the machine. Nevertheless, not all the acquired raw data can be used effectively to enhance the operation of a turbine. This work addresses the question of estimating the damage equivalent loads (DEL) of different components of a drivetrain. The estimation is based on low frequency sampled typically available SCADA measurements. Typical SCADA measurements that are used as input for the estimation model are generator rotational speed, low speed shaft torque and generator torque as well as, wind speed and direction. Several machine learning methods as random forests (RF), support vector machines (SVM), linear regression (LR), decision trees and neural networks (NN) were developed, exhibiting different behavior for each approach. The qualitative and quantitative performance of each algorithm are evaluated and compared against each other. Furthermore, analysis of importance of the input features is presented.