Abstract. In experiments to validate wind turbine design codes, the full inflow field moving into the rotor is never measured. Instead, it is reconstructed from spatially limited measurements by using an atmospheric model. As such, the inflow represents a source of uncertainty when validating turbine models. Here, we characterize the behavior and accuracy of modern inflow reconstruction techniques. We compare eight inflow models for nine ~10 minute reference inflows, three from a real-world experiment with a 2.8 MW turbine and six from a synthetic field campaign. We document the models' differences in time series behavior and statistical characteristics like mean profiles, turbulence intensity, and power spectra. Across all case studies, the Superstatistical Mann model had the smallest root mean square error (average of 0.93 m s-1), and TurbSim had the largest (average of 1.19 m s-1). PyConTurb performed similarly to the inflows based on the Mann model. Notably, error time series showed synchronized spikes across models, often corresponding to physically coherent features that were not observed in the hub-height measurements. This study points toward areas for future inflow reconstruction model development, and it provides the foundation for future work that will examine turbine load validation errors in conjunction with inflow errors.

Abstract. Estimating the site-specific fatigue reliability of wind turbines is an integral part of probabilistic lifetime extension assessment. Limitations in type, accuracy, and availability of site-specific data is one of the main challenges in such estimations. The present research tackles the challenge of estimating long-term fatigue loads using short-term strain gauge measurements via statistical extrapolation. The case study wind turbine is a Siemens 2.3 MW, in the Lillgrund wind farm, located in the Øresund strait between Denmark and Sweden. The turbine is heavily instrumented and Supervisory Control and Data Acquisition (SCADA) is also available. The study also reassesses the performance of the Frandsen model – as a simplified approach for estimating higher turbulence due to wakes – in a compact wind farm layout using aeroelastic simulations of the case study wind turbine. Furthermore, it shows the sensitivity of the site-specific reliability with respect to the uncertainty in material strength, fatigue load, and damage accumulation model. The results reveal that for the case-study site, the Frandsen model underestimates turbulence in below-rated mean wind speeds and overestimates the turbulence in above-rated mean wind speeds. However, using the Frandsen model for estimating the long-term fatigue loads in the case study location leads to a 35 % lower reliability index than a site-specific assessment using data from the SCADA system and, thus, is relatively more conservative. The study reveals that the sensitivity of the fatigue reliability to the load’s uncertainty is negligible in assessment using site measurements and relatively high when using the Frandsen model. The extrapolation approach used in the current study can facilitate the use of digital twins when strain gauge measurements are unavailable for a part or the whole span of the lifetime. In addition, the assessment of the Frandsen model in the case study wind farm, as an example of a wind farm with short spacing, adds valuable information to the ongoing studies in the literature about the performance of the model in intense and mixed-waked conditions. Finally, the provided information about robustness of the reliability based on the load estimation approach, is useful for considering uncertainty in the lifetime extension assessment.

This study evaluates a numerical multi-beam pulsed lidar mounted on the hub of the NREL 5MW reference wind turbine using the HAWC2 v13.1 numerical sensor for synthetic lidar measurement generation. While initially designed for single-beam operations, it facilitates multi-beam configuration simulations. We conducted an analysis of full-rotor longitudinal wind speed reconstruction by combining inverse-distance-weighting with synthetic sensor data from HAWC2. Utilizing a Mann-generated turbulence box for wind input at U = 11.4 m/s, we examined three lidar configurations for efficacy. The hub-mounted lidar proved efficient in capturing the incoming flow towards the turbine, showing a 60% improvement in overall reconstruction accuracy across the plane compared to the baseline, where hublidar measurements are simple average across the rotor plane. The rotor average wind speed showed a 30% enhancement compared to the baseline. Crucially, the lidar configuration, which impacted the spatial distribution across the rotor plane, emerged as a pivotal factor for effective reconstruction. Proper configuration assessment is essential, especially given the implications of rotational sampling and its impact under various wind conditions, for optimal performance. The proposed method, combining inverse-distance weighting with hub-lidar data for high spatial resolution measurements across the rotor plane, shows significant potential for real-time windflow estimation and lidar-assisted control applications.

Wind turbines are growing in size and increasingly suffer from aeroelastic instabilities. Unfortunately, numerical models often show inconsistent results during verification studies. We address this gap by first introducing novel linearization capabilities within the open-source aero-hydro-servo-elastic framework OpenFAST. Next, a code-to-code benchmark study is presented that compares modal parameters between OpenFAST and HAWCStab2 for a land-based version of the International Energy Agency 15-MW reference wind turbine modeled with quasi-steady aerodynamics. The two solvers are in strong agreement except for discrepancies in the second rotor flapwise modes. The differences are attributed to the torsional flexibility of the tower, which is assumed torsionally stiff in the OpenFAST model. Work is ongoing to close this modeling gap. The aeroelastic stability of a low-specific-power land-based wind turbine is also investigated. The impact of design choices is discussed, high-lighting how narrow the margins are between a stable design and an unstable design.

Fatigue assessment of wind turbines involves three main sources of uncertainty: material resistance, load, and the damage accumulation model. Many studies focus on increasing the accuracy of fatigue load assessment to improve the fatigue reliability. Probabilistic modeling of the wind's turbulence standard deviation is an example of an approach used for this purpose. Editions 3 and 4 of the IEC standard for the design of wind energy generation systems (IEC 61400-1) suggest different probability distributions as alternatives for the representative turbulence in the normal turbulence model (NTM) of edition 1. There are debates on whether the suggested distributions provide conservative reliability levels, as the established design safety factors are calibrated based on the representative turbulence approach. The current study addresses the debate by comparing annual reliability based on different scenarios of NTM using a probabilistic approach. More importantly, it elaborates on the relative importance of load assessment accuracy in defining the fatigue reliability. Using the DTU 10 MW reference wind turbine and the first-order reliability method (FORM), we study the changes in the annual reliability level and its sensitivity to the three main random inputs. We perform the study considering the blade root flapwise and the tower base fore–aft moments, assuming different fatigue exponents in each load channel. The results show that integration over distributions of turbulence in each mean wind speed results in less conservative annual reliability levels than representative turbulence. The difference in the reliability levels varies according to turbulence distribution and the fatigue exponent. In the case of the tower base, the difference in the annual reliability index after 20 years can be up to 50 %. However, the model and material uncertainty have much higher effects on the reliability levels compared to load uncertainty. Knowledge about such differences in the reliability levels due to the choice of turbulence distribution is especially important, as it impacts the extent of lifetime extension through reliability reassessments.

Wind energy is foundational for achieving 100% renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

The variability of the wind turbine loads complicates fatigue assessment in the design phase, as performing simulations covering the entire lifetime is computationally expensive. The current work provides important information for assessing the uncertainty in fatigue damage estimation due to finite data. We study the sample size effect on mean, variance, and skewness of damage in each wind bin, identify the important wind bins, and study the uncertainty propagation from each wind bin to the lifetime damage using 3600 aeroelastic simulations and bootstrapping. To achieve less than 1% error in the damage estimation across all load channels in the current case study, at least 100 turbulence seeds are needed. Damage in different wind bins follows a lognormal distribution when using the conventional approach of six seeds. The provided insights and information allow the designer to achieve a specific level of accuracy for a given computational cost using strategic bin sampling.

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade’s load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

Nacelle-mounted lidar measurements offer the opportunity to tailor turbulence to specific conditions, reducing aeroelastic uncertainty in one-to-one wind turbine validation and opening the door for novel lidar-based control methodologies. Despite this, the use of lidar to generate constrained turbulence is not commonplace, partially due to a lack of readily available tools. Kaimal-based constrained turbulence methods—such as the one implemented in the open-source constrained-turbulence generator PyConTurb—can be used to easily generate turbulence constrained to measurements in a matter of minutes. Unfortunately, the limitations of the Kaimal-based methods prevent the direct use of the lidar data as constraints. This paper therefore presents a preprocessing methodology to convert lidar data to PyConTurb-ready constraints. The method is demonstrated by quantifying the aeroelastic uncertainty for the DTU 10 MW and the NREL 5 MW without constraints and comparing it to the corresponding value with lidar constraints. The results show an excellent reduction in the one-to-one aeroelastic uncertainty and an adequate reduction on fatigue loads when lidar-constrained turbulence is used as inflow. The NREL 5 MW is found to have better reduction in uncertainty due to a rotor size that is better suited to the lidar geometry.

The problem of negative damping undermines the aeroservoelastic stability of floating offshore wind turbines. The negative damping problem is most prevalent around rated wind speed, where the sensitivity of thrust to wind speed is the largest. This paper investigates the implementation of peak shaving, a controller feature that limits the rated thrust by pitching the blades before rated wind speed is reached. Two controller designs are investigated: a de-tuned controller and a nacelle-feedback controller. A time-domain metric is defined, inspired by Lyapunov theory, in order to compute and assess the stability of floating offshore wind turbines. The model of the International Energy Agency 15-MW reference wind turbine mounted on the University of Maine VolturnUS-S floater is simulated in HAWC2 with the National Renewable Energy Laboratory reference open-source controller. Peak shaving is applied to the two controller designs and stability is assessed. According to the chosen metric, peak shaving does not improve the stability of the system. This is due to the trade-off between loads and error tracking: although the loads and displacements in the fore-aft direction of the turbine are reduced, the rotor-speed tracking is poorer, which increases the shaft torsion fatigue load.

Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the spatial variation of turbulence statistics is a tempting alternative, but the accuracy of these models with respect to the aeroelastic response is not well understood. This paper therefore highlights the effect of replacing wake meandering with spatially varying statistics (“profile functions”) in the inflow to a downstream turbine. Profile functions at different downstream and lateral locations are extracted from a large-eddy simulation with an upstream turbine and compared with two lower-fidelity models: one that prescribes both the mean and standard deviation of the turbulence and one that prescribes only the mean. The aeroelastic response of an NREL 5 MW wind turbine is simulated with the three different wake-model fidelities, and various quantities of interest are compared. The mean values for the power and rotor speed for the medium-and low-fidelity model match well, but the accuracy of the fatigue loads varies greatly depending on the load channel. Prescribing the profile function for the standard deviation is only beneficial for the tower-base fore-aft moment; all other DELs had similar accuracies for both the medium- and low-fidelity models. The paper concludes that blade DELs can be estimated using these simple models with some accuracy, but care should be taken with the load channels related to the shaft torsion and tower-base fore-aft bending moment.

Reference wind turbines (RWTs) that reflect the state-of-the-art of current wind energy technology are necessary in order to properly evaluate innovative methods in wind turbine design and evaluation. The International Energy Agency (IEA) Wind Technology Collaboration Platform (TCP) Task 37 has recently developed a new RWT geared towards offshore floating-foundation applications: the IEA Wind 15 MW. The model has been implemented in two aeroelastic codes, OpenFAST and HAWC2, based on an underlying common ontology. However, these toolchains result in slightly different structural parameters, and the two codes utilise different structural models. Thus, to increase the utility of the model, it is necessary to compare the aeroelastic responses. This paper compares aeroelastic loads calculated using different fidelities of the blade model in OpenFAST (ElastoDyn and BeamDyn) and HAWC2 (prismatic Timoshenko without torsion and Timoshenko with fully populated stiffness matrix), where both codes use the DTU Basic controller and the same turbulence boxes to reduce discrepancies. The aeroelastic responses to steady wind, step wind and turbulent wind (per IEC 61400-1 wind class IB) are considered. The results indicate a generally good agreement between the loads dominated by aerodynamic thrust and force, especially for the no-torsion blade models. Discrepancies were observed in other load channels, partially due to differences in the asymmetric loading of the rotor and partially due to differing closed-loop dynamics, and they will be the subject of future investigations.

This report describes a 15-megawatt offshore wind turbine with a fixed-bottom monopile support structure. This reference wind turbine is a Class IB direct-drive machine, with a rotor diameter of 240 meters and a hub height of 150 meters. An overview of the design is presented in Figure ES-1 and Table ES-1. The design reflects a joint effort between the National Renewable Energy Laboratory, sponsored by the U.S. Department of Energy, and the Technical University of Denmark, sponsored by the European Union's H2020 Program, through the second work package of International Energy Agency Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. A forthcoming report will detail a semisubmersible floating support structure developed by the University of Maine.

This paper investigates the effect that adding constraints to turbulence simulations has on the uncertainty of resulting aeroelastic loads. The constrained turbulence is generated using the open-source constrained turbulence generator PyConTurb ("Python Constrained Turbulence"). A selection of constraint patterns were used to mimic the design of a met mast layout; i.e., the number of sonic anemometers and their locations throughout the rotor. A case study is presented to demonstrate in detail the effects of adding constraints before a larger numerical experiment is presented. The results of the numerical experiment indicate that adding constraints is extremely beneficial in reducing the mean absolute error of both operational parameters and loads. The reduction in mean absolute error ranged from 13% to 98%. The error in the extreme values and damage-equivalent loads were not impacted by the added constraints due to lack of gusts in the original signals and the similarity of the power spectra of the constrained and non-constrained signals, respectively.

Remote wind sensing technologies, such as lidar, are becoming more and more mature and the wind energy industry is rapidly adopting them for a variety of purposes.One of these use cases is utilizing lidar measurements from a nacelle mounted device in order to improve the accuracy of load simulations by creating more realistic synthetic wind inputs.In this work we present an open source numerical framework, called ViConDAR for "Virtual Constrained turbulence and liDAR measurements", used for simulating lidar measurements and applying them as constraints in synthetic wind field generation.A realistic lidar simulator is used to obtain the virtual lidar measurements by scanning a synthetic wind field.These measurements are fed to open source constrained turbulence generation codes (TurbSim and PyConTurb), coupled to ViConDAR.The resulting constrained wind fields are compared to the original ones in order to quantify the level of convergence and can be used directly as inputs to aeroelastic simulations.Finally, two indicative applications of this framework are shown.First, a sensitivity analysis of the lidar parameters versus varying atmospheric conditions is carried out to investigate the potential of the lidar measurements to capture the wind field properties.Secondly, a sensitivity analysis is presented on the influence of different lidar parameters on the convergence of the full wind fields comparing both turbulence generation codes under varying atmospheric conditions.

This paper presents an open-source tool that can be used to simulate turbulence boxes constrained by measured data, which is useful for wind turbine model validation. The tool, called PyConTurb for “Python Constrained Turbulence”, uses a novel algorithm based on the Kaimal Spectrum with Exponential Coherence method, and the algorithm can efficiently generate turbulence boxes under a wide variety of measurement constraints. The theoretical background for the technique is presented along with a few notes on its implementation in Python. The utility of PyConTurb is demonstrated using real data measured using three-dimensional sonic anemometers at the Denmark Technical University Risø campus. The presented results demonstrate that PyConTurb can successfully generate turbulence boxes from real measured data, including recreating the desired spatial coherence relationships between the simulated and measured time series. PyConTurb is shown to be a promising tool for investigating new spatial coherence models and for future one-to-one wind turbine validation studies.

To fully understand how loads and turbine cost scale with turbine size, it is necessary to have identical turbine models that have been scaled to different rated powers. The report presents the WindPACT baseline models, which are a series of four baseline models that were designed to facilitate investigations into the scalings of loads and turbine cost with size. The models have four different rated powers (750 kW, 1.5 MW, 3.0 MW, and 5.0 MW), and each model was designed to its specified rated power using the same design methodology. The models were originally implemented in FAST_AD, the predecessor to NREL's open-source wind turbine simulator FAST, but have yet to be implemented in FAST. This report contains the specifications for all four WindPACT baseline models - including structural, aerodynamic, and control specifications - along with the inherent assumptions and equations that were used to calculate the model parameters. It is hoped that these baseline models will serve as extremely useful resources for investigations into the scalings of costs, loads, or optimization routines.