In this paper, inflow information is extracted from a measurement database and used for aeroelastic simulations to investigate if using more accurate inflow descriptions improves the accuracy of the simulated wind-turbine fatigue loads.

The inflow information is extracted from nearby meteorological masts (met masts) and a blade-mounted five-hole pitot tube. The met masts provide measurements of the inflow at fixed positions some distance away from the turbine, whereas the pitot tube measures the inflow while rotating with the rotor.

The met mast measures the free-inflow velocity; however the measured turbulence may evolve on its way to the turbine, pass beside the turbine or the mast may be in the wake of the turbine. The inflow measured by the pitot tube, in comparison, is very representative of the wind that acts on the turbine, as it is measured close to the blades and also includes variations within the rotor plane. Nevertheless, this inflow is affected by the presence of the turbine; therefore, an aerodynamic model is used to estimate the free-inflow velocities that would have occurred at the same time and position without the presence of the turbine.

The inflow information used for the simulations includes the mean wind speed field and trend, the turbulence intensity, the wind-speed shear profile, atmospheric stability-dependent turbulence parameters, and the azimuthal variations within the rotor plane. In addition, instantaneously measured wind speeds are used to constrain the turbulence.

It is concluded that the period-specific turbulence intensity must be used in the aeroelastic simulations to make the range of the simulated fatigue loads representative for the range of the measured fatigue loads. Furthermore, it is found that the one-to-one correspondence between the measured and simulated fatigue loads is improved considerably by using inflow characteristics extracted from the pitot tube instead of using the met-mast-based sensors as input for the simulations. Finally, the use of pitot-tube-recorded wind speeds to constrain the inflow turbulence is found to significantly decrease the variation of the simulated loads due to different turbulence realizations (seeds), whereby the need for multiple simulations is reduced.

Aeroelastic simulations are extensively used in the development of modern wind turbines. These simulations are used to estimate the dynamic response of the wind-turbine structure in both the research, the design and the certification phase. They are specifically used to investigate new concepts, evaluate various designs, and eventually to prove that the code-defined lifetime fatigue loads and extreme loads are below the capability limits of the wind-turbine subcomponents.

Two approaches for comparing the measured and simulated loads.

To validate aeroelastic codes, simulation results are usually benchmarked against results from other aeroelastic codes, compared to measurements of scaled wind-turbine models under laboratory conditions, or compared to measurements on full-scale turbines. In this paper, we focus on the ultimate approach, where simulation results are compared to full-scale measurements.

Aeroelastic simulations are typically based on idealized simplified models of
the wind-turbine structure (e.g. often modelled as beam-type structures),
its aerodynamic properties (e.g. often based on the blade element momentum
aerodynamic approach,

The paper is structured as follows. Initially, determination of inflow characteristics, matching a particular full-scale event, are discussed in some detail. Next, the experimental section is described which encompasses both the experimental set-up and the measured results for selected case studies. Then a description of the analogue numerical simulations follows, and these simulations are subsequently compared with the selected full-scale recordings. Finally, conclusions are drawn.

The inflow conditions obviously have a significant impact on the turbine load
response

The inflow conditions are typically decomposed into an average stationary
part and a turbulent fluctuating part. In many cases, the code-defined or the
site-averaged shear profiles and turbulence parameters are used in the inflow
modelling. This approach makes it possible to compare simulation results with
the average load level resulting from the full-scale measurements (see
Sect.

This paper is about the effects of using more precise and dedicated inflow
characteristics for aeroelastic simulations when dealing with validation of
aeroelastic codes. The idea is to extract detailed information about the
inflow from a selection of 10 min measurement periods with the aim of
defining accurate inflow fields characteristics of each of the periods, i.e.
descriptions of the mean inflow velocities, and the turbulent
fluctuations. These inflow characteristics are subsequently used as input for
numerical load simulations, and the simulated loads are then compared with
the measured loads in a one-to-one comparison (see Fig.

The blade-root flap-wise fatigue load plotted as a function of wind speed (Wsp) and coloured by turbulence intensity. The turbulence intensity affects the blade-root fatigue loads. However, much of the scatter is caused by other factors.

Case overview showing the origin of the mean wind speed (wsp), wind
speed trend, turbulence intensity (Tint), shear profile and Mann parameter
(

As seen in Fig.

The inflow characteristics required for the description of more accurate inflow fields can be extracted from cup or sonic anemometers at a nearby meteorological mast (met mast) if the anemometers are exposed to similar inflow conditions. This means that the mast must be close to the turbine, but outside of the rotor induction zone. Furthermore, wind directions during which the anemometers are in the wake of turbines or the mast itself must be discarded, as well as situations in which the turbine is in the wake of other turbines. In addition, anemometers are required at different heights to measure the mean wind shear profile. Wind veer (i.e. turning of the mean wind direction with height) is not considered in this study.

Alternatively, the inflow parameters can be obtained from a blade-mounted flow sensor (BMFS). Mounted at the blade, a BMFS is exposed to exactly the same inflow conditions as the turbine, and this is true regardless of the wind direction. In addition, a BMFS also provides valuable information about the flow variations within the rotor area.

However, a BMFS is located inside the rotor induction zone; therefore, a
method to compensate for the presence of the turbine in the flow recordings is
required, i.e. a method that takes the flow velocities measured with the BMFS
and calculates the free-stream inflow velocities that would have been
observed at the same time and location without the presence of the wind
turbine. In such studies, the method presented by

From the measurement database (see Sect.

For each case and period, the inflow characteristics are used as input for a set of six aeroelastic simulations with different turbulence realizations (seeds).

The measurement database used in this study was recorded from April to July 2009
as part of the DAN-AERO project

Overview of the Høvsøre test site for large wind turbines in Denmark. The Siemens turbine is located in the middle of a row of five megawatt turbines.

As seen in Fig.

Five-hole pitot tubes have been used in several research experiments to
measure the local inflow relative to the blades

During the current measurement period, an Aeroprobe CPSPY5 five-hole pitot
tube was mounted on one of the blades at a radius of 36 m, i.e. around one-third from the tip.
A five-hole pitot tube measures the relative flow speed
as well as the flow angle in two perpendicular planes. The pitot tube was
calibrated by Aeroprobe, and the uncertainty of the measured relative flow
speed and angles was determined to be less than 0.2 % and 0.2

From the relative flow speed and two perpendicular angles, the relative 3-D
flow velocity vector can be calculated

In this study, the velocity due to sensor movement is calculated based on the rotor rotation and the pitch motion. This means that movement due to dynamic tower and blade deflection is not included, and some discrepancy is consequently expected. In Pedersen et al. (2018), the error introduced by not taking the tower and blade deflection into account is investigated using HAWC2 simulations, and the root-mean-squared error of the instant axial wind speed is found to be around 2 %.

The flow velocity is mapped from the rotating blade section coordinate system to fixed global cartesian coordinates. During this process, additional uncertainty is introduced, as the exact orientation of the blade section is unknown due to the deflection and torsion of the structure.

Finally, the wind-turbine induction, i.e. the disturbance of the inflow field caused by the presence of the rotor, is estimated using a combination of aerodynamic models. In this study, the aerodynamic models comprise blade element momentum (BEM) based models for axial and tangential induction, a radial induction model and tip loss correction, as well as models for skew and dynamic inflow.

Subtracting the estimated induction from the measured flow velocity results
in an estimate of the free-stream inflow velocity, which would have been
observed at the same time and location without the presence of the turbine.
In this step, uncertainty is also introduced due to the mismatch between the
applied simple engineering models and the complex real world. The process and
the introduced uncertainties are described in detail by

The blade-root load sensors comprise flap-wise and edge-wise bending-moment
sensors (see Fig.

Orientation of the blade-root flap-wise,

Some of the sensors are found to drift considerably with temperature. Therefore, a linear temperature correction is applied before performing the calibration.

The edge-wise bending-moment sensors are calibrated using a set of time
series measured at low wind speed and with pitch angles around 0

Similarly, the flap-wise bending-moment sensors can be calibrated using time
series measured at a low wind speed and a 90

The mean flap-wise bending moments of the three blades are not equal after
this calibration. This is, however, justified as the measured pitch angles of
blades 2 and 3 are offset by around

The current measurement database contains no tower-load sensors. The dynamic tower loads are, however, mainly induced by the aerodynamic blade loads; therefore, it is possible to derive tower-load estimations from the blade-root load sensors.

The tower-bottom fore–aft bending moment is dominated by the constant weight
of the rotor and the dynamic thrust on the rotor. The thrust is related to
the rotor-plane projection of the blade-root bending moments (i.e. mainly the
flap-wise bending moments), and using a linear calibration a good
approximation can be achieved for a given wind speed:

Similarly, approximations of the tower-top tilt and yaw moments can be
formulated as

The derived tower-load sensors have been calibrated based on HAWC2
simulations. Applied to other HAWC2 simulations with comparable wind
conditions, the tower loads derived from the blade-root sensors fit quite
well with the actual simulated tower loads (see 8 m s

The calibration constants are, however, dependent on the mean wind speed.
Hence, the fine agreement seen in Fig.

Comparison of the HAWC2 simulated tower loads and the estimated
tower loads, which are derived from the HAWC2 simulated blade-root load
sensors and calibrated for 8 m s

The calibration constants are consequently determined for wind speeds ranging
from 4 to 15 m s

Relative fatigue load error of the derived tower-load sensors compared to the HAWC2 simulated tower loads. The derived tower-load sensors are obtained from the HAWC2 simulated blade-root load sensors and calibrated using wind-speed-dependent calibration constants.

At low wind speeds, the tower-bottom bending moment is dominated by
structural loads, whereas the impact of the aerodynamic blade loads is limited.
Hence, the derived tower-bottom sensor deviates considerably from the
simulated tower-bottom signal, and the fatigue load error is relatively high
(see Fig.

To facilitate comparisons of the predicted loads with their measured counterparts, aeroelastic simulations were performed.

The simulations used in this study are performed using HAWC2 – a non-linear
finite-element-based aeroelastic code intended for computing the wind-turbine
response in the time domain

The turbine model used for the simulations is based on the structural and
aerodynamic data of the Siemens 3.6 MW turbine, which was tested at
Høvsøre in 2009 during the DAN-AERO project (see
Sect.

To match the pitch-angle offsets observed in the measurements (see
Sect.

Within the HAWC2 framework, the turbine is controlled by the Basic
DTU controller

In this section, the inflow characteristics used for the different cases are
described (see an overview of the five cases in Table

Inflow characteristics of P1–P20 showing the wind speed (Wsp), wind speed trend (Trend), turbulence intensity (Turb. int.) and power shear exponent (Power shear exp).

In cases 1–3, the 10 min mean wind speed measured at Mast3 is used. Mast3
is located around 2.5 rotor diameters west of the turbine (see Fig.

In cases 4 and 5, the mean wind speed is extracted from the estimated
free-stream pitot-tube wind speed. To avoid the problem regarding the
influence of non-linear shear on the mean wind speed, only observations
recorded in the 85–95 m altitude regime are included (i.e. the hub height

In some of the selected periods, the mean wind speed changes considerably during the period. Therefore, a linear wind-speed trend is assumed and calculated for all periods and is included in the simulations in all cases except for Case 1.

Wind-speed trends may result in increased loads, e.g. tower-bottom fatigue loads, as the trend will contribute with one (large) fatigue cycle. Furthermore, the target turbulence intensity will be too high if calculated from the standard deviation of the raw wind-speed signal. Note, however, that periods with wind-speed trends may be problematic, as it means that the turbulence conditions are not stationary, and the theory behind the applied turbulence model assumes stationary conditions.

The mean wind shear profile has a high impact on the flap loads as well as on the tower-top tilt and yaw loads. The 10 min mean wind speed is not known in all parts of the rotor, and, therefore, a shear model is necessary. In this study, the power-law type of shear profile is used, and it is fitted to 1 h of measurements. As the wind may change during 1 h, we would like to base the shear profile on the selected 10 min observations. However, the 10 min mean vertical profile can have almost any shape, and a longer time period is therefore usually required to make a proper power-profile fit.

In Case 1, the site-average wind-speed-dependent shear profile is used, whereas the mean wind speeds at different heights, measured at the main met mast 850 m away, are used to estimate the vertical shear profile for cases 2 and 3. Note, that the main met mast has sensors up to 116.5 m, and the upper part of the rotor is therefore not represented.

It is possible to use the 10 min mean shear profile measured by the pitot tube directly, but outside of its altitude range a shear profile model is required. Therefore, the power-law shear profile is fitted to 1 h of the estimated free-stream pitot-tube wind speed and is used for cases 4 and 5.

Ideally the 10 min mean wind speed is known for the whole rotor. This is
obviously not the case, but from the pitot-tube measurements, the
10 min mean wind speed at the path of the pitot tube can be extracted and
used to specify the mean wind speed in a grid covering the rotor (see
Fig.

The aerodynamic models that are used to estimate the free-stream pitot-tube
wind speed do not include a model of the tower shadow. The wind-speed drop
due to tower shadow should not, however, be included in the inflow input to
the simulations. Therefore, the mean wind speed is linearly interpolated in a
30

The turbulence used in the simulations is generated using the Mann turbulence
model

For cases 1, 4 and 5, standard values are used for

The Mann turbulence model assumes neutral atmospheric stability conditions.
The parameters can, however, be fitted to spectra representing non-neutral
stability classes where slightly different parameters are obtained. The
stability-dependent parameters used for cases 2 and 3 (see
Table

Standard and stability-dependent turbulence parameters.

The 1 Hz equivalent loads coloured by the magnitude of the turbulence intensity measured at Mast3.

Standard or long-term-average values may be appropriate for

In Case 1, the turbulence is scaled after generation, such that the turbulence intensity in the centre of the turbulence field matches the turbulence intensity measured by Mast3 within the selected period. This approach is convenient as it ensures agreement between the measured and simulated hub-height turbulence intensity. It may, however, result in energy from scales that are not represented in the turbulence model being distributed on other frequencies. Furthermore, the approach is inappropriate if the centre of the turbulence field is not representative for the whole field.

In cases 2–5, the

The 1 Hz equivalent loads coloured by the magnitude of the power shear coefficient extracted from the main met mast.

The 1 Hz equivalent loads coloured by a atmospheric stability classification metric (i.e. Monin–Obukhov length) extracted from the main met mast.

Due to the low fixed-position resolution of the pitot-tube wind speed, only
the low frequency part of the

Mean relative error of the simulated equivalent loads.

In cases 3 and 5, the measured wind speeds are used as the input to a
constraint turbulence simulator that modifies existing turbulence fields,
e.g. stochastic realizations of the Mann turbulence model, to reproduce the
specified wind speeds at the corresponding positions while preserving the
statistics. The applied constraint turbulence simulation approach is
described by

Figures

Error distribution of the simulated equivalent loads.

An overview of the mean relative error of the different cases can be found in
Fig.

A schematic overview showing how to interpret
Figs.

In Case 1, only the wind speeds are different between the periods. Therefore,
the load levels within the two wind-speed groups are very similar, as seen in
Fig.

Case 1. Site average turbulence intensity and shear (wind-speed
trend neglected). For interpretation see Fig.

In Case 2, information about the wind-speed trend, the measured turbulence level and the shear profile is included in the simulations.

Including the wind-speed trend increases the loads considerably in some
periods. In P7, for example, the mean wind speed decreases 2.9 m s

In the selected periods, the turbulence intensity varies from 3.1 to
9.2 %. Including this information makes the range of the simulated loads
reflect the range of the measured loads. The turbulence scaling approach,
which is used for Case 1, is found to introduce substantial variation due to
different turbulence realizations (seeds). This variation is considerably
reduced in this and the succeeding cases by fitting the

The terrain is rather flat towards the west; therefore, the power shear exponents
are modest (0.06 to 0.21) and are generally similar to the
site-average values (0.09 for 8 m s

Furthermore, the stability dependent

Figure

Case 2. Best case based on met-mast inflow information. For
interpretation see Fig.

In Case 3, constraint turbulence simulation has been applied to constrain the
turbulence to match the Mast3 wind-speed recordings at the position of Mast3,
i.e. 250 m upstream. It has an effect on most of the simulated loads, but it
slightly increases the mean error of all load sensors (see
Fig.

The biggest error increase is seen for P5, which has a distinct drop in the
wind speed measured by Mast3 in the middle of the period. In the simulations,
a similar drop, introduced by the constraint turbulence simulator, is
unaffectedly advected with the steady mean wind to the turbine in agreement
with Taylor's frozen turbulence hypothesis

Case 4 uses inflow characteristics extracted from the estimated free-stream
pitot-tube wind speed. As seen in Table

These mismatches are caused by the spatial distance between the locations of
measurements, fundamental differences in the sensor technology and
measurement method, and the uncertainties introduced in the conversion from
pitot-tube measurement to free-stream wind speed in the fixed global
coordinates (see Sect.

Compared with Case 2 (the most equivalent met-mast case), all mean errors
decrease by 5 % or more except the mean error of the tilt moment at
8 m s

Case 4. Best case based on pitot-tube inflow information. For
interpretation see Fig.

In Case 5, the measured mean-wind-speed variations over the rotor area are modelled; furthermore, the instant measured pitot-tube wind speed is used to constrain the turbulence model.

Modelling the measured mean-wind-speed variations via the grid-based approach
(exemplified in Fig.

In this case, the turbulence field is generated using standard Mann
turbulence

In the selected periods, the use of constraint turbulence simulation reduces
the mean error for all load sensors. Furthermore, the range of the simulated
loads due to different turbulence realizations decreases considerably, such
that the need for multiple simulations with different seeds is reduced (see
Fig.

Case 5. Best case based on pitot-tube inflow information. For
interpretation see Fig.

In Case 5, the range of the simulated loads reflects the range of the measured loads. Therefore, they are assumed to be much more suitable for load extrapolation than the loads of Case 1.

The derived tower loads are slightly underestimated at 8 m s

Only a few of the lines that connect the measured and simulated flap and
tower-bottom observations intersect, meaning that the inflow conditions that
result in high-load levels in the measurements also result in high-load
levels in the simulations and vice versa. The same tendency is seen for the
tilt and yaw moment at 14 m s

At the beginning of this section, it was concluded that an advanced approach
that considers combinations of inflow parameters would be required to predict
the loads of specific periods. Aeroelastic simulations can be considered to
be such an approach, and to compare these simulations to the single parameter approach in
Figs.

Equivalent measured loads, coloured by the corresponding simulation result. The simulations are performed using inflow information from the met masts similar to Case 2 (but only one seed per period). If the simulated load equals the maximum measured load at the current wind speed, then the observation is red, whereas observations where the simulated load equals the minimum load measured at the current wind speed are blue.

Equivalent measured loads, coloured by the corresponding simulation result. The simulations are performed using inflow information from the pitot tube, similar to Case 5 (but only one seed per period). If the simulated load equals the maximum measured load at the current wind speed, then the observation is red, whereas observations where the simulated load equals the minimum load measured at the current wind speed are blue.

The most promising result is seen in the flap and tower-bottom loads coloured
according to the pitot-tube-based simulations (top row of
Fig.

The tilt and yaw moment scatter, in comparison, cannot be explained using
these approaches. In both cases, most high-load observations are
underestimated from 4 to 8 m s

In this paper, different inflow information is extracted from a measurement database and used for aeroelastic simulations to investigate if using more detailed inflow descriptions improves the accuracy of the simulated loads.

The inflow information is extracted from nearby met masts and from a blade-mounted five-hole pitot tube. The pitot tube is located inside the induction zone, i.e. the measured flow velocity is influenced by the presence of the turbine. Therefore, an aerodynamic model is used to estimate the free-stream inflow velocity that would have been observed at the position of the pitot tube without the presence of the turbine.

In the case study, 20 periods, which represent a wide range of loads at mean
wind speeds of 8 and 14 m s

The case study revealed that the loads in simulations based on site-average turbulence intensity and shear profile (the typical load validation approach) did not reflect the measured loads, and most of the simulated load ranges were considerably smaller than the ranges of measured loads. Therefore, load extrapolation based on this approach may be misleading.

Including the met-mast measured turbulence intensity increases the variation of the simulated loads and makes the simulated load range reflect the measured range. However, the one-to-one correspondences were poor, with deviations up to 67 %.

The turbulence scaling approach, where the turbulence is scaled such that the
turbulence intensity in the centre of the field matches the target intensity,
was found to introduce a considerable variation in the simulated loads.
Therefore, the scaling of the turbulence such that the integral of the target

In most periods, the inflow characteristics extracted from the pitot tube deviate from the inflow characteristics extracted from the met masts. These mismatches are caused by the spatial distance between the locations of met masts and the pitot tube, fundamental differences in the sensor technology and measurement method, and uncertainties introduced in the conversion from pitot-tube measurement to estimated free-stream inflow wind speed in fixed global coordinates.

Using the wind speed, turbulence intensity and shear measured by the blade-mounted pitot tube reduces the errors of the flap and tower-bottom loads in this study, whereas the errors of the tilt and yaw moments are similar. This indicates that it is beneficial to measure the inflow with a BMFS even though errors are introduced due to the dynamic and static deflection and torsion of the blade, as well as in the aerodynamic model that corrects for the turbine induction.

Including the measured wind-speed trend, shear profile, rotor-position-dependent variations in the mean wind and stability-dependent turbulence parameters were all found to change the loads significantly in some simulations, while the mean errors were only slightly affected. This information may, however, be important to include in other situations, e.g. half-wake situations and periods with high shear.

Constraint turbulence simulation was used to constrain the turbulence to match the instantaneously measured wind speeds at observation points. Constraining the turbulence to the wind speed measured by the met mast (250 m upstream) increased the errors of the simulated loads. In the simulations, a turbulence event introduced by the constraint turbulence simulator at the met-mast position is transported unaffected with the steady mean wind to the turbine, in agreement with Taylor's frozen turbulence hypothesis. In the real world, however, the turbulence structures change over time, and an upstream turbulence event may even pass beside the turbine. The event that hits the turbine in the simulation is thereby different from the event that hits the real turbine. Thus, it is not recommended to use constraint turbulence simulation based on wind speeds measured at a distance from the wind turbine.

Based on pitot-tube wind speed, however, constraint turbulence simulation
reduces the mean error of all load sensors in this study. The final case is
based on pitot-tube-derived mean wind speed, turbulence intensity and shear,
and constraint turbulence simulation based on the pitot-tube-recorded wind
speeds. In this case, the range of the simulated loads reflects the range of
the measured loads. Therefore, it is more suitable for load extrapolation.
Moreover, the sequences of the simulated and measured flap and tower-bottom
loads are quite similar, meaning that the inflow conditions that result in
high-load levels in the measurements in most cases also result in high-load
levels in the simulations and vice versa. The same tendency is seen for the
tilt and yaw moment at 14 m s

It was investigated if the enormous scatter that is seen, especially in the flap and tower-bottom loads, can be predicted by the turbulence intensity, shear profile or atmospheric stability conditions alone. The turbulence intensity explains some of the scatter, and the lowest loads are seen in stable conditions with low turbulence intensity and high shear. It is, however, concluded that a more sophisticated approach, which considers the actual combination of inflow parameters, is required to predict the loads of specific periods.

Aeroelastic simulations can be considered to be such an approach. Therefore, simulations representing all suitable periods have been performed based on inflow information from the met masts (wind speed, wind-speed trend, turbulence intensity and shear) and the pitot-tube recordings (wind speed, wind-speed trend, turbulence intensity, rotor-position-dependent shear and the instantaneously measured wind speed for constraint turbulence simulation). Based on these simulations, it is concluded that HAWC2 simulations based on inflow information from the pitot tube are able to predict the measured flap and tower-bottom load scatter very well in most periods. The met-mast-based simulations yield high loads for most periods in the upper half of the load scatter and vice versa, but the result is less impressive.

In both cases, the simulations cannot explain the tilt and yaw moment scatter, as most high-load observations are underestimated at some wind-speed ranges, and low-load observations are overestimated at other wind-speed ranges.

Simulation results are not available due to confidentiality issues.

MMP post-processed the measurement data and setup the simulation and comparison framework. All authors have interpreted the obtained data. MMP prepared the paper with revisions of all co-authors.

The authors declare that they have no conflict of interest.

The authors would like to acknowledge Siemens Wind Power for providing the data for the simulation model. The authors would also like to recognize funding from the Danish Energy Agency EUDP programme DAN-AERO MW projects, ENS contract nos. 33033-0074 and 64009-0258, for providing important data for the present study.

This paper was edited by Gerard J. W. van Bussel and reviewed by three anonymous referees.