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Contract No. DE-AC36-08GO28308

Conference Paper 

NREL/CP-5000-73191 

May 2019 

Validation of Numerical Models of the 

Offshore Wind Turbine from the Alpha 

Ventus Wind Farm Against Full-Scale 

Measurements Within OC5 Phase III

Preprint

Wojciech Popko, Amy Robertson, Jason Jonkman, Fabian Wendt, 

Philipp Thomas, Kolja Müller, Matthias Kretschmer, 

Torbjørn Ruud Hagen, Christos Galinos, Jean-Baptiste Le Dreff, 

Philippe Gilbert, Bertrand Auriac, Sho Oh, Jacob Qvist, 

Stian Høegh Sørum, Loup Suja-Thauvin, Hyunkyoung Shin, 

Climent Molins, Pau Trubat, Paul Bonnet, Roger Bergua, 

Kai Wang, Pengcheng Fu, Jifeng Cai, Zhisong Cai, 

Armando Alexandre, and Robert Harries

Presented at the ASME 2019 38th Annual Conference on 

Ocean, Offshore, and Arctic Engineering

Glasgow, Scotland 

June 9–14, 2019 
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Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5000-73191 

May 2019 

National Renewable Energy Laboratory

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Validation of Numerical Models of the 

Offshore Wind Turbine from the Alpha 

Ventus Wind Farm Against Full-Scale 

Measurements Within OC5 Phase III

Preprint

Wojciech Popko, Amy Robertson, Jason Jonkman, Fabian Wendt, 

Philipp Thomas, Kolja Müller, Matthias Kretschmer, 

Torbjørn Ruud Hagen, Christos Galinos, Jean-Baptiste Le Dreff, 

Philippe Gilbert, Bertrand Auriac, Sho Oh, Jacob Qvist, 

Stian Høegh Sørum, Loup Suja-Thauvin, Hyunkyoung Shin, 

Climent Molins, Pau Trubat, Paul Bonnet, Roger Bergua, 

Kai Wang, Pengcheng Fu, Jifeng Cai, Zhisong Cai, 

Armando Alexandre, and Robert Harries

Suggested Citation 

Popko, Wojciech, Amy Robertson, et al. 2019. Validation of Numerical Models of the 

Offshore Wind Turbine from the Alpha Ventus Wind Farm Against Full-Scale 

Measurements Within OC5 Phase III: Preprint. Golden, CO: National Renewable Energy 

Laboratory. NREL/CP-5000-73191. https://www.nrel.gov/docs/fy19osti/73191.pdf. 
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VALIDATION OF NUMERICAL MODELS OF THE OFFSHORE WIND TURBINE FROM

THE ALPHA VENTUS WIND FARM AGAINST FULL-SCALE MEASUREMENTS

WITHIN OC5 PHASE III

Wojciech Popko,

1 Amy Robertson,2 Jason Jonkman,2 Fabian Wendt,2 Philipp Thomas,1 Kolja M  ̈uller,3 Matthias Kretschmer,3

Torbjørn Ruud Hagen,

4 Christos Galinos,5 Jean-Baptiste Le Dreff,6 Philippe Gilbert,7 Bertrand Auriac,8 Sho Oh,9

Jacob Qvist,

10 Stian Høegh Sørum,11 Loup Suja-Thauvin,12 Hyunkyoung Shin,13 Climent Molins,14 Pau Trubat,14 Paul Bonnet,15

Roger Bergua,

16 Kai Wang,16 Pengcheng Fu,17 Jifeng Cai,17 Zhisong Cai,17 Armando Alexandre,18 Robert Harries18

1Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems, Division Wind Turbine and System Technology, Bremerhaven, Germany

wojciech.popko@iwes.fraunhofer.de

2National Renewable Energy Laboratory, Golden, CO, USA, 3University of Stuttgart, Stuttgart Wind Energy, Stuttgart, Germany,

4OWEC Tower AS, Oslo, Norway, 5Technical University of Denmark, Department of Wind Energy, Roskilde, Denmark

6Electricite de France, Recherche et D  ́ eveloppement, Palaiseau, France,  ́7

IFP Energies Nouvelles, Solaize, France,

8Principia, La Ciotat, France, 9Nippon Kaiji Kyokai (ClassNK), Tokyo, Japan, 104Subsea, Hvalstad, Norway,

11Norwegian University of Science and Technology, Department of Marine Technology, Trondheim, Norway, 12Simis AS, Trondheim, Norway,

13University of Ulsan, School of Naval Architecture and Ocean Engineering, Ulsan, The Republic of Korea,

14Polytechnic University of Catalonia, Barcelona, Spain, 15Siemens Industry Software, Cornella de Llobregat, Spain, `

16Envision Energy Limited, Shanghai, China, 17China General Certification Center, Beijing, China, 18DNV GL, Bristol, The United Kingdom

ABSTRACT

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore

wind turbines (OWTs) through comparison of simulated results

to the response data of physical systems. Phase III of the

OC5 project validates OWT models against the measurements

recorded on a Senvion 5M wind turbine supported by the OWEC

Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen

for the validation: (1) Idling below the cut-in wind speed; (2)

Rotor-nacelle assembly (RNA) rotation maneuver below the cutin wind speed; (3) Power production below and above the rated

wind speed; and (4) Shutdown. A number of validation load

cases were defined based on these operating conditions. The

following measurements were used for validation: (1) Strains

and accelerations recorded on the support structure; (2) Pitch,

yaw, and azimuth angles, generator speed, and electrical power

recorded from the RNA. Strains were not directly available from

the majority of the OWT simulation tools. Therefore, strains were

calculated based on out-of-plane bending moments, axial forces,

and cross-sectional properties of the structural members. Also,

a number of issues arose during the validation: (1) The need for

a thorough quality check of sensor measurements; (2) The sensitivity of the turbine loads to the controller and airfoil properties,

which were only approximated in the modeling approach; (3) The

importance of estimating and applying an appropriate damping

value for the structure; and (4) The importance of wind characteristics beyond turbulence on the loads. The simulation results

and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, probability

density functions of strains and accelerometers. A good match

was achieved between the measurements and models set up by

OC5 Phase III participants.

1

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Northeast 

side

Southwest 

side

Southeast 

side

Northwest 

side

Eastern 

leg

Northern

leg

Southern

leg

Western 

leg

FIGURE 1: LEFT: LOCATION OF THE OWT WITHIN THE ALPHA VENTUS WIND FARM—MODIFIED SKETCH FROM [1]; RIGHT:

JACKET ORIENTATION W.R.T. TRUE NORTH—VIEW FROM THE TOP.

INTRODUCTION

The Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project [2], which operates under

the International Energy Agency (IEA) Wind Task 30 is the

follow-up project of OC3 and OC4, which ran from 2005 to

2009 and from 2010 to 2014, respectively. The focus of OC3 and

OC4 was to verify and benchmark simulation tools for offshore

wind turbines (OWTs) with an emphasis on support structures

through code-to-code comparison. This verification work led

to improvements in model accuracy, which is a crucial achievement as the advancement of the offshore wind industry is closely

tied to the development and accuracy of aero-servo-hydro-elastic

OWT models [3, 4]. Participants of OC3 and OC4 expressed

great interest in creating an extension to IEA Task 30 to focus

on validating offshore wind modeling tools against experimental

and in-situ data.

The OC5 project was focused on validation of aero-hydroservo-elastic simulation tools for OWTs through comparison of

simulated results to the response data of physical systems. OC5

was organized in three phases jointly coordinated by the National

Renewable Energy Laboratory (NREL) from the USA and the

Fraunhofer Institute for Wind Energy Systems IWES (IWES)

from Germany. While the first two phases dealt with physical

response data from tank tests [5, 6], Phase III dealt with the fullscale open-ocean system. Phase III of the OC5 project analyzed

the Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The reference

met-ocean and structural measurements were provided by the

Research at Alpha VEntus (RAVE) consortium for model validation. Alpha ventus is located in the North Sea at the site of the

average water depth of 28 m, around 45 km north of the Borkum

island, as shown in Fig. 1.

The validation results discussed in this paper represent the

outcome of several modeling iterations. Within each modeling

iteration, the participants updated their simulation settings to better match the measurements. It should be noted that prior to the

validation, all numerical OWT models were verified to fix modeling errors. Their verification was published in a separate paper

by Popko et al. [7]. The models were not calibrated to the data

after the verification step.

A number of academic and industrial project partners from

11 countries participated in the task. Those actively involved in

Phase III are listed in Tab. 1.

A set of state-of-the-art simulation tools for OWT modeling

is represented in Phase III of the OC5 project. Table 2 summarizes some of their simulation capabilities that are important for

validation of OWT models in Phase III.

DEFINITION OF OFFSHORE WIND TURBINE MODEL

A description of the numerical model of the OWT consisting of the rotor-nacelle assembly (RNA), the tower, the transition

piece (TP), the jacket substructure (OWEC Quattropod), its foundation piles, and soil properties was set up at Fraunhofer IWES

2

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TABLE 1: PARTICIPANTS OF OC5 PHASE III AND THEIR

TOOLS.

Organization full name Abbreviation Country Tool

4Subsea,

Simis AS

4S-Simis Norway ASHES

China General Certification Center CGC China Bladed V4.8

Nippon Kaiji Kyokai ClassNK Japan NK-UTWind

Technical University of Denmark

– Department of Wind Energy

DTU Denmark HAWC2

DNV GL DNVGL UK Bladed V4.8

Electricite de France  ́

– Recherche et Developpement  ́

EDFRD France FAST V8,

DIEGO

Envision Energy Limited Envision China SAMCEF Wind

Turbines 18.0

(SWT)

IFP Energies Nouvelles,

PRINCIPIA

IFPEN-PR France DeepLinesWind

V5R4

Fraunhofer IWES – Division Wind

Turbine and System Technology

IWES Germany MoWiT

National Renewable Energy Laboratory NREL USA OpenFAST v0.1.0

Norwegian University of Science and

Technology

– Department of Marine Technology

NTNU Norway SIMA

OWEC Tower OWEC Norway –

Senvion Senvion Germany –

Siemens Industry Software SIS Spain SAMCEF Wind

Turbines 18.0

(SWT)

University of Stuttgart

– Stuttgart Wind Energy

SWE Germany Simpack

University of Ulsan – School of Naval

Architecture and Ocean Engineering

UOU The Republic

of Korea

FAST V8

Polytechnic University of Catalonia UPC Spain FloaWDyn

by Popko [8] based on the data provided by Senvion and OWEC

Tower. The complexity of the OWT models was proven adequate

for this validation task [7]. On the other hand, the models were

relatively simple (load analysis level) to minimize the implementation effort and modeling errors in the simulation tools.

The verification (code-to-code comparison) and tuning of

those models, prior to their validation, was performed against

a reference OWT model implemented in Flex5-Poseidon by

the University of Stuttgart—Stuttgart Wind Energy (SWE), and

documentation provided by Senvion and OWEC Tower. The

reference OWT model from SWE contains structural and aerodynamic properties of the real blades, and the fully functional

controller (torque, pitch, yaw, etc.) that could not be disclosed to

the OC5 Phase III participants. The SWE reference model was

extensively validated by Kaufer [9] and Muller [10] within the  ̈

RAVE projects—Offshore-Windenergieanlagen (OWEA), and

OWEA Loads [11], respectively. Therefore, it was also considered as a reference model for the verification of other numerical

models prior to their validation in Phase III. The verification results of the models, which are used in the validation exercise

presented in this paper, were published in a separate paper by

Popko et al. [7].

ALPHA VENTUS MEASUREMENTS FOR VALIDATION

The reference measurements at different operating conditions were selected by SWE considering the following constraints: (1) The met-ocean conditions and the structural measurements had to be available for the same time window; (2)

The data from the majority of the strain gauges and accelerometers, which are located at the critical positions for capturing

the global system response, should be available; (3) The wind

turbine should experience free-flow conditions—no wake effect

from other wind turbines in the wind farm; and (4) The measurements should be 10 minutes long and leave sufficient time in

between the adjacent measurements.

The measurement time period between April 2011 and January 2012 was used for screening, as during that time the

majority of sensors operated correctly. The above baseline filtration constraints were applied to identify data regions that met

the following conditions:

• Idling below the cut-in wind speed.

• Idling below the cut-in wind speed and the RNA rotation

maneuver.

• Power production below the rated wind speed.

• Power production above the rated wind speed.

• Power production above the rated wind speed, followed by

the normal shut-down.

The following 10-minute structural measurements, sampled

with 50 Hz, were available for the validation purposes:

• Blade pitch angle and azimuth position, expressed in deg.

• Yaw position w.r.t. true north, expressed in deg.

• Generator speed, expressed in rpm.

• Electrical power, expressed in kW.

• Accelerations at the tower top and bottom, expressed in

m/s

2

.

• Bending moments at the tower bottom, expressed in kNm.

• Uncalibrated strains at different positions along the jacket

substructure, expressed in μm/m.

The location of the strain gauges along the jacket substructure is shown in Fig. 2. There are four sensors for each

measurement location. They are positioned around the circumferences of the legs and braces. Each sensor is effectively one

strain gauge composed of four resistances creating a full-bridge

configuration. The full-bridge configuration helps to increase the

output signal and optimizes compensation of temperature and

mechanical noise impacts. For validation purposes a single strain

gauge is selected from a given location.

The following met-ocean conditions were available in terms

of their statistics (mean, minimum, maximum, and standard deviation values):

• Wind speed at the hub height, expressed in m/s.

• Wind direction w.r.t. true north, expressed in deg.

3

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TABLE 2: OVERVIEW OF SIMULATION CAPABILITIES OF TOOLS USED WITHIN OC5 PHASE III.

Tool Structural Aerodynamics Hydrodynamics Control

ASHES Structural dynamics: FEM

Beam model: Euler-Bernoulli

Damping model: Stiffness proportional Rayleigh

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

+ skew inflow correction

Dynamic wake model: Øye

Dynamic stall model: Øye

Wind field grid format: Rectangular

Regular wave model:

Airystr

Irregular wave model:

JONSWAP/PM

Hydro model: ME

DLL,

UD

Bladed V4.8 Structural dynamics: MBS + flexible modally

reduced bodies

Beam model: Timoshenko

Damping model: Modal

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

+ skew inflow correction

Dynamic wake model: Øye or Pitt and Peters

Dynamic stall model: Beddoes-Leishman or Øye

Wind field grid format: Rectangular

Regular wave model:

Airystr, Stream

Irregular wave model:

JONSWAP/PM

Hydro model: ME + MF

DLL,

UD

DeepLinesWind

V5R4 (DeepLW)

Structural dynamics: FEM

Beam model: Mindlin-Reissner

Damping model: Stiffness proportional Rayleigh

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

+ skew inflow correction + relaxation of induction factors

Dynamic wake model: Øye

Dynamic stall model: Øye or Risø

Wind field grid format: Rectangular

Regular wave model:

Airystr, Stream

Irregular wave model:

JONSWAP/PM

Hydro model: ME

DLL,

UD

DIEGO Structural dynamics: FEM

Beam model: Euler-Bernoulli

Damping model: Stiffness and mass proportional

Rayleigh

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

Dynamic wake model: Pitt and Peters

Dynamic stall model: Beddoes-Leishman or Risø

Wind field grid format: Rectangular

Regular wave model:

Airystr, Stokes

Irregular wave model:

JONSWAP/PM

Hydro model: ME + MF

DLL

FAST V8 /

OpenFAST v0.1.0

Structural dynamics: Substructure: FEM +

Craig-Bampton; Turbine: FEM preprocessor +

Modal/MBS; Blades: Modal

Beam model: Substructure and blades:

Timoshenko; Turbine: Euler-Bernoulli

Damping model: Modal

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

Dynamic wake model: Pitt and Peters

Dynamic stall model: Beddoes-Leishman

Wind field grid format: Rectangular

Regular wave model:

Airystr, Stokes 2nd order

Irregular wave model:

JONSWAP/PM

Hydro models: ME

DLL,

UD

FloaWDyn Structural dynamics: FEM (co-rotational

formulation)

Beam model: Euler-Bernoulli

Damping model: Support structure: stiffness and

mass proportional Rayleigh

Basic aerodynamics: BEM (AeroDyn) + Glauert correction + Prandtl tip and

root losses

Dynamic wake model: Peters-He dynamic inflow

Dynamic stall model: Beddoes-Leishman

Wind field grid format: Single point wind at hub height

Regular wave model:

Airystr, Stokes 5th order

Irregular wave model:

JONSWAP/PM

Hydro model: ME

DLL,

UD

HAWC2 Structural dynamics: MBS/FEM

Beam model: Timoshenko

Damping model: Support structure and blades:

stiffness proportional Rayleigh

Basic aerodynamics: BEM with Madsen and Larsen correction for shear and

dynamic inflow, Glauert and Coleman modification for skewed inflow

Dynamic wake model: Øye

Dynamic stall model: Øye or Beddoes-Leishman

Wind field grid format: Rectangular

Regular wave model:

Airystr

Irregular wave model:

JONSWAP/PM

Hydro model: ME

DLL

NK-UTWind Structural dynamics: FEM

Beam model: Euler-Bernoulli

Damping model: Support structure: stiffness

proportional Rayleigh; Blades: modal

Basic aerodynamics: BEM (AeroDyn v14)

Dynamic wake model: Pitt and Peters

Dynamic stall model: Beddoes-Leishman

Wind field grid format: Rectangular

Regular wave model:

Airystr

Irregular wave model:

JONSWAP/PM

Hydro model: ME

DLL

MoWiT Structural dynamics: Support structure: FEM;

Blades: MBS + modal reduced bodies

Beam model: Support structure: Timoshenko

Damping model: Support structure: stiffness and

mass proportional Rayleigh; Blades: modal

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

+ skew inflow correction + relaxation of induction factors

Dynamic wake model: Øye

Dynamic stall model: Øye or Beddoes-Leishman

Wind field grid format: Rectangular

Regular wave model:

Airystr, Stokes 5th order

Irregular wave model:

JONSWAP/PM

Hydro model: ME + MF

DLL

SAMCEF

Wind Turbines

18.0 (SWT)

Structural dynamics: FEM/MBS/Modal

(Craig-Bampton)

Beam model: Timoshenko

Damping model: Support structure: stiffness

proportional Rayleigh; Blades: modal

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

+ skew inflow correction + relaxation of induction factors

Dynamic wake model: Øye

Dynamic stall model: Beddoes-Leishman

Wind field grid format: Rectangular or polar

Regular wave model:

Airystr, Stokes 5th order

Irregular wave model:

JONSWAP/PM

Hydro model: ME + MF

DLL,

UD

SIMA Structural dynamics: FEM

Beam model: Euler-Bernoulli with shear correction

Damping model: Stiffness and mass proportional

Rayleigh

Basic aerodynamics: BEM + Glauert correction + Prandtl tip and root losses

+ skew inflow correction

Dynamic wake model: Øye

Dynamic stall model: Øye

Wind field grid format: Rectangular

Regular wave model:

Airystr, Stokes 5th order

Irregular wave model:

JONSWAP/PM

Hydro model: ME + MF

DLL,

UD

Simpack Structural dynamics: MBS, modal reduced FEM

Beam model: Timoshenko

Damping model: Modal

Basic aerodynamics: BEM (AeroDyn v15) + Glauert correction + Prandtl tip

and root losses

Dynamic wake model: Pitt and Peters

Dynamic stall model: Beddoes-Leishman

Wind field grid format: Rectangular

Regular wave model:

Airystr

Irregular wave model: –

Hydro model: ME + MF

DLL

Airystr – linear Airy wave theory with Wheeler stretching

BEM – blade element momentum

DLL – dynamic-link library

FEM – finite element method

JONSWAP – deep-water wave spectrum

ME – semi-empirical Morison’s equation

MF – MacCamy-Fuchs linear diffraction theory

PM – Pierson-Moskowitz spectrum

Stokes – nonlinear Stokes wave theory

Stream – Dean’ Stream function wave theory

UD – user-defined subroutine

4

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kp_2036

kp_307

kp_308

kp_309

kp_305 kp_306

kp_310

Northern 

leg

Northwest side

Western 

leg

Southwest side

kp_405

kp_406

kp_2044

kp_2043

Eastern 

leg

Southern 

leg

Southeast side

kp_2011

Northern 

leg

Northeast side

kp_2036

Pitch angle 

Azimuth position

Yaw angle

Generator speed

Electrical power

Tower bottom 

acceleration at 

MSL +26.2 m

Tower top 

acceleration at 

MSL + 87.5 m

kp_2046

kp_2021

kp_2026

Strain gauge [μm/m]

Accelerometer [m/s2]

Angle measurement [deg]

Power measurement [kW]

Speed measurement [rpm]

Bending moment [kNm]

Tower bottom 

bending moment 

at MSL +26.2 m

FIGURE 2: PLACEMENT OF SENSORS ALONG RNA, TOWER, AND JACKET SUBSTRUCTURE, WHICH WERE AVAILABLE FOR

OC5 PHASE III.

• Significant wave height, expressed in m.

• Peak spectral wave period, expressed in s.

• Wave direction w.r.t. true north, expressed in deg.

The met-ocean conditions were not measured directly at the location of the OWT. The wind data were recorded at the FINO

I platform and the wave data were recorded nearby the transformer station located in the southeast corner of the alpha ventus

wind farm (see Fig. 1). The wind data were available in terms of

10-minute statistics, whereas the wave data were provided as a

3-hour statistic.

Other data not directly available from measurements

Some data were not directly available from the RAVE measurements or were difficult to derive. Therefore, it was necessary

to assume their values based on the available standards or engineering practice of project participants:

• Inclination of the mean wind flow w.r.t. to a horizontal plane

according to IEC 61400-3 [12].

• Air density according to IEC 61400-3 [12].

• Seawater density according to IEC 61400-3 [12].

• Marine growth vertical range, thickness, and density according to OWEC Tower documentation.

• Drag and inertia coefficients for jacket members covered

with marine growth were derived from the SWE reference

model implemented in Flex5-Poseidon.

VALIDATION METHODOLOGY

The postprocessing of all simulation results was performed

internally at Fraunhofer IWES. The simulation results were compared against the measurements in terms of time series, discrete

Fourier transforms (DFTs), power spectral densities (PSDs), and

probability density functions (PDFs). Note that the DFT shows

the amplitude spectrum of the signal, while the PSD describes

how the power of the signal is distributed over frequency.

It should be noted that a direct comparison of the simulated

and measured time series was not always possible due to: (1)

The differences in the coordinate systems, in which the simulated time series were reported and in which the measurements

were recorded; and (2) Output capabilities of the majority of simulation tools. Therefore, some signal processing was necessary

before the data could be compared. The methodology for signals

alignment and comparison is described in this section.

Coordinate systems

All simulation results were delivered by the project participants in terms of time series data. The time series of bending

moments, forces, and accelerations were provided in the member

local coordinate system, where the member xlocal-axis is always

aligned along the member centerline, zlocal-axis is perpendicular

to the xlocal-axis and aligned according to the direction cosines

for the zlocal-axis, and ylocal-axis creates the right-hand Cartesian

coordinate system.

All strain and acceleration measurements were provided

w.r.t. the local coordinate systems defined in [13]. These local

5

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coordinate systems are not the same as those used for the simulation results. The alignment of these local coordinate systems

was necessary and is described in the following paragraphs.

Wind, wave, and nacelle directions were specified w.r.t.

to true north—for both—the measurements and simulations as

shown in Fig. 1. Positive values indicate the clockwise direction

when looking from the top.

Rotor speed and its azimuth position are defined as positive

values when the rotor rotates in the clockwise direction when

looking downwind. It is the same convention for the measurements and simulations.

Strain signals

Strain outputs are not directly available from the majority of

the OWT simulation tools. Therefore, it was necessary to derive

strain, ε, based on the simulated time series of the out-of-plane

bending moments, My local, Mzlocal, axial force, Fx local, and geometrical properties of the circular cross section, such as the outer

diameter and the wall thickness of the given member.

The relation between the stress, σ, and the bending moment,

M, is defined as:

σ =

M

I

y (1)

where I is the area moment of inertia for a hollow cylindrical

cross section in m4and y is the distance to the neutral axis in m.

The relation between the stress, σ, and the strain, ε, is described as:

σ = Eε (2)

where E is Young’s modulus in N/m2.

Combining Eq. 1 with Eq. 2, adding the axial force contribution, and multiplying with 106

leads to strain expressed in

μm/m:

ε =



My

IE

+

Fx local

AE 

· 106(3)

where Fx local is the axial force in N and A is the cross-sectional

area of the member in m2.

Furthermore, it was required to align these derived strains

w.r.t. the circumferential positions of the strain gauges installed

on the jacket substructure, as described by Eq. 4. An example of

a local coordinate system (simulation tool) and a position of the

strain gauge (real jacket) is shown for the node kp 2046 in Fig. 3.

M = My local cos(α) + Mzlocalsin(α) (4)

where My local and Mzlocal are the out-of-plane bending moments

in the member local coordinate system, expressed in Nm and

α is the rotation angle to align with the strain gauge position,

expressed in deg.

Eastern 

leg

Southern 

leg

Northern 

leg

zlocal

ylocal

0

o

270o

90o

180o

22. 5

o

location of 

strain gauge 

kp_2046

Western 

leg

α

kp_2046

Northwest 

side

Southwest 

side

FIGURE 3: MEMBER LOCAL COORDINATE SYSTEM AT NODE

KP 2046 (RED ARROWS), LOCATION OF THE STRAIN GAUGE

INDICATED BY BLUE ARROW TIP.

The full-scale strain measurements in the jacket substructure

were not calibrated. Therefore, it was necessary to remove the

arithmetic mean, ε ̄, from the measured and computed strain time

series, εi, before their comparison:

ε = εi −ε ̄ (5)

Some of the measured strain signals had a flipped sign. They

were probably installed in the upside-down position. Their sign

was corrected during the postprocessing.

Acceleration signals

The accelerometer data are available at the tower bottom and

top in two perpendicular directions in the horizontal plane. The

vector magnitude of acceleration was calculated from measured

and computed acceleration time series according to the following

Eq.:

|a| =

q

a

2

y local +a

2

zlocal (6)

Other signals

Other signals, such as electrical power, generator speed, yaw

angle, pitch angle, and azimuth angle are compared directly between the measurements and the simulated data.

6

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TABLE 3: OVERVIEW OF VALIDATION LOAD CASES IN OC5 PHASE III.

Load Case OWT settings Wind conditions Marine conditions Simulation settings

1.0 – Idling below cut-in

wind speed

• Free idling, rotor brake disabled

• φb = 0◦

• θp = 90◦

• θyaw = 26.8◦

• Steady, deterministic wind

• Vhub = 3.61 m/s

• θ wind = 270◦

• α = 0.14

• Still water • Tpre = user defined

• Tsim = 100 s

• ∆t = 0.05 s

1.1x – Idling below cut-in

wind speed

• Free idling, rotor brake disabled

• φb = 0◦

• θp = 90◦

• θyaw = 26.8◦

• Steady, deterministic wind

• Vhub = 3.61 m/s

• θ wind = 270◦

• α = 0.14

• Irregular Airy with PM, 6 seeds

• Hs = 0.89 m

• Tp = 9.88 s

• θwave = 345.8◦

• Tpre = user defined

• Tsim = 600 s

• ∆t = 0.05 s

1.2 – Idling below cut-in

wind speed, RNA rotation in

counterclockwise direction

• Free idling, rotor brake disabled

• φb = 0◦

• θp = 90◦

• θ ̇

yaw = −0.28◦

/s

• θyaw,init = 20◦

• θyaw,final = 58◦

• Steady, deterministic wind

• Vhub = 3.61 m/s

• θ wind = 263.6◦

• α = 0.14

• Regular Airy wave

• H = 0.57 m

• T = 10.65 s

• θwave = 344.6◦

• Tpre = user defined

• Tsim = 1150 s

• ∆t = 0.05 s

2.1x – Power production

below rated wind speed

• Power production governed by

external controller

• θp = 0◦

• θyaw = 267.5◦

• Stochastic wind field,

Kaimal spectrum model, 6 seeds

• Vhub = 7.9 m/s

• σlong = 0.38 m/s

• σlat = 0.7 σlong m/s

• σvert = 0.5 σlong m/s

• θ wind = 255.9◦

• α = 0.14

• Irregular Airy with PM, 6 seeds

• Hs = 2.55 m

• Tp = 6.57 s

• θwave = 330.2◦

• Tpre = user defined

• Tsim = 600 s

• ∆t = 0.05 s

2.2x – Power production

above rated wind speed

• Power production governed by

external controller

• θyaw = 235.5◦

• Stochastic wind field,

Kaimal spectrum model, 6 seeds

• Vhub = 16.57 m/s

• σlong = 0.45 m/s

• σlat = 0.7 σlong m/s

• σvert = 0.5 σlong m/s

• θ wind = 218.3◦

• α = 0.28

• Irregular Airy with PM, 6 seeds

• Hs = 1.60 m

• Tp = 6.26 s

• θwave = 260.4◦

• Tpre = user defined

• Tsim = 600 s

• ∆t = 0.05 s

2.3x – Power production

above rated wind speed

• Power production governed by

external controller

• θyaw = 254.3◦

• Stochastic wind field,

Kaimal spectrum model, 6 seeds

• Vhub = 18.98 m/s

• σlong = 0.34 m/s

• σlat = 0.7 σlong m/s

• σvert = 0.5 σlong m/s

• θ wind = 231.6◦

• α = 0.36

• Irregular Airy with PM, 6 seeds

• Hs = 1.34 m

• Tp = 5.84 s

• θwave = 272.7◦

• Tpre = user defined

• Tsim = 600 s

• ∆t = 0.05 s

3.1x – Shutdown • Normal shutdown governed by

external controller

• Shutdown triggered at Tshut-down

• Generator torque drops linearly

from the rated value to 0 Nm within

10 s

• θ ̇

p = 2.54◦

/s

• θyaw = 268.3◦

• Stochastic wind field,

Kaimal spectrum model, 6 seeds

• Vhub = 14.12 m/s

• σlong = 0.56 m/s

• σlat = 0.7 σlong m/s

• σvert = 0.5 σlong m/s

• θ wind = 247.4◦

• α = 0.14

• Irregular Airy with PM, 6 seeds

• Hs = 1.51 m

• Tp = 6.82 s

• θwave = 284.4◦

• Tpre = 200 s

• Tshut-down = Tpre + 50 s

• Tsim = 600 s

• ∆t = 0.05 s

θp – blade pitch angle, 90◦for blades pitched to feather

θ ̇

p – blade pitch rate, positive value toward feather

θyaw – yaw position w.r.t. true north

θyaw,init – initial yaw position w.r.t. true north

θyaw,final – final yaw position w.r.t. true north

θ ̇

yaw – yaw rate, negative value for counterclockwise rotation

θwave – wave mean direction w.r.t. true north

θwind – wind mean direction w.r.t. true north

σlong – standard deviation of longitudinal wind component

σlat – standard deviation of lateral wind component

σvert – standard deviation of vertical wind component

φb – blade azimuth angle, 0◦for first blade pointing upward

α – wind shear

∆t – output time step

x = a, b, c, d, e, f – six independent seeds for wind and sea state

PM – Pierson-Moskowitz spectrum

H – regular wave height

Hs – significant wave height

T – regular wave period

Tp – peak-spectral wave period

Tpre – presimulation time

Tshut-down – shutdown trigger time

Tsim – simulation time

Vhub – mean wind speed at the hub height

VALIDATION LOAD CASES

The following operating conditions of the wind turbine were

chosen for the validation: (1) Idling below the cut-in wind speed;

(2) RNA rotation maneuver below the cut-in wind speed; (3)

Power production below and above the rated wind speed; and (4)

Power production followed by the shutdown. Seven validation

load cases (LCs) were defined based on these operating conditions. Their complexity increases, allowing for the stepwise

comparison of results and tracing back possible errors coming

from different models and methods implemented in the simulation tools. Table 3 lists all validation LCs that were simulated in

Phase III.

The following settings were common for all LCs:

• Fully-flexible OWT models.

7

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• Foundation stiffness was modeled, depending on the individual participant preferences:

• By the apparent fixity method, where the OWT piles

were fixed at 8.5 m below the seabed—4S-Simis

(ASHES), DTU (HAWC2), EDFRD (DIEGO, FAST

V8), Envision (SWT), IFPEN-PR (DeepLW), IWES

(MoWiT), NREL (OpenFAST v0.1.0), SWE (Simpack), and UOU (FAST V8).

• By applying p-y curves along the foundation piles extending to 42 m below the seabed—CGC, DNVGL

(Bladed V4.8), ClassNK (NK-UTWind), NTNU

(SIMA), and UPC (FloaWDyn).

• Static blade pitch error of -0.3 deg for blade 2 and

+0.3 deg for blade 3 — 4S-Simis (ASHES), CGC, DNVGL

(Bladed V4.8), DTU (HAWC2), EDFRD (DIEGO), Envision (SWT), IFPEN-PR (DeepLW), NREL (OpenFAST

v0.1.0), NTNU (SIMA), SWE (Simpack), and UOU (FAST

V8).

• Static blade mass imbalance for blade 2 by increasing the

blade density over the entire blade by 0.6% — 4S-Simis

(ASHES), CGC, DNVGL (Bladed V4.8), DTU (HAWC2),

EDFRD (FAST V8, DIEGO), Envision (SWT), IFPEN-PR

(DeepLW), NTNU (SIMA), SWE (Simpack), and UOU

(FAST V8).

• The jacket substructure rotated 30◦counterclockwise w.r.t.

true north as shown in Fig.1.

• The jacket substructure legs were flooded up to the mean sea

level of 28 m and the braces were sealed.

• Inclination of the mean wind flow w.r.t. a horizontal plane

of 0◦.

• Air density of 1.225 kg/m3.

• Seawater density of 1025 kg/m3.

• Mean sea level (MSL) of 28 m.

• No sea current.

• Marine growth applied.

In LC 1.0, the idling turbine below the cut-in wind speed was

simulated. This LC was meant to check mean values and signs

of the signals from different sensors from the measurements and

the simulation results. In LC 1.1x, the idling turbine below the

cut-in wind speed was simulated as in LC 1.0. The only difference between these two LCs was introduction of the stochastic

sea state instead of the still water. This LC was meant to check

the sensitivity of the numerical models to stochastic marine conditions. In LC 1.2, the yaw maneuver of the idling turbine below

the cut-in wind speed was simulated.

In LCs 2.1x, 2.2x, and 2.3x the power production was simulated with the stochastic wind files and the stochastic sea state.

LCs 2.1x simulate the power production below rated wind speed,

while LCs 2.2x and 2.3x simulate the power production above

rated wind speed.

In LC 3.1x, the power production above the rated wind

speed followed by the normal shutdown was simulated with the

stochastic wind files. This LC was meant for analysis of a transient event.

Turbulent wind fields were generated at NREL for LCs

2.X.x and 3.1x. Detailed input parameters for generation of wind

fields with Kaimal spectrum by Veers method were specified by

Popko [14]. It was decided to use six independent wind seeds (x

= a, b, c, d, e, f), each 10 minutes long, for every single LC in

order to get statistically comparable results as recommended in

the IEC 61400-1 standard [15]. The stochastic wind files could

also be generated individually by those participants, whose tools

are not able to utilize the provided wind fields due to a different

grid format.

For each LC, the outputs were recorded at a number of nodal

points denoted as sensors located at the RNA, the tower, and the

jacket substructure, as shown in Fig. 2. The location of these

outputs corresponds to the location of physical sensors on the

actual OWT.

Initial simulation transients were removed by using a presimulation time, Tpre, which is simulated but cut-out from the

result files in all simulations. For the majority of LCs, Tpre was

not explicitly defined. It was chosen individually by each participant in order to avoid initial numerical transients and to satisfy

the initial conditions of the given LC. The time step for data output was defined as ∆t = 0.05 s for all LCs.

SELECTED RESULTS

This section presents example results of the OWT models

validation against the measurements from the Senvion 5M wind

turbine and the jacket support structure from OWEC Tower from

the alpha ventus wind farm.

Presented results give a general overview of differences

between the measurements and the OC5 Phase III simulation

results. The results discussed in this paper represent the final

outcome of multiple modelling iterations that were necessary

to develop numerical models of the OWT and validation LCs.

During each modelling iteration, the participants updated their

simulation settings to better match with the measurements.

RNA Rotation Maneuver

Figures 4 and 5 show time series of strains with a mean offset adjustment at kp 2036 and kp 307, which are placed at the

top of the northern leg and at the lowest X-brace at the northwest

jacket side, respectively. The exact locations of these sensors are

shown in Fig. 2. The strain signal is changing sinusoidally during the RNA maneuver. The maneuver was simulated in LC 1.2

as described in Tab. 3.

The measurements are plotted with black curves and are denoted at the bottom of the plot legends as Event 2011-03-23. In

8

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kp_2036 northern leg top, UoU removed due to severe oscillations

FIGURE 4: LC 1.2 – STRAIN TIME SERIES WITH MEAN OFFSET ADJUSTMENT AT KP 2036, TOP OF THE NORTHERN

LEG.

kp_307 north-west 

FIGURE 5: LC 1.2 – STRAIN TIME SERIES WITH MEAN

OFFSET ADJUSTMENT AT KP 307, LOWEST X-BRACE AT

NORTHWEST JACKET SIDE.

both Figs. there are some oscillations visible in the measurement

signals. Their dominant frequency corresponds to the 1st global

mode of the OWT at around 0.31 Hz. In the case of the measurements recorded at the kp 307 sensor at the lowest X-brace, there

is also an additional frequency of around 0.9 Hz. This frequency

corresponds to the torsional mode of the support structure. Some

irregular peaks appear every 110 – 180 s in the measurements

from the kp 2036 sensor at the top of the northern leg. They

result from the superposition of two frequencies. The first frequency is induced by the yaw mechanism, which rotates the RNA

with 2.54◦/s (0.007 Hz or 142 s), and the second frequency results from the slowly rotating rotor of the actual wind turbine

with around 0.3 rpm (0.005 s Hz or 200 s). On the contrary to the

measurements, in the majority of the simulation results the rotor speed was almost zero—it was standing still during the RNA

rotation.

In general, the simulation results of the majority of the participants are very well aligned with the measurements. Their

amplitudes and phases match the measurement. It should be

noted that steady oscillations are present in the majority of the

simulation results at the kp 307 sensor (Fig. 5). They are more

pronounced than the oscillations visible in the measurements.

These oscillations from the simulations are dominated by the

tower torsional mode around 0.9 Hz or the 1st blade flapwise

frequency, which is in the range from 0.58 to 0.65 Hz. These frequencies were triggered during the simulations due to the sudden

start of the yaw maneuver. The project participants did not have

access to the yaw mechanism definition neither to the real turbine controller and the yaw maneuver initialization procedure.

Some transient oscillations are visible in the UOU (FAST V8)

results the kp 307 sensor. However, they could be mitigated by

extending the presimulation time. The DNVGL (Bladed V4.8)

strain time series at the kp 307 sensor has a flipped sign associated with the incorrect definition of the output coordinate system.

Power Production Above Rated Wind Speed

Figures 6 and 7 show generator power and speed time series for LC 2.2e, respectively. The measurement time series

are plotted with different gray shade curves. They come from

eight events recorded between April 4 and April 6, 2011, when

the met-ocean conditions were relatively comparable. A high

variation of the measured generator power, compared to the simulation results, is observed. The standard deviation of generator

power varies between 53 and 66 kW for different measurement

events. For the simulation results, the standard deviation is

one order of magnitude smaller and varies between 1 and 6 kW.

Furthermore, a very significant high frequency content in the

measured generator power, compared to the simulation results,

is observed. The PSD analysis proved that the measured generator power has significantly more energy across a wide frequency

range. Peaks around 2 Hz are not really visible in the measured

data, but are clearly evident for simulations (not shown in this paper). This indicates that a low-pass filter was used in the actual

Senvion controller. On the other hand, the measured generator

speed has only a slightly smaller standard deviation compared to

the simulation results. For the measurements it varies within the

range of 1.61 to 2.62 rpm, whereas for the simulations it varies

from 2.59 to 3.28 rpm. It can be concluded that the actual Senvion controller acts more aggressively on the generator torque to

keep the generator speed constant.

It should be mentioned that a similar, aggressive torque

control was also observed in the partial loading region in the

measurements for LC 2.1.

Figures 8 and 9 show single-sided amplitude spectra from

DFT of aggregated strain time series of six simulation seeds from

LC 2.2 at kp 2026 (eastern leg top) and kp 2021 (eastern leg

bottom), respectively. The exact locations of these sensors are

9

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generator power

FIGURE 6: LC 2.2E – ZOOMED GENERATOR POWER TIME

SERIES.

generator speed

FIGURE 7: LC 2.2E – ZOOMED GENERATOR SPEED TIME

SERIES.

shown in Fig. 2. Vertical dotted lines in both DFT figures indicate subsequent rotor harmonics (1P, 3P, 6P, and 9P) above the

rated wind speed, Vr, and global eigenmodes that are also described with the vertical text. These dotted lines help to identify

sources of frequency peaks.

The strain amplitude at the frequency corresponding to 1P

(0.2 Hz) are significantly underpredicted by IWES (MoWiT) for

both sensors—higher (kp 2026) and lower (kp 2021) location at

the eastern leg. IWES did not use the blade mass imbalance and

the static pitch error in its OWT model. On the other hand, a

strong overprediction of the 1P (0.2 Hz) frequency is observed in

case of NTNU (SIMA). This might be associated with the modeling approach utilized by NTNU.

The strain amplitude at the frequency corresponding to

the 1st global mode (0.31 Hz) are significantly underpredicted

by simulations for both sensors—higher (kp 2026) and lower

(kp 2021) location at the eastern leg. The strain amplitude at

the frequency dominated by 3P and the 1st flapwise mode (both

around 0.6 Hz) is relatively well captured in the simulation reLC 2.2 – 2026 eastern leg top

FIGURE 8: LC 2.2 – SINGLE-SIDED AMPLITUDE SPECTRUM

FROM DFT OF AGGREGATED STRAIN TIME SERIES OF SIX

SEEDS AT KP 2026, EASTERN LEG TOP.

LC 2.2 – 2021 eastern leg bottom

FIGURE 9: LC 2.2 – SINGLE-SIDED AMPLITUDE SPECTRUM

FROM DFT OF AGGREGATED STRAIN TIME SERIES OF SIX

SEEDS AT KP 2021, EASTERN LEG BOTTOM.

sults for both locations. The amplitudes at higher frequencies at

around 1.8 Hz to 1.9 Hz are significantly overpredicted by simulations at kp 2021 at the bottom of the leg. For kp 2026 at the

top of the leg this is not observed. There is a significant increase

of amplitudes for the measurements at the frequencies above 2

Hz at kp 2021 at the bottom of the leg. This is not observed in

the simulation results. All these observations also apply to LC

2.3.

In summary, the underprediction of amplitudes at the lower

frequencies (up to 0.4 Hz) would indicate issues with the system damping. The damping values used in the numerical models

might be too high. The estimation of an appropriate damping

value for the structure is difficult without the measurements.

The overprediction of simulated strain amplitudes at higher frequencies at around 1.9 Hz might be related to filters used in the

controller.

10

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Shutdown Transient Event

The power production followed by the normal shutdown was

simulated according to LC 3.1x defined in Tab. 3. This LC was

meant for the analysis of the transient behaviour of the OWT. The

measurements are always plotted with black curves and denoted

at the bottom of the plot legends as Event 20110401-01.

Figure 10 shows the time series of the pitch angle during

the normal operation of the OWT just before the shutdown was

triggered (45 to 50 s), during the shutdown (50 to 83 s), and just

after (83 to 95 s). For the majority of the simulation results, the

mean pitch angle is slightly larger than the measured one during

the power production (45 to 50 s). This is caused by differences

in blade aerodynamics between the tuned NREL 5-MW blades

(utilized in OC5 Phase III) and the real Senvion 5M turbine, as

explained by Popko [7]. The measured pitch angle during the

shutdown increases linearly to 70◦from 50 up to 76 s, afterward

it is kept constant for around 4 s, and then continues pitching to

feather with a slightly higher rate. The reason for this intermediate stop in pitching in the measured signal is unknown. For the

simulations it was decided to use the constant pitch action from

50 to 83 s until all blades are pitched to 90◦. This is a reasonable

simplification.

pitch angle

FIGURE 10: LC 3.1F – PITCH ANGLE TIME SERIES.

Figure 11 shows the generator power time series. The majority of the simulation results follow the measurement. The

measured generator power drops nonlinearly from its rated value

to 0 Nm within 10 s. For the simulation of OWT models, it was

decided to implement a simplified linear drop of the generator

torque. This simplification can be justified as it should not have

a large impact on the loads in the jacket substructure.

Figure 12 shows the generator speed time series. The measured speed is always kept higher during the shutdown event

compared to the simulation results. This is caused by differences

in blade aerodynamics between the tuned NREL 5-MW blades

(utilized in OC5 Phase III) and the real Senvion 5M turbine, and

the control system.

LC 3.1f – generator power

FIGURE 11: LC 3.1F – GENERATOR POWER TIME SERIES.

LC 3.1f – generator speed

FIGURE 12: LC 3.1F – GENERATOR SPEED TIME SERIES.

LC 3.1f – 2026 strain, eastern leg top – UoU removed due to severe oscillations

FIGURE 13: LC 3.1F – STRAIN TIME SERIES WITH MEAN

OFFSET ADJUSTMENT AT KP 2026, TOP OF THE EASTERN

LEG.

Figure 13 shows the time series of strain with mean offset adjustment during the shutdown event at the kp 2026 sensor

located at the top of the eastern leg (Fig. 2). Note that the

11

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TABLE 4: PERCENTAGE OF CRITICAL DAMPING RATIO FOR MAIN OWT COMPONENTS MODELED BY DIFFERENT PHASE III

PARTICIPANTS.

Participant Tool Jacket +

piles

Tower Blades Drivetrain Comments

UPC FloaWDyn 0.16% 0.40% no damping rigid Support structure: Stiffness and mass proportional Rayleigh, Blade: No damping

IWES MoWiT 0.16% 1.24% 2% 5% (torsional) Support structure: Stiffness and mass proportional Rayleigh, Blade: Modal (8 modes)

Envision SWT 0.16% 0.40% 0.48% 4.77% (torsional) Support structure: Stiffness proportional Rayleigh, Blade: Modal

NTNU SIMA 0.16% 0.40% 0.23% rigid Support structure and blade: Stiffness proportional Rayleigh

4S-Simis ASHES 0.16% 0.40% no damping flexible, no damping Support structure: Stiffness proportional Rayleigh, Blade: No damping

ClassNK NK-UTWind 1.24% 1.24% 0.48% rigid Support structure: Stiffness proportional Rayleigh, Blade: Modal (3 modes)

DTU HAWC2 0.70% 0.40% 0.48% rigid Support structure and blade: Stiffness proportional Rayleigh

IFPEN-PR DeepLW no damping 0.40% 0.48% rigid Tower and blade: Stiffness proportional Rayleigh

CGC Bladed V4.8 0.50% 0.50% 0.48% 4.77% (torsional) Support structure and blade: Modal (16 support structure modes, 7 blade modes)

DNVGL Bladed V4.8 1.24% 1.24% 0.26% flexible Support structure and blade: Modal (6 blade modes)

NREL OpenFAST 1% 0.80% 0.48% flexible Support structure and blade: Modal (8 jacket modes, 4 tower modes, 3 blade modes)

UOU FAST V8 1% 0.40% 0.48% rigid Support structure and blade: Modal (8 jacket modes, 4 tower modes, 3 blade modes)

EDFRD FAST V8 0.16% 1.24% 0.48% rigid Support structure and blade: Modal (4 tower modes, 3 blade modes)

EDFRD DIEGO 1.24% 1.24% 0.48% rigid Support structure and blade: Stiffness and mass proportional Rayleigh

SWE Simpack 1% 0.40% 0.48% rigid Support structure and blade: Modal (8 jacket modes, 4 tower modes, 3 blade modes)

measurement signal has a smaller overshoot at around 60 s compared to the majority of the simulation results. The real turbine

controller is able to efficiently mitigate loads resulting from the

shutdown.

PDF kp 2046 western leg 

FIGURE 14: LC 3.1 – PDF OF AGGREGATED STRAIN TIME

SERIES OF SIX SEEDS AT KP 2046 AT THE TOP OF WESTERN LEG, CALCULATED FROM THE LAST 300 S OF SIMULATION.

Figures 14, 15, 16, and 17 show PDFs of strain at the western (kp 2046) , northern (kp 2036), eastern (kp 2026), and the

southern (kp 2011) leg, respectively. All PDFs were calculated

based on the last 300 s of the 600 s time series. This was done

to analyze damping of the entire OWT. Standard deviations for

all signals are shown in the legends. The critical damping values

used in the numerical models are presented in Tab. 4.

LC 3.1 – PDF kp 2036 northern leg

FIGURE 15: LC 3.1 – PDF OF AGGREGATED STRAIN TIME

SERIES OF SIX SEEDS AT KP 2036 AT THE TOP OF NORTHERN LEG, CALCULATED FROM THE LAST 300 S OF SIMULATION.

Please note that the appropriate damping setting depends on

how the damping is applied (modal versus stiffness/mass proportional as defined in Tab. 4) and how the mode is defined

(fixed-fixed versus fixed-free). For example, in FAST a percentage of critical damping can be specified for the tower modes,

as well as for the jacket modes that are derived through the

Craig-Bampton (CB) reduction. Fixed bottom substructures in

FAST are modeled in the SubDyn module, which assumes a fixed

boundary condition at the top and the bottom of the jacket for the

CB modal reduction. Therefore, the structural damping defined

for the CB reduction only affects jacket internal modes and has

little affect on global bending modes of the entire system that

12

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PDF kp 2026 eastern leg

FIGURE 16: LC 3.1 – PDF OF AGGREGATED STRAIN TIME

SERIES OF SIX SEEDS AT KP 2026 AT THE TOP OF EASTERN

LEG, CALCULATED FROM THE LAST 300 S OF SIMULATION.

PDF kp 2011 southern leg

FIGURE 17: LC 3.1 – PDF OF AGGREGATED STRAIN TIME

SERIES OF SIX SEEDS AT KP 2011 AT THE BOTTOM OF

SOUTHERN LEG, CALCULATED FROM THE LAST 300 S OF

SIMULATION.

involve displacement of the TP. On the other hand, the damping in NK-UTWind, DIEGO (Rayleigh damping) and in Bladed

(modal damping) is actually applied to the entire support structure (jointly to the tower and the substructure)—it is not split as

in FAST. In other tools this might be done differently; therefore it

is difficult to directly compare damping values used in different

simulation tools.

A relatively good agreement between the measurements and

the simulation results is observed at the northern and southern

legs — the standard deviation of the measurement is only slightly

larger than the standard deviations for the majority of simulation

results. This would imply that the soil and structural damping

values, which were used in the OWT models, were tuned correctly. Much larger oscillations of measurements are observed at

the western and eastern legs. In this case the standard deviation

of the measurements is around four times larger than the standard deviations of simulated signals. This behaviour might be

attributed to the actual soil properties, since a similar directional

phenomenon was also observed in the measurement of the idling

turbine in LCs 1.0 and 1.1x.

CONCLUSIONS

Validation of the OWT numerical models against the fullscale open-ocean system was a particularly challenging task. The

participants of OC5 Phase III did not have access to the real

blade design data and the full wind turbine controller due to

confidentiality—it should not be forgotten that the load effects

are sensitive to the controller and airfoil properties, which were

only approximated in the modeling approach. Also, large measurement uncertainties related to the open-ocean environment

created additional challenges when setting up the validation LCs.

There was also the need for a thorough quality check of sensor

measurements, as inconsistencies were seen for ones that had

been originally identified as accurate by RAVE. The estimation

of an appropriate damping value for the structure turned out to be

an issue. First of all, the originally provided values seemed to be

too high. Second, different tools utilize different damping models for different turbine components, which made the damping

tuning even more challenging.

Nevertheless, the obtained results are satisfactory and show

that the numerical models can reasonably mimic the full-scale

system when they are carefully tuned.

The experience from the OC5 project gathered in validating

small- and full-scale systems will lead to a more rigorous validation practice, which will be developed and employed in the

follow up project called Offshore Code Comparison Collaboration, Continued, with Correlation, and unCertainty (OC6). The

new project will have a strong emphasis on quantifying uncertainty in test campaigns used for validation.

ACKNOWLEDGEMENTS

OWEC Tower is acknowledged for releasing the jacket substructure, transition piece, and foundation design data for Phase

III.

Senvion is acknowledged for sharing the definition of the

Senvion 5M wind turbine and its tower.

The RAVE consortium is acknowledged for releasing the

measurement data from the alpha ventus wind farm.

This work was authored [in part] by the National Renewable

Energy Laboratory, operated by Alliance for Sustainable Energy,

LLC, for the U.S. Department of Energy (DOE) under Contract

No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable

Energy Wind Energy Technologies Office. The views expressed

in the article do not necessarily represent the views of the DOE

13

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
or the U.S. Government. The U.S. Government retains and the

publisher, by accepting the article for publication, acknowledges

that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published

form of this work, or allow others to do so, for U.S. Government

purposes.

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