Fore-aft Bending Moment Research Articles

Validation of new and existing methods for time-domain simulations of turbulence and loads

We seek to obtain a second-by-second match between the simulated and measured structural loads of a utility-scale wind turbine. To obtain the one-to-one load simulations, we start with the furthest upstream component of the modeling chain: the turbulent inflow. We consider new and existing methods to generate constrained-turbulence flow fields. The new method is based on large-eddy simulations (LES) and machine learning (ML). The existing methods include Kaimal-based TurbSim and the superstatistical wind field model. The inflow measurements used to constrain these simulations are obtained with a nacelle-mounted scanning lidar. We compare the flow fields for the different inflow simulation approaches and validate their associated load predictions against measurements collected in the Rotor Aero-dynamics, Aeroelastics, and Wake (RAAW) field campaign. We find that the rotor-position control developed for this study is key in enabling the time match between measurements and simulations. When this control approach is used, the load simulation performance tracks with the inflow simulation fidelity, with LES+ML yielding errors ≤ 4% for the damage-equivalent loads of flapwise bending moment, and tower fore-aft bending moments.

OC6 project Phase IV: validation of numerical models for novel floating offshore wind support structures

Abstract. This paper provides a summary of the work done within Phase IV of the Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project, under International Energy Agency Wind Technology Collaboration Programme Task 30. This phase focused on validating the loading on and motion of a novel floating offshore wind system. Numerical models of a 3.6 MW horizontal-axis wind turbine atop the TetraSpar floating support structure were compared using measurement data from a 1:43-Froude-scale test performed in the University of Maine's Alfond Wind–Wave (W2) Ocean Engineering Laboratory. Participants in the project ran a series of simulations, including system equilibrium, surge offsets, free-decay tests, wind-only conditions, wave-only conditions, and a combination of wind and wave conditions. Validation of the models was performed by comparing the aerodynamic loading, floating support structure motion, tower base loading, mooring line tensions, and keel line tensions. The results show a relatively good estimation of the aerodynamic loading and a reasonable estimation of the platform motion and tower base fore–aft bending moment. However, there is a significant dispersion in the dynamic loading for the upwind mooring line. Very good agreement was observed between most of the numerical models and the experiment for the keel line tensions.

Probabilistic Design Methods for Gust-Based Loads on Wind Turbines

The IEC 61400-1 standard specifies design load cases (DLCs) to be considered in the design of wind turbine structures. Specifically, DLC 2.3 considers the occurrence of a gust while the turbine shuts down due to an electrical fault. Originally, this load case used a deterministic wind event called the extreme operating gust (EOG), but the standard now also includes an approach for calculating the extreme response based on stochastic simulations with turbulent wind. This study presents and compares existing approaches with novel probabilistic design approaches for DLC 2.3 based on simulations with turbulent wind. First, a semiprobabilistic approach is proposed, where the inverse first-order reliability method (iFORM) is used for the extrapolation of the response for electrical faults occurring at a given rate. Next, three probabilistic approaches are formulated for the calculation of the reliability index, which differs in how the aggregation is performed over wind conditions and whether faults are modeled using a Poisson distribution or just by the rate. An example illustrates the methods considering the tower fore-aft bending moment at the tower base and shows that the approach based on iFORM can lead to reductions in material usage compared to the existing methods. For reliability assessment, the probabilistic approach using the Poisson process is needed for high failure rates, and the reliabilities obtained for designs using all semiprobabilistic methods are above the target level, indicating that further reductions may be obtained via the use of probabilistic design methods.

Hydroelastic response of a flexible spar floating wind turbine: Numerical modelling and validation

This study examines the hydroelastic response of a large floating wind turbine (FWT) through coupled numerical analysis. Four numerical models with different levels of complexity in the hydrodynamic and structural modelling were established for a scaled 10 MW elastic spar FWT using the engineering tool SIMA. Two of them model the platform as a flexible body with distributed wave loads based on either Morison’s equation or based on potential flow theory combined with Morison-type drag. The two other models consider the platform as a rigid body. These models were calibrated and validated against experimental results from a series of characterization tests (hammer test and decay test) and tests in waves (regular and irregular). Results for motions, strains, and tower base fore-aft bending moment are compared. All models predict damped natural periods for rigid body motions close to the experimental results from decay tests (within 4%). Overall, the wave-frequency responses (motion, strain on the platform, and tower base bending moment) in all models agree well with the experimental results in regular waves and irregular waves. For the structural internal loads around the 1st bending mode natural frequency, the distributed potential flow model agrees well with the experiments, while a large overestimation is seen in the flexible model with Morison’s equation.

Gaidai-Xing reliability method validation for 10-MW floating wind turbines

In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.

Evaluation of lidar-assisted wind turbine control under various turbulence characteristics

Abstract. Lidar systems installed on the nacelle of wind turbines can provide a preview of incoming turbulent wind. Lidar-assisted control (LAC) allows the turbine controller to react to changes in the wind before they affect the wind turbine. Currently, the most proven LAC technique is the collective pitch feedforward control, which has been found to be beneficial for load reduction. In literature, the benefits were mainly investigated using standard turbulence parameters suggested by the IEC 61400-1 standard and assuming Taylor's frozen hypothesis (the turbulence measured by the lidar propagates unchanged to the rotor). In reality, the turbulence spectrum and the spatial coherence change by the atmospheric stability conditions. Also, Taylor's frozen hypothesis does not take into account the coherence decay of turbulence in the longitudinal direction. In this work, we consider three atmospheric stability classes, unstable, neutral, and stable, and generate four-dimensional stochastic turbulence fields based on two models: the Mann model and the Kaimal model. The generated four-dimensional stochastic turbulence fields include realistic longitudinal coherence, thus avoiding assuming Taylor's frozen hypothesis. The Reference Open-Source Controller (ROSCO) by NREL is used as the baseline feedback-only controller. A reference lidar-assisted controller is developed and used to evaluate the benefit of LAC. Considering the NREL 5.0 MW reference wind turbine and a typical four-beam pulsed lidar system, it is found that the filter design of the LAC is not sensitive to the turbulence characteristics representative of the investigated atmospheric stability classes. The benefits of LAC are analyzed using the aeroelastic tool OpenFAST. According to the simulations, LAC's benefits are mainly the reductions in rotor speed variation (up to 40 %), tower fore–aft bending moment (up to 16.7 %), and power variation (up to 20 %). This work reveals that the benefits of LAC can depend on the turbulence models, the turbulence parameters, and the mean wind speed.

Floating wind turbine energy and fatigue loads estimation according to climate period scaled wind and waves

Offshore wind power is one of the fastest-growing renewable energy sources, as it is expected to play a major role in the transition towards sustainability and net zero emissions. Despite its potential, the interaction of the turbines with the oceanic waves, especially in case of floating turbines, is one of the main drawbacks associated to it. In fact, mechanical oscillations caused by the waves could potentially alter the operation and lifetime of the turbines. Hence, while the characterization of the wind is sufficient for the long-term design of onshore wind turbines, the procedure is more complex in case of offshore turbines, since the height, period and direction of the waves will affect the lifetime of the turbine. In this paper, a methodology for the evaluation of the energy generation and fatigue mechanical loads of a Floating Offshore Wind Turbine (FOWT) considering a 30-year period is proposed. To that end, meteorological data from 1991 to 2020 are characterized using a cluster analysis and reduced into a computationally affordable number of simulation cases. Results show negligible energy loss of a FOWT due to interaction with the oceanic waves. However, a substantial increment of the mechanical fatigue in the side-side and fore-aft bending moments of the tower are detected. Such analyses might be applied for the predictability of the lifetime of an offshore wind turbine, as well as the selection of potential optimal wind farm locations, based on climatic patterns and the evolution of meteorological data.

A novel design approach for estimation of extreme load responses of a 10-MW floating semi-submersible type wind turbine

Offshore structures are constructed to withstand extreme wind and wave-induced loads, so studying these extreme loads is vital as it allows offshore structures, e.g., wind turbines, to be designed and operated with minimal disruption. A novel statistical model that is precise and meticulous will facilitate these extreme load values to be estimated accurately. Bivariate average conditional exceedance rate (ACER2D) method was utilized in this paper. This multivariate statistical analysis is more appropriate than a univariate statistical analysis for complete structures, e.g., wind turbines, since it can extrapolate the extreme values with better accuracy. This paper uses this ACER2D method to explore a novel approach to estimating the extreme load responses of a 10-MW semi-submersible type floating wind turbine (FWT). Two cases are considered to understand the feasibility of the ACER2D on the extreme load responses. The first case analyses the blade root flap wise bending moment, while the second one analyses the tower bottom fore-aft bending moment. Based on the performance of the proposed novel method, the ACER2D method can offer better robust and precise bivariate predictions of the bending moments of the FWT.

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

Floating offshore wind turbines (FOWT) generate green renewable energy and are a vital part of the modern offshore wind energy industry. Robust predicting extreme offshore loads during FOWT operations is an important safety concern. Excessive structural bending moments may occur during certain sea conditions, posing an operational risk of structural damage. This paper uses the FAST code to analyze offshore wind turbine structural loads due to environmental loads acting on a specific FOWT under actual local environmental conditions. The work proposes a unique Gaidai-Fu-Xing structural reliability approach that is probably best suited for multi-dimensional structural responses that have been simulated or measured over a long period to produce relatively large ergodic time series. In the context of numerical simulation, unlike existing reliability approaches, the novel methodology does not need to re-start simulation again each time the system fails. As shown in this work, an accurate forecast of the probability of system failure can be made using measured structural response. Furthermore, traditional reliability techniques cannot effectively deal with large dimensionality systems and cross-correction across multiple dimensions. The paper aims to establish a state-of-the-art method for extracting essential information concerning extreme responses of the FOWT through simulated time-history data. Three key components of structural loads are analyzed, including the blade-root out-of-plane bending moment, tower fore-aft bending moment, and mooring line tension. The approach suggested in this study allows predicting failure probability efficiently for a non-linear multi-dimensional dynamic system as a whole.

A Straightforward Approach to Site-Wide Assessment of Wind Turbine Tower Lifetime Extension Potential

This contribution presents a novel methodology to evaluate the lifetime extension potential of wind turbines—taking towers as the key component that preserves onshore turbines’ structural integrity—as a consequence of the difference between design and site-specific loads. Specifically, attention is drawn to the site-specific wind direction distribution, which provides an additional source of lifetime extension potential. For this purpose, variants of closed-form solutions (based on the tower section’s normal stress) are developed to enable fatigue damage accumulation due to fore-aft and side-to-side bending moments at any point on the tower circumference without the need for further information on tower section geometry or material properties. Based on the degree of data availability, different scenarios are defined to estimate lifetime extension potential from the accurate tower’s normal stress and approximations using resultant bending moment, fore-aft bending moment, and finally, wind rose data only. The methodology is applied to a wind farm case study using the actual SCADA data with a partially validated turbine’s aeroelastic model to obtain operational loads. The results indicate that this quick and fairly accurate approach can be used as an initial stage in identifying wind turbines across large farms, which have the largest lifetime extension potential.

Results of fatigue measurement campaign on XL monopiles and early predictive models

In the present contribution, data from a measurement campaign on XL monopiles (with 9.5 MW turbines and water depths of up to 36 m) is presented. This campaign is based on data collected by three types of sensors: strain gauges (installed at the TP-tower interface), accelerations (taken at bottom, mid and upper levels of the tower) and SCADA data, comprising of wind speed, yaw angle, power, pitch angle and rpm. Additionally, wave and tidal data from a public Flemish maritime weather database is added. In a comparative analysis of XL monopiles’ and standard monopiles it was seen that the fatigue behaviour of the former radically departs from the latter, with side-to-side damage surpassing fore-aft for nominal operational conditions, and standstill fatigue damage generally being bigger than nominal. Furthermore, following a methodology described in previous OWI-Lab research, artificial neural network models are trained using the aforementioned sensor data to estimate the fore-aft and side-side tower bending moment damage equivalent loads (DEL), through the use of three months worth of data with a comparative analysis ensuing. Finally, the models’ performance is investigated for concrete operating conditions by correlating it with environmental and operating conditions (EOCs).

Effects of Simulation Length and Flexible Foundation on Long-Term Response Extrapolation of a Bottom-Fixed Offshore Wind Turbine

Abstract This paper deals with statistical and modeling uncertainty on the estimation of long-term extrapolated extreme responses in a monopile offshore wind turbine. The statistical uncertainty is addressed by studying the effect of simulation length. Modeling uncertainty is explored by evaluating the effects of considering a rigid and flexible foundation. The soil's flexibility is taken into account by considering the improved apparent fixity method. To identify the most relevant environmental conditions, the modified environmental contour method is used. The analysis focuses on the fore-aft shear force (FASF) and the fore-aft bending moment (FABM) at the mudline. The results show that using a simulation length of 10 min does not provide sufficient accuracy. It was found that for the FASF, simulation lengths of at least 30 min are required to achieve an accuracy of about +/−5%. For the FABM, it was found that both the extrapolations made with 20 min and 30 min simulations achieved similar levels of accuracy of about 20%. Meanwhile, the results obtained from 10 min simulations reached deviations of about 40%. Finally, from the comparison made between a rigid and flexible foundation, it was found that the extrapolated responses exhibit maximum deviations up to around 5% and 10% for the FASF and the FABM, respectively. Also, for the FABM, it was observed that the consideration of a flexible foundation causes the critical wind speed to shift from 16.5 m/s (rigid) to 18 m/s (flexible).

Optimal ancillary control for frequency regulation of wind turbine generator based on improved fatigue load sensitivity

Frequency regulation adjusts the power or torque of the wind turbine generator (WTG) without considering the operating dynamics of WTG’s shaft and tower, which results in a significant increase in the fatigue load experienced by the shaft torque and tower bending moment. In that, this paper proposes an optimal ancillary control (OAC) method to mitigate the above-mentioned fatigue load. The establishment of the OAC method includes the improvement of fatigue load sensitivity calculation accuracy and the formulation of optimization objective and constraints. Most importantly, the dual-mass drive train and wind speed fluctuations are considered by the fatigue load sensitivity. Moreover, the minimization of the fluctuation of main shaft torque and tower fore-aft bending moment is employed as the optimal objective. Finally, the dynamic adjustment of the power reference is reflected through the constraints. As a result, the OAC method can significantly reduce the WTG fatigue load and thus improve the system frequency stability. Case studies are conducted under various working conditions. By analyzing the damage equivalent load, rainflow cycles and frequency response, the effectiveness of the proposed method is verified.

The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake

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.

Short-crestedness effect on the dynamic response of offshore floating wind turbines

ABSTRACT In practice, the ocean waves are short-crested and three-dimensional. The primary objective of the present research is to form a better understanding of the dynamics of offshore floating wind turbines in multi-directional waves. The short-crested waves are modelled using a spreading exponent function. The wave directionality effect on the dynamic performance of a semisubmersible offshore floating wind turbine is studied. In short-crested waves, the longitudinal response is reduced whereas the lateral response is amplified. The tower base fore-aft bending moment and the mooring line tension force are reduced. In the meanwhile, the fatigue damages of critical points at the tower base are also reduced. It is concluded that short-crestedness is beneficial to the structural integrity. The present research reveals the importance of wave short-crestedness effect on the performance of offshore floating wind turbines.

Analysis of turbulence models fitted to site, and their impact on the response of a bottom-fixed wind turbine

This study compares a wind field recommended by the wind turbine design standards to more realistic wind fields based on measurements. The widely used Mann spectral tensor model with inputs recommended by the standard, is compared to FitMann, the Mann model with inputs fitted to measurements and TIMESR; using measured time series combined with the Davenport coherence model. The Mann model produces too low energy levels at the lowest frequencies of the wind spectra, while the wind spectra generated by FitMann approaches the measured values. TIMESR reproduces the measured spectral values at all frequencies. The different models give similar vertical coherence, while the Mann and FitMann models give lower horizontal coherence than TIMESR. Investigating the wind loads on a bottom-fixed 10-MW wind turbine, the spectra for the tower bottom fore-aft and blade root flapwise bending moment follow the shape of the wind spectra closely at low frequencies. The low-frequency range is important for the blade root and in particular the tower bottom bending moment. Thus, the TIMESR model, followed by FitMann, is assumed to give the most accurate fatigue estimates. For the specific situation analysed in this study, the FitMann model gives only 18 and 5 % lower estimates than TIMESR of the tower bottom and blade root damage equivalent bending moments, while the Mann model gives 27 % and 12 % lower estimates. The tower top yaw and fore-aft bending moments depend on the wind coherence. For the specific situation analysed in this study, the FitMann model gives 9 and 5 % higher estimates of the tower top yaw and tower top damage equivalent (bending) moments compared to TIMESR, while the Mann model gives 23 % and 18 % higher estimates. Since only measurements of the vertical coherence are available, it is not clear which model is superior for the tower top moments. However, the importance of a proper coherence model is documented.

A Comparison of the Turbine Tower Damping Effects of a Series of Back Twisted Active Pitch-to-Stall Blades for a Spar and a Semi-Submersible FOWT

Abstract Floating offshore wind turbines are subjected to higher tower fatigue loads than their fixed-to-seabed counterparts, which could lead to reductions in turbine life. The worst increases are generally seen in the tower axial fatigue, associated with the tower fore-aft bending moment. For a spar type platform, this has been shown to increase by up to 2.5 times and for a semi-submersible platform, by up to 1.8 times. Reducing these loads would be beneficial, as the alternative of strengthening the towers leads to increase in cost. Here, two offshore floating wind turbine systems, of the spar type, are analyzed and selected responses and tower fatigue are compared: one incorporates a variable-speed, variable-pitch-to-stall blade control system and a back twisted blade, and the other a conventional pitch-to-feather control. The results are then compared with those obtained in an earlier study, where the same turbine configurations were coupled to a semi-submersible platform. A weighted wind frequency analysis at three mean turbulent wind speeds of 8, 13, and 18 m/s highlights that the impact of the back twist angle magnitude and initiation point on tower axial fatigue life extension was the same for both platform types. Compared with their respective feather base models, an increase in the tower axial fatigue life of 18.8% was seen with a spar platform and 10.2% with a semi-submersible platform, when a back twist angle to the tip of −6 deg was imposed along with the variable-speed, variable-pitch-to-stall control.

Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression, or principal component analysis. For this study, recordings of data were gathered during approximately 11 months, preprocessed, and filtered by different operational modes, namely standstill, partial load, and full load. The results indicate that all feature selection techniques were able to maintain high accuracy when trained with artificial neural networks. The neighbourhood component analysis yields the lowest number of features required while maintaining the interpretability with an absolute mean squared error of around 0.07 % for full load. Finally, the applicability of the resulting model for predicting loads in the wind turbine is tested by reducing the amount of data used for training by 50 %. This analysis shows that the predictive model can be used for continuous monitoring of loads in the tower of the wind turbine.

Rejecting wake-rotor overlapping load disturbances: An extension to active power control of wind farms

This paper studies an extension to the active power control of wind farms for further structural load alleviation of waked wind turbines. We demonstrate that the structural fatigue loading of a downstream turbine, which its rotor overlaps with wakes of its upwind turbine, can be significantly alleviated, while the wind farm power production follows a power reference signal. A load variations model is proposed, based on the multi-rotor concept, to sense the variations of the wind velocity and the associated loading across the rotor area of a single wind turbine. Then, an individual rotor control is proposed to reject the rotor-wake overlapping load disturbances about an operating condition, commanded with the closed-loop APC at the wind farm level. The applicability and key features of the controller are discussed with a wind farm example consisting of 2×2 turbines. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with an atmospheric boundary layer. The effectiveness of the proposed load disturbance rejection is evaluated using the damage equivalent load of the tower base fore-aft bending and torsional moments of the individual wind turbines.

Is the Blade Element Momentum theory overestimating wind turbine loads? – An aeroelastic comparison between OpenFAST's AeroDyn and QBlade's Lifting-Line Free Vortex Wake method

Abstract. Load calculations play a key role in determining the design loads of different wind turbine components. To obtain the aerodynamic loads for these calculations, the industry relies heavily on the Blade Element Momentum (BEM) theory. BEM methods use several engineering correction models to capture the aerodynamic phenomena present in Design Load Cases (DLCs) with turbulent wind. Because of this, BEM methods can overestimate aerodynamic loads under challenging conditions when compared to higher-order aerodynamic methods – such as the Lifting-Line Free Vortex Wake (LLFVW) method – leading to unnecessarily high design loads and component costs. In this paper, we give a quantitative answer to the question of load overestimation of a particular BEM implementation by comparing the results of aeroelastic load calculations done with the BEM-based OpenFAST code and the QBlade code, which uses a particular implementation of the LLFVW method. We compare extreme and fatigue load predictions from both codes using sixty-six 10 min load simulations of the Danish Technical University (DTU) 10 MW Reference Wind Turbine according to the IEC 61400-1 power production DLC group. Results from both codes show differences in fatigue and extreme load estimations for the considered sensors of the turbine. LLFVW simulations predict 9 % lower lifetime damage equivalent loads (DELs) for the out-of-plane blade root and the tower base fore–aft bending moments compared to BEM simulations. The results also show that lifetime DELs for the yaw-bearing tilt and yaw moments are 3 % and 4 % lower when calculated with the LLFVW code. An ultimate state analysis shows that extreme loads of the blade root out-of-plane bending moment predicted by the LLFVW simulations are 3 % lower than the moments predicted by BEM simulations. For the maximum tower base fore–aft bending moment, the LLFVW simulations predict an increase of 2 %. Further analysis reveals that there are two main contributors to these load differences. The first is the different way both codes treat the effect of the nonuniform wind field on the local blade aerodynamics. The second is the higher average aerodynamic torque in the LLFVW simulations. It influences the transition between operating modes of the controller and changes the aeroelastic behavior of the turbine, thus affecting the loads.