bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of a Near Wake model on the performance of a Multi-Megawatt wind turbine with an active flap
Authors: Gamberini A , Barlas T , Gomez Gonzalez A
Published: June 2024

The impact of including the near wake model in the aeroelastic models of an SWT-DD-120 wind turbine equipped with an active trailing edge flap system (ATEF) is studied. The ATEF installed on this turbine was tested in full scale in a series of campaigns between 2018 and 2022. Since standard BEM methods cannot accurately capture the induced velocities near the transition areas of the start and end of flap add-ons due to the strong interaction of the local trailed vorticity with the neighboring blade sections, this study aims to investigate if and how the near wake model is relevant in the aerodynamic and aeroelastic characterization of rotor blades with active flaps. It is shown that the Near Wake model, when combined with an active trailing edge flap system, has a marginal impact on the blades’ thrust-related loads, independently from the presence, actuation state, and actuation frequency of the flap. The impact on the torque-related loads is also marginal at the blade root, while it becomes more relevant in the blade area where the flaps are deployed.
Effect of Induction and Blade Elasticity Modelling on Wind Turbine Rotor Performance Predictions
Authors: Aryan Navid , Öztürk İldeniz , Putra Muhammad Juanda , Sezer-Uzol Nilay , Oğuz Elif , Greco Luca
Published: June 2024

This study investigates the impact of blade induction modelling on the accuracy of wind turbine rotor aeroelastic predictions. It extends the capabilities of AEOLIAN (AErOeLastic sImulAtioN), a Fluid Structure Interaction (FSI) solver based on Blade Element Momentum Theory (BEMT) coupled with a Lumped Mass approach to represent the blade structure. Herein, AEOLIAN’s analytical wake induction engineering model is replaced with the outcomes of a physically-consistent three-dimensional Free-Vortex Wake (FVW) formulation initially employed in AeroROTOR. This versatile aeroelastic simulation tool is implemented within the framework of MATLAB Simulink/Simscape-Multibody©, a modular environment suitable for industry analysts, researchers, and academic users focusing on wind turbine aero-servo-elastic applications. Furthermore, it serves to lay the groundwork for the development of advanced control laws for multi-megawatt rotors, fostering innovation in the design and optimization of the next-generation wind turbines. The presented analyses focus on predicting the aeroelastic behavior of the bottom-fixed NREL 5MW rotor in uniform axial flow over the operating range, complemented by more detailed investigations at the rated condition undergoing inflow with/without wind misalignment (yaw). The study on key performance parameters is conducted by comparing with the higher-fidelity data from available Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD) coupled with CFD.
Improved performance of k − ω SST turbulence model in predicting airfoil characteristics for a wide range of airfoil thicknesses
Authors: Adeel-Ur-Rehman A , Theron J N , Kassem H , Stoevesandt B , Peinke J
Published: June 2024

A variety of airfoils designed for wind turbine rotor blade applications were simulated with k − ω shear stress transport (SST) turbulence model in its standard form, the k − ω SST turbulence model with a modified a
1 constant (referred to as the a
1 method), the Spalart-Allmaras turbulence model, and the panel method XFoil. To assess their performance, the results of airfoil lift, drag, and coefficient of pressure were compared against available wind tunnel data, where available. The standard k
−ω SST turbulence model is found to over-predict Reynolds shear stresses, delay the flow separation, and under-predict the separated-flow region on the airfoil’s suction side. Airfoil thicknesses between 11% and 36% of the chord length were studied. Using a modified a
1 constant, some airfoils exhibited up to a 20% improvement in the prediction of lift and drag coefficients within the post-stall range of angle of attack (AoA). It is important to note that the a
1 method’s applicability and effectiveness is specifically tested for airfoils, and its performance is highly dependent on the airfoil geometry.
Dynamic stall measurements of a harmonic pitching NACA63-018 airfoil at high Reynolds numbers
Authors: Mikkelsen Robert , Bak Christian , Gaunaa Mac , Fischer Andreas , Olsen Anders S. , Ildvedsen Sigurd , Beckerlee Jimmie
Published: June 2024

Wind tunnel test of the NACA63-018 airfoil is performed investigating high Reynolds number dynamic stall behaviour at PLCT [1]. The foil is tested within the reduced frequency range of k ≃0.03 and Reynolds number range Re=1.5-6M in the linear attached flow range and at stalled mean AoA at moderate harmonic pitch amplitudes. The foil is fitted with one centred high resolution pressure tab distribution and six spanwise distributed low resolution pressure tab sections for evaluation of spanwise correlations. With the additional tunnel instrumentation, cross comparison between different lift-drag estimates will be shown covering gage, airfoil pressure, wall pressure and wakerake, and evaluation of steady vs harmonic spanwise variations.
Improving data sharing in wind energy - structural health monitoring case study
Authors: Barber Sarah , Marykovskiy Yuriy , Abdallah Imad
Published: June 2024

A lack of data sharing in the wind energy sector presents a large barrier to increasing the value of wind energy through innovation. One way of improving data sharing is to make it “FAIR”: findable, accessible, interoperable and reusable. The FAIR Data Maturity Model is a tool developed by the Research Data Alliance that can be used to assess and improve the “FAIRness” of data, by quantifying the extent of its findability, accessibility, interoperability and reusability. In this work, we investigate how the FAIR Data Maturity Model could be applied to improve data sharing in the wind energy sector, via a structural health monitoring (SHM) case study. This case study is created as part of a WeDoWind challenge, and was chosen due to the high potential of SHM in reducing the costs of energy through predictive maintenance. WeDoWind is a framework for creating mutually beneficial collaborations, and the WeDoWind wind energy ecosystem is a growing ecosystem of diverse people all over the world sharing and exchanging knowledge and data. It is found that the FAIRness of the provided data set is limited due to the lack of community standards, and the absence of public data sharing services catering specifically to the wind energy context. However, the FAIR Data Maturity Model is successfully applied to improve the FAIRness of the data sets in the case study. A participant survey shows that this made data sharing easier in the context of a WeDoWind data sharing project. Finally, the project results in a set of recommendations for helping the wind energy community to improve the FAIRness of data.
Improvements to a novel learning algorithm for model-based wind turbine controllers
Authors: Fechner U , Spagnolo F , Mulders SP
Published: June 2024

Wind turbine controllers are nowadays ever more advanced and rely on accurate internal controller model information. Therefore a calibrated model is needed for attaining predictable controller performance and ensuring stable operation. To calibrate the internal model information, a novel learning control scheme has recently been proposed that exploits the dynamics of the closed-loop controlled wind turbine system, without the need for wind speed measurements. The learning algorithm thereby periodically excites the generator power controller input signal. An extremum-seeking demodulation scheme was used to calibrate the internal model information. This paper improves the existing learning scheme in two ways: Firstly, it investigates how the frequency of the excitation signal influences the signal-to-noise ratio. Secondly, the problem was reformulated as a root-finding problem. This requires using the in-phase component of the phase-corrected learning signal. In addition, a precalculated lookup table relates the measured in-phase component directly to model uncertainty. It was found that an increased excitation frequency improves the signal-to-noise ratio (SNR) by an order of magnitude. Combined, these contributions improve the convergence speed more than twenty times, addressing the effect of aerodynamic degradation and its consequences on controller performance.
Multi-Rotor Wind Turbine Control: Strategies for Pitch Replacement and Mechanical Load Mitigation
Authors: Tohamy Youssef E. , Giger Urs , Marten David , Schulte Horst
Published: June 2024

This paper presents a two-level control scheme for multi-rotor wind turbines. The first subordinate decentralized controller replaces the active pitch control with oversized generators that achieve the power limitation objective. In combination with this, the second higher-level controller mitigates the mechanical load on the main tower by adjusting the thrust forces acting on the side turbines through coordinated local torque control. The results proved that the decentralized controller managed to replace the pitch control by achieving the same power curve, and the load mitigation controller tended to equate the thrusts on both turbines, which mitigates the mechanical loads on the turbine tower. A high-fidelity QBlade model was developed and showed consistent results to the simplified model in SIMULINK.
Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models
Authors: Klein A , Wintermeyer-Kallen T , Stegink J , Kösters T , Zierath J , Nelles O , Abel D , Vallery H , Basler M
Published: June 2024

To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.
Towards fine tuning wake steering policies in the field: an imitation-based approach
Authors: Bizon Monroc C , Bušić A , Dubuc D , Zhu J
Published: June 2024

Yaw misalignment strategies can increase the power output of wind farms by mitigating wake effects, but finding optimal yaws requires overcoming both modeling errors and the growing complexity of the problem as the size of the farm grows. Recent works have therefore proposed decentralized multi-agent reinforcement learning (MARL) as a model-free, data-based alternative to learn online. These solutions have led to significant increases in total power production on experiments with both static and dynamic wind farms simulators. Yet experiments in dynamic simulations suggest that convergence time remains too long for online learning on real wind farms. As an improvement, baseline policies obtained by optimizing offline through steady-state models can be fed as inputs to an online reinforcement learning algorithm. This method however does not guarantee a smooth transfer of the policies to the real wind farm. This is aggravated when using function approximation approaches such as multi-layer neural networks to estimate policies and value functions. We propose an imitation approach, where learning a policy is first considered a supervised learning problem by deriving references from steady-state wind farm models, and then as an online reinforcement learning task for adaptation in the field. This approach leads to significant increases in the amount of energy produced over a lookup table (LUT) baseline on experiments done with the mid-fidelity dynamic simulator FAST.Farm under both static and varying wind conditions.
Synchronized Dynamic Induction Control: An Experimental Investigation
Authors: Van Vondelen AAW , Van Der Hoek DC , Navalkar ST , Van Wingerden JW
Published: June 2024

Wind turbines in farms face challenges such as reduced power output and increased loading when their rows align with the wind direction—a phenomenon known as the wake effect. To address this issue, dynamic induction control has been proposed, which involves dynamically adjusting the induction of upstream turbines to enhance the mixing of the wake with the free stream. As a continuation of this method, downstream turbines could potentially leverage the periodic structure in the upstream turbines’ wake to improve power production further downstream by synchronizing their dynamic induction control actions. This study investigates the potential of such an approach using a three-turbine scaled setup in a wind tunnel. The findings reveal that synchronization not only improves wake mixing downstream but also results in a substantial power gain on the synchronizing turbine, suggesting potential for a synchronization controller.
New methodology to estimate fatigue from acceleration time series experimentally validated in an onshore and floating offshore wind turbine
Authors: Pimenta Francisco , Mata Gonçalo , Magalhães Filipe
Published: June 2024

In onshore wind turbines, the association of fatigue damage with the environmental conditions based on the wind field properties have already been shown to be effective. However, most of the predictive power of such approach is based on average results, being the informative power regarding a single event extremely reduced. In this paper, a new methodology based on a reduced order model where the tower top accelerations are explicitly taken into consideration is proposed, with the adequate adaptations for floating offshore and onshore applications. After a numerical validation of the model predictions using OpenFAST, the methodology is used to reconstruct the bending moments time series of both an onshore and a floating wind turbine that are currently being monitored with accelerometers and strain gauges. The existence of this experimental data allowed for a direct comparison of the accelerations based estimates with the values obtained from the strain gauges readings and the method was proven to reproduce with great accuracy the tower bending moments time series. This conclusion sustain the claim that the proposed methodology for fatigue estimation is more robust than conventional SCADA-driven approaches and more informative regarding each individual event, making it suitable to be used in real time decision making processes.
Enabling self calibration for the wind turbine digital twin: a blending-function learning algorithm
Authors: Liu Y. , Liu C. , Hermans K.
Published: June 2024

The digital twin emerges as an economically viable solution for estimating signals that would otherwise necessitate additional instrumentation on wind turbines. While effective, the digital twin model may not comprehensively mirror the real-world dynamics of a wind turbine. Conversely, the long-term operation of a wind turbine over the years often results in aerodynamic degradation. Both factors contribute to sub-optimal prediction performance of the digital twin. This paper presents a novel blending-function learning algorithm designed to address this challenge. It is a data-driven approach that eliminates the need for a priori physical knowledge of wind turbine dynamics. By employing a pre-defined sinusoidal blending function, the algorithm projects the deviation between measured and estimated signals onto a lifted function space. This transformation aids in identifying potential aerodynamic degradation, which is then fed back into the digital twin for online self-calibration of wind turbine parameters. Simulation results demonstrate that the proposed learning algorithm exhibits satisfactory performance in mitigating the impact of aerodynamic degradation on digital twin predictions. It not only identifies the degradation factor but also calibrates the model parameters in an online adaptive manner, resulting in a robust representation of the wind turbine in the digital realm and thereby maintaining its prediction performance.
Towards unsupervised fault detection for offshore wind turbine cable protection systems using contrastive learning
Authors: Duthé Gregory , L’Homme Yan , Abdallah Imad , Chatzi Eleni
Published: June 2024

As offshore wind power expands globally, it is essential to ensure the reliable operation of components of such critical infrastructures. A less explored instance of such components, which are though essential in terms of operation, is found in subsea turbine cables and their protection systems, whose failure can incur prolonged shutdown periods and costly repairs. We propose a novel unsupervised machine learning approach exploiting use of Distributed Acoustic Sensing (DAS) data and contrastive learning for monitoring offshore wind turbine Cable Protection Systems (CPSs). A Transformer neural network adapted for time-series ingests the high-frequency, noisy DAS CPS time-series measurements, and is trained to learn a coherent representation of the data using a contrastive learning scheme that enforces temporal and positional consistency in the latent space. This latent representation can then be used to perform anomaly detection in an unsupervised manner, alleviating the need for costly labeled offshore anomaly data. We demonstrate that a coherent representation of the data is learnt by the model, which we then use to detect synthetic anomalies and an actual CPS stabilization event.
Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines
Authors: Lio Alan W.H. , Meng Fanzhong , Larsen Gunner Chr.
Published: June 2024

LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers.

A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.
Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations
Authors: Gagnon Louis , Lutz Thorsten
Published: June 2024

This paper presents a method for automatically reconstructing and smoothing surfaces from laser-scanned wind turbine blades. The aim is to accurately reconstruct turbine blade surfaces in the absence of an accurate CAD model. The input consists of a series of imperfectly aligned blade point clouds, and the output is a CFD surface mesh. The automatic process starts by segmenting the blade into as many sections as there are points in the spanwise direction of the target CFD mesh. Each segment is prepared for conversion into a periodic B-spline by undergoing angular sorting, application of the Iterative Closest Point algorithm, and light smoothing with the Savitzky-Golay filter. The final surface mesh consists of a series of B-spline airfoils with matching control points fitted on a series of spanwise nonperiodic splines. The smoothed airfoils closely match the noisy point cloud data across the entire blade. Three blades of a single turbine were scanned and meshed. The maximum distance between the blade tips of the three clouds is 2.5 cm (0.1% radius). Minor differences in airfoil profiles were observed, but they had negligible effects on lift and drag. Pitch torques were slightly more affected.
Alignment calibration and correction for offshore wind measurements using scanning lidars
Authors: Gramitzky Kira , Jäger Florian , Hildebrand Tabea , Gloria Nathalie , Riechert Jens , Steger Matthias , Pauscher Lukas
Published: June 2024

Long range scanning lidars offer great potential to measure wind characteristics for a variety of application in offshore wind energy. In order to derive accurate wind measurements it is critical to thoroughly calibrate the beam alignment. In offshore environments, the pitch and roll angle of a scanning lidar can be determined from sea surface levelling (SSL). This paper compares two methods to perform SSL, a method based on PPI scans and a novel method using RHI scans. Moreover, a previously described method to identify the distance at which the laser beam enters the water is extended to be applicable to larger measurement distances. Based on data from an offshore campaign in the North Sea, the differences between the RHI and PPI methods and their sensitivity to the selected elevation angles (PPI) and distances (RHI) are analysed. It is shown that both SSL methods prove to be robust for determining pitch and roll for all examined settings. The average results of the two methods show only minor differences. The standard deviation across all settings and both methods is 0.010° for pitch and 0.024° for roll. Compared to typically observed platform movements introduced by thrust on the wind turbine, the differences between different SSL settings are small. Comparison of the platform movements measured by the high sensitivity inclinometer and SSL shows that even slight differences in pitch and roll can be detected by SSL. This indicates a high level of accuracy for both methods investigated.
Instantaneous mesh load factor (Kγ
) measurements in a wind turbine gearbox using fiber-optic strain sensors
Authors: Gutierrez-Santiago Unai , Keller Jonathan , Fernández-Sisón Alfredo , Polinder Henk , Van Wingerden Jan-Willem
Published: June 2024

The mesh load factor, Kγ
, describes how loads are shared between planet gears and has become one of the key design challenges in modern wind turbine gearboxes. Planet load sharing directly impacts tooth root stresses, a critical driver of torque density and gearbox reliability. Experimental evaluation of Kγ
is typically performed from sun gear tooth root strain gauge measurements, which are complex. Furthermore, such measurements can only provide an average value of load sharing. The present study describes an alternative method to evaluate the mesh load factor in wind turbine gearboxes based on fiber-optic strain sensors installed on the outer surface of the fixed ring gear. We present the results of an extensive measurement campaign to evaluate this novel sensing solution installed on the input planetary stage of a 2-MW wind turbine gearbox at the National Renewable Energy Laboratory’s Flatirons Campus (Colorado, USA). The number of strain sensors on the ring gear was selected as an integer multiple of the number of planets, which has enabled an instantaneous evaluation of the mesh load factor. The effect of operating conditions on the planet load-sharing behavior of the gearbox has been investigated. The mesh load factor measured for operating conditions close to rated was below 1.05, well below IEC 61400-4 standard requirements.
Applying motion compensation to offshore wind lidar reconstructed wind measurements
Authors: Benzo C , Delbos F , Yahiaoui S
Published: June 2024

Floating lidar systems present an effective and advantageous solution for offshore wind speed measurements, providing wind speed and direction data at multiple heights with easy deployment to provide widespread wind information around an area. One of the causes of apprehension, however, concerns buoy motion which induces overestimations of turbulence intensity and potentially imprecise measurements of horizontal wind speed. This research aims to reduce this uncertainty by applying motion compensation to three wind field reconstruction techniques (scalar, vector, and hybrid) to assess the amelioration on horizontal wind speed and turbulence intensity; especially how motion compensation can improve scalar and hybrid outputs. A three-month dataset of a floating lidar system (FLS) was compared to a nearby fixed lidar. The results demonstrate that motion compensation provides an improvement for horizontal wind speed measurement but inflates the overestimation of turbulence intensity from the floating lidar system. Though scalar reconstruction measurements provide slightly more accurate data compared to hybrid reconstructed measurements, motion compensation helps limit the bias disparity; ergo, applying motion compensation reduces the differences in bias for scalar and hybrid reconstructed measurements. This study results in two conclusions. Basic motion compensation provides a notable amelioration for horizontal wind speed FLS measurements, yet further research must be conducted to find a suitable method to improve turbulence intensity. Additionally, the scalar reconstruction method still slightly outperforms hybrid reconstruction in offshore measurement results, but motion compensation aids in limiting the differences between the two.
The lidar probe volume averaging effect: A wind tunnel investigation in streamwise turbulence with continuous-wave lidar
Authors: Uluocak Sinem , Theuer Frauke , Neuhaus Lars , Zúňiga Inestroza Manuel Alejandro , Hulsman Paul , Kühn Martin
Published: June 2024

The main limitation of lidars to capture the turbulence is the filtering of small-scale fluctuations within the probe volume, which is far less significant with conventional anemometers. In this study, the probe volume averaging effect on the streamwise turbulence statistics is investigated in the wind tunnel. Different turbulent flows, which exhibit distinct turbulence intensities and integral length scales are generated and subsequently captured using a short-range continuous-wave WindScanner with different probe volumes. Hot wire measurements are performed as a reference. The results indicate that the turbulence intensity (TI) is underestimated using conventional lidar methods compared to the hot wire measurements. The relative error increases with the increasing ratio of probe volume over integral length scale (l
p
/L) which is the indication of probe volume averaging. The TI is underestimated by 4 % at l
p
/L = 0.5 and by 63 % at the largest tested l
p
/L = 11.3 with the conventional lidar method. However, the TI estimated from the averaged Doppler spectrum of the lidar compensates for the probe volume averaging effect and shows a better agreement with the hot wire measurements with an average overestimation of 7.8 %. This study shows that the continuous-wave lidars have the potential to estimate the TI under different flow conditions using the averaged Doppler spectrum method.
Framework for Design Validation of Control Algorithms used on Mechanical Hardware-in-the-Loop Nacelle Test Rigs
Authors: Feindt Oliver , Siddiqui Muhammad Omer , Zuga Adam , Thomas Philipp
Published: June 2024

Full scale multi-MW Wind Energy Converter (WEC) nacelles including the mechanical and electrical components as well as the main and converter control systems, are tested at the Dynamic Nacelle Testing Laboratory (DyNaLab) at Fraunhofer IWES. A Hardware-in-the-Loop environment emulates the missing rotor and accurately applies torque to the nacelle via a motor and the test rig drivetrain. In a current nacelle test campaign, several structural changes have been made to reduce the necessary adaptor tolerances of the test rig interface and to include the nacelle hub. These changes can potentially impact the system’s dynamic response and controller performance. This paper presents the current research with a focus on improving the control algorithms for accurate application of the rotor torque by implementing a simulative validation framework. The objectives of this framework include addressing the impact of modifications to the test rig drivetrain, variations in the nacelle adaptor, and communication delays between real-time simulation models, the test rig, and the WEC controller prior to performing tests on the real system. The methodology shown therefore involves three steps: controller design using a simplified multi-mass torsional model of all components necessary for testing, validation with a high-fidelity virtual test bench model and testing the resulting controller performance during the commissioning phase for the actual test. Overall, this approach is expected to reduce the commissioning duration, enhance the performance of the test rig, and identify the limitations in achieving stable torque control before the application to the real wind turbine nacelle on the test rig.
Correlation of Blade Loading with SpinnerLidar-Measured Inflow
Authors: Herges Tommy , Houck Dan , Kelley Christopher
Published: June 2024

The Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) project’s main objective was collecting data for validation of aerodynamic and aeroelastic codes for large, flexible rotors. These data come from scanning lidars of the inflow and wake, met tower, profiling lidar, blade deflection from photogrammetry, turbine SCADA data (including root bending loads), and hub-mounted SpinnerLidar inflow measurements. The goal of the present work is to analyze various methods to align the SpinnerLidar inflow data in time and space with individual blade loading. These methods would prove a way of analyzing turbine response while estimating the flowfield at each blade and provide a way of improving turbine response understanding using field data in real time, not just from simulations. The hub-mounted SpinnerLidar measures the inflow in the rotor frame meaning the locations of the blades relative to the measurement pattern do not change. The present work outlines some methods for correlating the SpinnerLidar inflow measurements with root bending loads in the rotor frame of reference accounting for both changes in wind speed and rotor speed from the measurement location one diameter upstream to each blade.
Introducing a partial bottom-up model for onshore wind turbine CAPEX estimation
Authors: Reichartz T , Jacobs G , Oertmann A , Blickwedel L , Schelenz R
Published: June 2024

European wind turbine manufacturers are under significant cost pressure. To minimize risk in investment and development decisions, it is crucial to identify cost drivers, especially given the high volatility of resource prices. This study presents a cost model that breaks down the component costs of onshore horizontal axis gearbox or direct drive wind turbines. Individual sub-models are developed for each component, using existing models where available. Production costs of steel towers, rotor blades and gearboxes are modeled, following a bottom-up approach. The calculated main investment costs for a 4.4 MW onshore wind turbine in 2023 are 1311 €/kW. These results are approximately 15 % higher than those of a previous study conducted in Germany in 2022. The increase is attributed to price developments, high inflation, and the fact that manufacturers have been selling turbines at a loss, thus underestimating turbine costs in the recent past. The model is a valuable tool for researchers, OEMs, and policy makers to analyze manufacturing and material costs. It facilitates informed decision-making about future research needs in the face of economic challenges.
Wind farm modelling of tilted rotors
Authors: Vimalakanthan Kisorthman , Bot Edwin , Engels Wouter
Published: June 2024

This study explores the benefits of tilted rotors in wind farms, focusing on a 3x3 grid of 10MW turbines and a 153-turbine farm modelled after the Hollandse-Kust West site with 15MW turbines. Computational Fluid Dynamics models ranging from mid to higher order, including FarmFlow (Parabolic RANS), TNO-VBM (RANS), and NREL-SOWFA (LES), were validated against CL-Windcon wind tunnel measurements.

Comparisons between low and high-order CFD models reveal good agreement, with LES indicating increased wake mixing. Tilted rotor simulations align well, except for the lower-order model, deviating at extreme tilt angles. Positive tilts in a 3x3 farm result in a 10% increase in sub-rated wind farm yield and a 26% reduction in peak thrust loads during post-rated conditions. However, for larger wind farm clusters, tilting the rotors can increase the production of some parts within while its overall production is not increased. The investigated results depend on the tested scenario, thus further exploration is recommended.
Using high sensitivity DC accelerometers for torque estimation on a wind turbine gearbox
Authors: Smeets V , Bosmans J , Gallas S , Kirchner M , Geens L , Ortmann S , Naets F
Published: June 2024

In this paper we propose a novel approach to estimate the torque on a wind turbine gearbox using single-axis high sensitivity DC accelerometers. The torque estimator uses an Augmented Extended Kalman Filter (AEKF), combining physics-based models with measurement data. The accelerometer sensor model is derived by analysing the measurements, leading to the identification of two torque-driven acceleration contributions: acceleration due to force excitation and acceleration due to a change in measurement direction. For the latter, two different sources, with distinct frequency content, are capable of changing the measurement direction. The first source corresponds to rigid body motion (f < 0.15Hz), the second one to ring gear deformation (0.15 < f < 4Hz). Using this information, the measured signal can be filtered to target specific acceleration contributions. The first torque estimation approach targets frequencies from 0.15 to 4Hz and produces a Normalised Mean Absolute Error (NMAE) of 2.12%. The second approach targets frequencies up to 0.15Hz, and will estimate the rigid body deflection angle. The torque is calculated through a linear identified relation between the rigid body angle and applied input torque. This approach yields an NMAE of 2.59%. This leads to the conclusion that high sensitivity DC accelerometers are a valid option for estimating torque on a wind turbine gearbox. These torque monitoring approaches will pave the way for advanced condition monitoring strategies and the calculation of remaining useful lifetime of the gearbox.
Simulation of a conventionally neutral boundary layer with two-equation URANS
Authors: Baungaard M , Van Der Laan M P , Kelly M , Hodgson E L
Published: June 2024

Simulating conventionally neutral boundary layers (CNBLs) with the unsteady Reynolds-Averaged Navier-Stokes (URANS) technique is investigated in this paper using a modified two-equation linear eddy viscosity turbulence model. For CNBLs over a flat and uniform surface, as typically used as the inflow to wind farm simulations, the governing equations of URANS can be solved with a one-dimensional solver, which makes the simulation of a typical CNBL five to six orders of magnitude faster than with large-eddy simulation (LES) approaches. However, URANS on the other hand requires more modelling than LES, and its accuracy is heavily dependent on the turbulence model employed. Through a cross-code study of a CNBL case with data from five different LES codes, it is found that the length-scale limiter of the employed turbulence model should be removed to correctly predict the atmospheric boundary layer (ABL) height evolution and the qualitative shape of various atmospheric profiles. A parametric study of simulations with varying initial ABL height further demonstrates the prediction capabilities of URANS, although a comparison with LES data shows that modelling of turbulence anisotropy and near-surface turbulence could be improved.
Feasibility analysis of a novel conical plain bearing with rotating segments as wind turbine main bearing
Authors: Euler J , Jacobs G , Zweiffel U , Jakobs T , Decker T , Röder J
Published: June 2024

Further expansion of wind energy is a key aspect for the transition towards renewable energy production. Operational expenditures constitute a significant portion of the cost of wind energy. One key aspect within these costs are maintenance costs. Main bearings of wind turbines suffer from comparably high failure rates. The exchange of a wind turbine main bearing is especially costly, as the rotor is dismounted and the drivetrain needs to be dismantled. Currently all commercially available wind turbine main bearings are roller bearings. One approach to reduce the operational costs is to develop segmented plain bearings as wind turbine main bearings. The segmented design allows for repair on tower without dismantling the drivetrain. In this work a novel, flexible plain bearing concept as wind turbine main bearing is presented. Key feature of this novel concept are its sliding segments that are mounted to the rotating main shaft. In contrast to designs which segments are mounted to the still standing housing, the rotatable shaft allows the segments to be rotated into intended positions for inspections or maintenance. In case of failure, faulty segments can be exchanged in a preferred service position, which provides easy access. The novel concept is evaluated using elasto-hydrodynamic-multi-body simulations and compared to the already established FlexPad concept.
Buckling behavior of rotor blade sandwich panels with spatially distributed material uncertainties
Authors: Prigge F , Balzani C
Published: June 2024

The study evaluates the impact of material uncertainties on the buckling behavior of sandwich panels in wind turbine rotor blades. The analysis is limited to linear buckling and is performed using stochastic finite element Monte Carlo simulation on a rectangular and flat submodel of the rotor blade’s trailing edge panel. The finite element model of the panels is simply supported on all edges. To generate the spatial material property distributions, the Karhunen-Loève expansion is used in combination with Latin hypercube sampling. The results compare the effects of various correlation lengths of the spatial distributions. The buckling loads vary in correlation to the average panel stiffness caused by the random distributions. The spatial distribution has a less dominant effect, reducing the mean value of the buckling load results. The amount of reduction in buckling load is highest when the correlation length of the distribution is close to the harmonic half-wave of the dominant buckling shape.
Federated Learning and Unlearning as Enablers of Wind Turbine Digital Twins
Authors: Stadtmann Florian , Rasheed Adil
Published: June 2024

Real-time capable models are paramount for the successful adaptation of digital twin technology into industries such as wind energy, but high-fidelity physics-based models cannot achieve the required speed, while data-driven and hybrid methods require large amounts of training data which is typically confidential. In this work, the combination of federated learning with hybrid modeling is proposed to train fast and reliable models across multiple confidential data sets owned by different stakeholders. The approach is demonstrated on physics-guided neural networks to estimate the lift and drag of wind turbine airfoils. A scenario is devised where multiple confidential data subsets are confined to different client devices. It is shown that the physics-guided neural networks can be trained through federated learning across those devices and data subsets and that the resulting models can recover and even surpass the accuracy of a model that is trained conventionally by merging the data subsets on a single device. The presented approach is highly scalable and can be easily adapted to many other applications. This work also discusses federated unlearning methods, which allow data owners to remove all traces of a data subset used in training if they decide to revoke their contribution.
Large-eddy simulation study on wave-wake interactions past an offshore wind turbine
Authors: Ciri Umberto
Published: June 2024

Large-eddy simulations (LES) of the flow past an offshore wind turbine under different underlying wave fields have been performed. A one-way coupling between the air and water flows has been realized through a hybrid immersed-boundary/level-set method. The velocity in the water domain is forced with the potential flow solution, while the response of the atmospheric boundary layer to the changes in the sea-surface height (tracked by the level-set equation) is simulated with a LES approach coupled to a rotating actuator disk model to mimic the effect of the wind turbine. A parametric study has been performed varying the wave period and wavelength while keeping the amplitude constant, resulting in different wave age parameters ranging from young developing waves to old swell waves. The wave field has a significant effect on the lower region of the atmospheric boundary layer, slowing down the wind field in proximity of the air-water interface and considerably increasing the local turbulence kinetic energy (TKE). The interaction between the wave evolution and the TKE results in a non-monotonic trend of the wake recovery rate with the wave age, when compared to the baseline value in the wake of a turbine over a flat wall. Both developed waves results in a lower recovery rate, whereas intermediate-age waves present a larger value than the baseline. The increased TKE in the lower layers of the rotor revolution induces an increased fluctuating component in the power production and blade loads. Nevertheless, for the parameters considered in this study, the spectra of the blade loads do not show a clear signature at the wave frequency, but the increased fluctuating component occurs over a broad range of frequencies.
Effects on the resolution of the near-wake by the use of different meshes using RANS and URANS
Authors: Aa Madsen M H , Zahle F , Sørensen N N , Bottasso C L
Published: June 2024

This work compares the wake resolution for several meshes in order to find an accurate yet computationally efficient grid. The mesh is identified through a parametric study where the wake resolution is gradually increased using up to 55 million cells. The mesh is designed in order to support CFD-based shape design, considering not only the wind turbine power but also an improved wake recovery. Furthermore, a comparison between steady-state and transient RANS formulations is presented. The proposed meshing should allow for extending a recently published CFD-based shape design focusing on wind turbine power, to also include wake behavior in an optimization context.
SEAHOWL: Partitioned Multiphysics and Multifidelity Modelling of Wind Turbines with Monolithically Coupled Elastodynamics
Authors: De Lataillade Tristan , Yu Wenchao , Pallud Maxime , Capaldo Matteo
Published: June 2024

We introduce SEAHOWL (Servo-Elasto-Aero-Hydro Offshore Wind Lab), a novel multiphysics multifidelity simulation framework for large-scale onshore, offshore, and floating wind turbines. For structural dynamics, multibody and finite element problems are coupled monolithically and solved within a single system of equations through Project Chrono, providing high numerical stability and optimal coupling accuracy. Combined with partitioned multiphysics, modularity is at the heart of the framework with various numerical methods and solvers (internal or external) available for each physics. This flexibility allows for the selection a target trade-off between numerical accuracy and computational efficiency on a use-case basis. Simulations showcased here have been selected through the prism of industrial R&D challenges, covering: controller response and loads over the operational wind range, 3P effect for different levels of fidelity for the blades, floating wind turbine response under irregular waves and turbulent wind, and innovative multibody application for 3P fatigue mitigation. SEAHOWL is cross-validated against the reference OpenFAST whole-turbine simulator from NREL, showing overall good agreement while differences between the two frameworks are highlighted.
Opensource machine learning metamodels for assessing blade performance impairment due to general leading edge degradation
Authors: Castorrini A. , Ortolani A. , Minisci E. , Campobasso M. S.
Published: June 2024

Blades leading edge erosion can significantly reduce annual energy production of wind turbines. Accurate estimates of the resulting blade performance impairment are paramount to predict the resulting energy losses and enable cost-informed decisions on optimal maintenance and operational strategies, maximizing energy production and reducing maintenance costs. Computational Fluid Dynamics (CFD) is a robust approach for predicting the performance losses due to LEE. However, the impact of the damage on blade aerodynamics varies depending on damage pattern, extent and location. Therefore, direct CFD simulation of a sufficiently general set of damaged blades is computationally not viable in industrial applications, since the energy loss assessment needs to be performed for hundreds of turbines at many times of the wind farm operation. To address this issue, previous studies showed how CFD can be used to train machine learning metamodels of the perfomance of damaged blade sections, enabling the definition of multi-fidelity energy loss prediction systems. This study presents improved metamodels, using validated CFD to generate training datasets that cover a more general and wider range of erosion patterns, from low-amplitude roughness to severe grooves. In order to provide the industry with additional erosion geometry-linked tools for estimating energy yield losses, and foster further research and development in this area, the developed meta-models have been made available online with unrestricted access.
An open-source Python-based tool for Mann turbulence generation with constraints and non-Gaussian capabilities
Authors: Dimitrov Nikolay , Pedersen Mads , Hannesdóttir Ásta
Published: June 2024

This study presents Hipersim, an efficient, open-source, Python-based turbulence box generation tool. Hipersim combines Mann turbulence generation, application of constraints, and non-Gaussian turbulence generation, features that have not been available previously in a single package. The output of the tool is compatible with popular aeroelastic simulation tool formats (Hawc2 and TurbSim). Hipersim also has significantly better computational efficiency than earlier tools, as well as the possibility for parallelization. The source code is publicly accessible, and the tool is also available to install as a Python library. The present paper outlines the capabilities of Hipersim, explains some of the distinctive features of its implementation, and provides brief validation and comparison with similar tools.