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Contract No. DE-AC36-08GO28308

Verification of a Numerical Model of the 

Offshore Wind Turbine from the Alpha 

Ventus Wind Farm Within OC5 Phase III 

Preprint

W. Popko, M. L. Huhn, A. Robertson, J. Jonkman, 

F. Wendt, K. Muller, M. Kretschmer, F. Vorpahl, 

T. Ruud Hagen, C. Galinos, J. Le Dreff, P. Gilbert, 

B. Auriac, F. Navarro Villora, P. Schunemann, 

I. Bayati, M. Belloli, S. Oh, Y. Totsuka, J. Qvist, 

E. Bachynski, S. Høegh Sørum, P. E. Thomassen, 

H. Shin, F. Vittori, J. Galvan, Climent Molins, 

P. Bonnet, T. van der Zee, R. Bergua, K. Wang, P. Fu, 

and J. Cai

Full author affiliation inside

Presented at the ASME 2018 37th International Conference on 

Ocean, Offshore and Arctic Engineering (OMAE 2018)

Madrid, Spain

June 17‒22, 2018 

Suggested Citation 

Popko, Wojciech, Matthias L. Huhn, Amy Robertson, Jason Jonkman, Fabian 

Wendt, Kolja Müller, Matthias Kretschmer, Fabian Vorpahl, Torbjørn Ruud 

Hagen, Christos Galinos, et al. 2018. “Verification of a Numerical Model of 

the Offshore Wind Turbine from the Alpha Ventus Wind Farm Within OC5 

Phase III: Preprint.” Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5000-70878. https://www.nrel.gov/docs/fy18osti/70878. 

Conference Paper

NREL/CP-5000-70878

August 2018
NOTICE

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Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering

OMAE 2018

June 17 –22, 2018, Madrid, Spain

OMAE2018-77589

VERIFICATION OF A NUMERICAL MODEL OF THE OFFSHORE WIND TURBINE

FROM THE ALPHA VENTUS WIND FARM WITHIN OC5 PHASE III

Wojciech Popko,

1 Matthias L. Huhn,1 Amy Robertson,2 Jason Jonkman,2 Fabian Wendt,2 Kolja M  ̈uller,3 Matthias Kretschmer,3

Fabian Vorpahl,

4 Torbjørn Ruud Hagen,5 Christos Galinos,6 Jean-Baptiste Le Dreff,7 Philippe Gilbert,8 Bertrand Auriac,9

Francisco Navarro V ́ıllora,

10 Paul Sch  ̈unemann,11 Ilmas Bayati,12 Marco Belloli,12 Sho Oh,13 Yoshitaka Totsuka,14

Jacob Qvist,

15 Erin Bachynski,16 Stian Høegh Sørum,16 Paul E. Thomassen,17 Hyunkyoung Shin,18

Felipe Vittori,

19 Josean Galvan ́ ,20 Climent Molins,21 Paul Bonnet,22 Tjeerd van der Zee,23 Roger Bergua,24

Kai Wang,

24 Pengcheng Fu,25 Jifeng Cai 25

1Fraunhofer Institute for Wind Energy Systems IWES, Division Wind Turbine and System Technology, 27572 Bremerhaven, Germany

wojciech.popko@iwes.fraunhofer.de

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

4Senvion, Osnabruck, Germany,  ̈5OWEC Tower AS, Oslo, Norway, 6Technical University of Denmark, Department of Wind Energy,

Roskilde, Denmark 7Electricite de France, Recherche et D  ́ eveloppement, Palaiseau, France,  ́

8

IFP Energies Nouvelles, Solaize, France,

9Principia, La Ciotat, France, 10Siemens Gamesa Renewable Energy, Hamburg, Germany, 11University of Rostock, Endowed Chair

of Wind Energy Technology, Rostock, Germany, 12Politecnico di Milano, Department of Mechanical Engineering, Milano, Italy, 13Nippon

Kaiji Kyokai, Tokyo, Japan, 14Wind Energy Institute of Tokyo Inc., Tokyo, Japan, 154Subsea, Hvalstad, Norway, 16Norwegian University

of Science and Technology, Department of Marine Technology, Trondheim, Norway, 17Simis AS, Trondheim, Norway, 18University of Ulsan,

School of Naval Architecture and Ocean Engineering, Ulsan, The Republic of Korea, 19National Renewable Energy Centre, Sarriguren,

Spain, 20TECNALIA, Derio, Spain, 21Polytechnic University of Catalonia, Barcelona, Spain, 22Siemens Industry Software,

Cornella de Llobregat, Spain, `

23Knowledge Centre WMC, Wieringerwerf, The Netherlands, 24Envision Energy Limited, Shanghai, China,

25China General Certification Center, Beijing, China

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 analyzes the Senvion 5M wind turbine supported by the

OWEC Quattropod from the alpha ventus offshore wind farm.

This paper shows results of the verification of the OWT models

(code-to-code comparison). A subsequent publication will focus

on their validation (comparison of simulated results to measured

physical system response data). Based on the available data, the

participants of Phase III set up numerical models of the OWT in

their simulation tools. It was necessary to verify and to tune these

models. The verification and tuning were performed against an

OWT model available at the University of Stuttgart – Stuttgart

Wind Energy (SWE) and documentation provided by Senvion and

OWEC Tower. A very good match was achieved between the results from the reference SWE model and models set up by OC5

Phase III participants.

INTRODUCTION

The Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project [1], 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

1

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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 dramatic

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 [2, 3]. 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/or in-situ data.

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

simulated results to the response data of physical systems. OC5

is 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 [4, 5], Phase III aims to use openocean data for the validation work. Such data are obtained from

alpha ventus—the first German offshore wind farm. 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.

OWT location

FIGURE 1: LOCATION OF THE OWT WITHIN THE ALPHA VENTUS WIND FARM—MODIFIED SKETCH FROM [6]

Senvion1from Germany supported Phase III with data necessary to set up the turbine and the tower models, and with all

1https://www.senvion.com/global/en/

necessary control settings that would allow running benchmark

exercises on validation of simulation tools. OWEC Tower2from

Norway provided the jacket support structure design data, including the transition piece (TP), foundation, and soil properties. The

OWT schematic is shown in Fig. 2.

Hub height at 

LAT +92 m

Jacket substructure

Seabed 

at LAT -28m

126 m rotor 

diameter

Nacelle

Hub

Tower top 

outputs

Tower bottom 

outputs

Pitch 

system 

outputs

Tower

Rotor 

speed 

output

LAT

TP

FIGURE 2: THE SENVION 5M TURBINE WITH THE JACKET

SUPPORT STRUCTURE FROM OWEC TOWER—MODIFIED

SKETCH FROM [7]

This paper shows results of the verification of the OWT

models (code-to-code comparison) prior to their validation (comparison of simulated results to measured physical system response data). Based on the available data, the OC5 Phase III participants set up numerical models of the Senvion 5M turbine with

the jacket support structure in their simulation tools. It should be

noted that the OC5 Phase III participants have access to limited

data of the OWT due to intellectual property (IP) reasons—the

full definition of the controller, and detailed structural and aerodynamic properties of the blades could not be disclosed. Therefore, it was necessary to:

• Adapt controller and blades from a generic turbine model of

the same power class that is available in the public domain,

• Build OWT models consisting of the OWEC Tower support structure, Senvion 5M turbine, and adapted generic controller and generic blades,

2https://www.owectower.no

2

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• Verify and tune these OWT models prior to their validation,

• Answer the question whether those simplified OWT

models—with adapted controller and blades—are sufficient

for validation of a complex offshore system.

The verification and tuning was performed against an OWT

model implemented in Flex5-Poseidon by the University of

Stuttgart—Stuttgart Wind Energy (SWE), and documentation

provided by Senvion and OWEC Tower. The OWT model

from SWE contains structural and aerodynamic properties of

the real blades, and the fully functional controller that could

not be disclosed to the OC5 Phase III participants. The SWE

model was extensively validated by Kaufer [8] and Muller [9]  ̈

within the Research at alpha ventus (RAVE) projects—OffshoreWindenergieanlagen (OWEA), and OWEA Loads [10], respectively. Therefore, it can be considered as a reference model for

the verification of other numerical models prior to their validation in Phase III.

A number of academic and industrial project partners from

11 countries participate 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

verification of OWT models in Phase III. It should be noted that

hydrodynamic capabilities are not listed herein, as the verification procedure was focused on the structural dynamics, aerodynamics, and the turbine controller.

DEFINITION OF OFFSHORE WIND TURBINE

A description of the numerical model of the OWT consisting

of the rotor-nacelle assembly (RNA), the tower, the TP, the jacket

substructure, its foundation piles, and soil properties was set up

at Fraunhofer IWES by Popko [11] based on the data provided by

Senvion and OWEC Tower. The document does not suggest one

modeling approach, as different simulation tools have different

levels of complexity and features. Phase III participants chose

how to interpret these data to best suit their simulation tools. The

OWT and its basic dimensions are shown in Fig. 2. Note that the

jacket piles are not visualized in this figure.

The complexity of the OWT model should be adequate for

the foreseen validation task. On the other hand, it should be relatively simple to minimize the implementation effort and modeling errors in various simulation tools. Furthermore, not all design data could be released to the project participants due to the

IP issues.

Controller

The full definition of the Senvion 5M turbine controller including the controller dynamic-link library (DLL), which is a

TABLE 1: PARTICIPANTS OF OC5 PHASE III AND THEIR

TOOLS

Organization full name Abbreviation Country Tool

4Subsea 4S Norway ASHES

National Renewable Energy Centre CENER Spain FAST V8

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

Electricite de France – Recherche et D  ́ eveloppement EDF R&D France  ́ FAST V8

Envision Energy Limited ENVISION China SAMCEF Wind

Turbines 17.1 (SWT)

IFP Energies Nouvelles IFPN France DeepLinesWind - V5R2

Fraunhofer Institute for Wind Energy Systems IWES

– Division Wind Turbine and System Technology

IWES Germany MoWiT

National Renewable Energy Laboratory NREL USA FAST V8

Norwegian University of Science and Technology

– Department of Marine Technology

NTNU-M Norway SIMA

OWEC Tower OWEC Norway –

Politecnico di Milano

– Department of Mechanical Engineering

POLIMI Italy FAST V8

Principia PR France DeepLinesWind - V5R2

Senvion Senvion Germany –

Siemens Gamesa Renewable Energy SGRE Germany SAMCEF Wind

Turbines 17.1 (SWT)

Simis AS SIMIS Norway ASHES

Siemens Industry Software SISW Spain SAMCEF Wind

Turbines 17.1 (SWT)

University of Stuttgart

– Stuttgart Wind Energy

SWE Germany Flex5-Poseidon,

Simpack

TECNALIA TECNALIA Spain FAST V8

University of Ulsan

– School of Naval Architecture and Ocean Engineering

UOU The Republic of

Korea

FAST V8

Polytechnic University of Catalonia UPC Spain FloaWDyn

University of Rostock

– Endowed Chair of Wind Energy Technology

URO Germany SiWEC

Wind Energy Institute of Tokyo Inc. WEIT Japan FAST V8

Windrad Engineering GmbH Windrad Germany SiWEC

Knowledge Centre WMC WMC The Netherlands FOCUS6 Offshore

standard input parameter to simulation tools, could not be disclosed to the OC5 Phase III participants due to IP issues. Therefore, Fraunhofer IWES decided to adapt the baseline NREL 5-

MW turbine controller for Phase III needs. Basic control parameters were tuned to match dynamic behavior of the reference OWT

model with the full Senvion 5M controller, which was available

for comparison at SWE. The tuning was focused on two operating regions: (1) the variable speed region for optimal power

tracking below the rated wind speed, and (2) the constant power

region above the rated wind speed. All parameters that were

tuned are listed in [11]. Alternatively, participants of Phase III

could utilize control strategies suggested in [11] to implement

their own controller.

Blades

Detailed structural and aerodynamic properties of the blades

(mass, stiffness, chord, thickness, twist distribution, airfoil polars, etc.) could not be disclosed to the participants of Phase

III. Therefore, it was required to tune the existing blades of the

3

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TABLE 2: OVERVIEW OF SIMULATION CAPABILITIES OF

TOOLS USED WITHIN PHASE III

Tool Structural

(elastic)

Aerodynamics

(aero)

Control

(servo)

ASHES FEM, Euler-Bernoulli BEM + Glauert correction + Prandtl tip and hub

loss, DS

DLL, UD

Bladed V4.8 MBS + flexible modally reduced bodies,

Timoshenko

BEM + Glauert correction + Prandtl tip and hub

loss + skew inflow correction

DLL, UD

DeepLinesWind

- V5R2

FEM, Mindlin BEM + Glauert correction + Prandtl tip and hub

loss + skew inflow correction, relaxation of

induction factors

DLL

FAST V8 Substructure: FEM + Craig-Bampton,

Timoshenko, Turbine: FEM preprocessor +

Modal/MBS, Euler-Bernoulli, Blades: FEM,

Timoshenko

BEM + Glauert correction + Prandtl tip and root

losses + Pitt and Peters skewed wake

DLL, UD

Flex5-Poseidon FEM + Modal BEM or GDW + DS DLL, UD

FloaWDyn MBS/FEM, Euler-Bernoulli (Aerodyn module) BEM + Glauert correction +

Prandtl tip and root losses

DLL, UD

FOCUS6

Offshore

FEM, Timoshenko BEM + Wilson and Lissaman correction + Prandtl

tip and root losses

DLL, UD

HAWC2 MBS/FEM/stiffness-proportional Rayleigh

damping, Timoshenko

BEM with ‘Madsen and Larsen’ correction for

shear and dynamic inflow, Glauert and Coleman

modification for skewed inflow and dynamic stall

DLL

NK-UTWind FEM, Euler-Bernoulli BEM (AeroDyn14) DLL

MoWiT MBS/FEM/modal reduced bodies,

Euler-Bernoulli

BEM + Glauert correction + Prandtl tip and hub

loss + skew inflow correction, relaxation of

induction factors

DLL

SAMCEF

Wind Turbines

17.1 (SWT)

FEM/MBS, Timoshenko BEM + DS + Glauert correction + Prandtl tip and

hub loss + dynamic wake + skew inflow +

relaxation of induction factors

DLL, UD

SIMA FEM, Euler-Bernoulli with shear correction BEM + Glauert correction + Prandtl tip loss + skew

inflow correction + Øye correction for dynamic

stall and inflow

DLL, UD

Simpack MBS, modal reduced FEM, Timoshenko BEM + Glauert correction + Prandtl tip and root

losses + Pitt and Peters skewed wake

DLL

SiWEC MBS/FEM, modal reduced, Euler-Bernoulli BEM + Glauert correction + Prandtl tip loss + skew

inflow correction + Øye correction for dynamic

stall

DLL, UD

BEM – blade element momentum

DLL – dynamic-link library

DS – dynamic stall implementation

FEM – finite element method

GDW – generalized dynamic wake

MBS – multibody-dynamics formulation

Modal – modal reduced system

UD – user-defined subroutine

NREL 5-MW offshore baseline turbine [12] to obtain a comparable structural response to the real blades that are used for

the Senvion 5M turbine. Blade mass and stiffness distributions

were tuned to achieve the first two flap- and edgewise eigenfrequencies of the real blade that were provided to the OC5 Phase

III participants by Senvion. The remaining structural and aerodynamic properties were taken from the NREL 5-MW turbine

definition [12]. Such an approach can be justified because: (1)

the gross properties of the NREL 5-MW model were established

based on the Senvion 5M turbine, and (2) the Senvion 5M turbine

utilizes blades that were manufactured by LM Wind Power—

the company that helped to establish the structural properties of

the blades used in the Dutch Offshore Wind Energy Converter

Project [13], which later on were adapted for the NREL 5-MW

turbine. Therefore, the basic properties such as the length, mass,

center of gravity, and damping ratio of the NREL 5-MW turbine

blades and the blades from LM Wind Power used for the Senvion

5M turbine, are similar.

It must be emphasized that due to the differences in blade

aerodynamics between the tuned NREL 5-MW blades (utilized

in OC5 Phase III) and Senvion 5M reference turbine model

(available for comparison at SWE), it was not possible to obtain a comparable system response at all operating regions of

the turbine. Therefore, verification and validation could only be

performed at certain ranges of operating wind speeds, as further

described in the results section of this paper. It should be noted

that tuning of the NREL blade aerodynamic properties in order to

achieve a similar response to the real blade in all operating conditions would be very time consuming, and therefore not doable

within the time frame of Phase III.

Jacket substructure

The following assumptions were made in modeling the

jacket substructure:

• At joints, the connecting nodes of elements were defined

at the intersection points of the members centerlines. This

leads to overlap of elements in the analyzed jacket substructure.

• The local joint flexibility (LJF) was not modeled in the

jacket substructure. Beam elements were simply clamped at

the intersection of the centerlines of a joint’s tubular members. Different tools have different modeling capabilities for

the LJF, which would lead to additional uncertainties in the

results interpretation. More detailed description on the current state of practice for modeling joints in space frame support structures can be found in [14].

• The TP was modeled with massless beam elements. Its mass

was accounted for by three point masses. Note that the

real TP is a complex structure that consists of many noncylindrical elements. Therefore, modeling of its exact geometry would not even be possible in many of the simulation

tools.

• The secondary steel masses of the jacket substructure, such

as pile stoppers, cathodic protection, J-tubes, cables, crane,

and boat landing platform were modeled as point masses applied at certain structural nodes and as redistributed masses

by increasing the material density of certain jacket members.

• Impact of corrosion in terms of the reduced material stiffness or material thickness is not accounted for. The model

will subsequently be validated against the measurements

that were recorded around 1 year after deployment of the

structure. During that time period, there was no significant

development of corrosion.

DEFINITION OF LOAD CASES

Four groups of load cases (LCs) of increasing complexity

were defined for stepwise comparison of results and tracing back

potential errors from the implementation of OWT models in different simulation tools. Table 3 lists all verification LCs that were

simulated in Phase III. A detailed description of these LCs can be

4

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found in [15]. A similar stepwise approach for model verification

was already adapted in the OC3 and OC4 projects [16, 17].

TABLE 3: OVERVIEW OF VERIFICATION LOAD CASES

Load

Case

Enabled DOFs Wind Conditions Simulation Conditions

1a Flexible RNA and tower, locked rotor, 6

DOFs constrained at tower bottom

No air θ = 0 deg, φ = 0 deg –

first blade points upwards

1b Flexible RNA, tower, TP and jacket

substructure, locked rotor, 6 DOFs

constrained at LAT -29.5 m at 4 jacket legs

No air as in LC 1a

1c Flexible RNA, tower, TP, jacket

substructure and foundation piles, locked

rotor, elastic foundation model, 6 DOFs

constrained at the bottom of 4 foundation

piles

No air as in LC 1a

2a Flexible RNA and tower, 6 DOFs

constrained at tower bottom

No air Eigenanalysis, θ = 0 deg,

φ = 0 deg – first blade

points upwards

2b Flexible RNA, tower, TP and jacket

substructure, 6 DOFs constrained at LAT

-29.5 m at 4 jacket legs

No air as in LC 2a

2c Flexible RNA, tower, TP, jacket

substructure and foundation piles,

elastic foundation model

No air as in LC 2a

2d Flexible jacket substructure and TP,

rigid tower and RNA, 6 DOFs constrained

at LAT -29.5 m at 4 jacket legs

No air as in LC 2a

3.1 Rigid RNA and tower, 6 DOFs constrained

at tower bottom, 1 rotational DOF for rotor,

1 rotational DOF for pitch mechanism

Steady, deterministic wind, changing from

Vcut-in = 3 m/s to Vcut-out = 30 m/s with

step of 1 m/s lasting for 50 s, no wind shear

Power production,

periodic time series

solution, T = 1400 s

3.2a

seed 1–6

as in LC 3.1 Stochastic wind, 6 seeds, Vhub = 7 m/s,

σu = 1.17 m/s, σv = 0.94 m/s,

σw = 0.58 m/s, no wind shear

Power production,

periodic time series

solution, T = 600 s for

each wind seed

3.2b

seed 1–6

as in LC 3.1 Stochastic wind, 6 seeds, Vhub = 13 m/s,

σu = 1.83 m/s, σv = 1.46 m/s,

σw = 0.92 m/s, no wind shear

as in LC 3.2a

3.2c

seed 1–6

as in LC 3.1 Stochastic wind, 6 seeds, Vhub = 18 m/s,

σu = 2.44 m/s, σv = 1.96 m/s,

σw = 1.22 m/s, no wind shear

as in LC 3.2a

3.2d

seed 1–6

as in LC 3.1 Stochastic wind, 6 seeds, Vhub = 16 m/s,

σu = 2.11 m/s, σv = 1.69 m/s,

σw = 1.06 m/s, no wind shear

as in LC 3.2a

4.1 Flexible RNA and tower, 6 DOFs

constrained at tower bottom

Steady, deterministic wind, changing from

Vcut-in = 3 m/s to Vcut-out = 30 m/s with

step of 1 m/s lasting for 50 s, no wind shear

Power production,

periodic time series

solution, T = 1400 s

4.2c

seed 1–6

as in LC 4.1 Stochastic wind, 6 seeds, Vhub = 18 m/s,

σu = 2.44 m/s, σv = 1.96 m/s,

σw = 1.22 m/s, no wind shear

Power production,

periodic time series

solution, T = 600 s for

each wind seed

4.2d

seed 1–6

as in LC 4.1 Stochastic wind, 6 seeds, Vhub = 16 m/s,

σu = 2.11 m/s, σv = 1.69 m/s,

σw = 1.06 m/s, no wind shear

as in LC 4.2c

Vhub – mean wind speed at the hub height

Vcut-in – cut-in wind speed

Vcut-out – cut-out wind speed

θ – blade pitch angle

φ – rotor azimuth angle

LAT – lowest astronomical tide

T – simulation time

σu – standard deviation of longitudinal wind component

σv – standard deviation of lateral wind component

σw – standard deviation of vertical wind component

In LC group 1, mass, resulting vertical force, and resulting

fore-aft and side-to-side overturning moments were examined at

the tower bottom (LC 1a), at four jacket legs around the seabed

(LC 1b), and at the bottom of four foundation piles (LC 1c).

In LC group 2, modal properties were examined for the coupled system consisting of the RNA and the support structure with

different boundary conditions. In LC 2a, the RNA and tower

were modeled as flexible and 6 degrees of freedom (DOFs) were

constrained at the tower bottom. In LC 2b, the RNA, tower, TP,

and jacket substructure were modeled as flexible and 6 DOFs

were constrained at four jacket legs around the seabed. In LC

2c, the entire OWT including its piles was modeled as flexible

and participants could model foundation stiffness by the apparent fixity method or alternatively by applying p-y curves along

the foundation piles. In LC 2d, the TP and jacket substructure

were set as flexible with four jacket legs constrained around the

seabed, whereas the tower and the RNA were rigid.

LC groups 3 and 4 were meant for verification of the turbine

controller and aerodynamic forces. In the former group, the RNA

and tower are modeled as rigid, whereas in the latter as flexible.

In LCs 3.1 and 4.1, the stepped deterministic wind is applied to

investigate transient response of the system at all operating wind

speeds from Vcut-in of 3 m/s to Vcut-out of 30 m/s. In LCs 3.2a,b,c,

and d and LCs 4.2c and d turbine response and controller performance were analyzed with turbulent wind at different operating

wind speeds, below rated, around rated, and above rated, respectively.

In all verification LCs the marine environment was disregarded. This means that such features as waves, tides, currents,

buoyancy force, marine growth, and flooded elements were not

accounted for in the analysis. However, such features will be

included in the subsequent validation part of OC5 Phase III.

Turbulent wind fields using a Kaimal spectrum were generated at NREL for LC groups 3.2 and 4.2 based on the specification from [15]. It was decided to use six independent wind seeds,

each 10 minutes long, for every single LC in order to get statistically comparable results as recommended in the IEC 61400-1

standard [18]. 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 (i.e.,

Flex5 utilizes a polar grid).

Different components of the OWT were modeled as flexible

or rigid depending on the LC type. Environmental loads were

applied depending on the definition of the given LC. For each

LC, the outputs were recorded at a number of nodal points denoted as sensors located at the RNA and the tower, as shown in

Fig. 2. The location of outputs was chosen to capture the global

response of the OWT.

Start-up transients were removed by using a presimulation

time, Tpre, which is simulated but cut-out from the result files in

all simulations. 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.

DATA POSTPROCESSING AND ANALYSIS METHODS

All simulation results were delivered by the project participants in terms of time series data. Their post processing was performed internally at Fraunhofer IWES in Matlab and MCrunch

[19].

LCs with deterministic wind were visually compared in

5

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
terms of their time series. The results accuracy was checked by

the nondimensional root mean square error (RMSE). The RMSE

can be used as a measure of the difference between time series

data points, Xt, predicted by every single OC5 Phase III model

and the time series data points, XSWE,t, obtained from the reference SWE model. These individual differences, at each time

step, t, are aggregated by the RMSE into a single value. The

RMSE is defined as the square root of the mean squared error divided over the number of the data points, n, in the analyzed time

series:

RMSE =

r

∑

n

t=1

(Xt −XSWE,t)

2

n

(1)

It has to be noted that the RMSE:

• Is not the ‘ultimate measure’ for assessment of accuracy.

The time series results that are phase shifted from the SWE

reference time series get larger errors compared to those that

are in phase. Therefore, the RMSE should not be interpreted

in separation from the time series plots.

• Gives a relatively high weight to large errors as they are

squared before they are averaged. Therefore, higher discrepancies between the reference SWE model and the OC5

Phase III models are penalized more by this statistical measure.

A nondimensional form of the RMSE is obtained by normalizing the RMSE to the range of the reference data set XSWE,max −

XSWE,min. The NRMSE is expressed as a percentage, where

lower values indicate less residual variance and therefore a better

fit:

NRMSE =

RMSE

XSWE,max −XSWE,min

100% (2)

LCs with turbulent wind were examined in terms of probability density functions (PDFs) and power spectral densities

(PSDs). PSDs and PDFs are computed from six aggregated time

series, each 10 minutes long.

SELECTED RESULTS

This section presents example results of the OWT models

verification against the reference model consisting of the Senvion

5M wind turbine and the jacket support structure from OWEC

Tower, which was implemented in the Flex5-Poseidon at SWE.

Information about Flex5-Poseidon can be found in [20]. This encrypted model contains a detailed description of the entire OWT

including structural and aerodynamic properties of the blades designed by LM Wind Power and the fully functional controller

from Senvion. It should be noted that SWE also participates in

Phase III with another simulation tool—Simpack. The Simpack

OWT model is based only on the OC5 Phase III specification

[11] and should not be confused with the reference OWT model

implemented in Flex5-Poseidon.

Presented results give a general overview of differences between the reference SWE model and the OC5 Phase III models.

The results discussed in this paper represent the final outcome of

multiple modelling iterations that were necessary to develop numerical models of the OWT, which are adequate for further validation needs. During each modelling iteration, the participants

updated their OWT models to better match with the solutions

from the reference SWE model and documentation provided by

Senvion and OWEC Tower.

Comparison of Mass and Overturning Moments

Verification of different implementations of the OWT model

against the reference SWE model is performed by comparing

masses and fore-aft overturning moments at the tower base or at

the bottom of foundation piles, respectively. Mass and moments

would give a rough idea whether the structural properties of the

turbine are correctly implemented. Mass distribution would have

a direct impact on structural dynamics. High discrepancies in the

overturning moment would indicate issues with center of gravity

(CG) of different system components and/or incorrect stiffness

distribution.

-0.8%

-0.7%

-0.6%

-0.6%

-0.5%

-0.5%

-0.5%-0.5%

-0.5%

-0.4%-0.4%

-0.4%

-0.4%

-0.4%

-0.4%

-0.4%

-0.4%

-0.3%

0.0%

0.0%

2.8%

615

620

625

630

635

640

645

650

655

Mass [t]

FIGURE 3: LC 1A – MASS OF THE RNA AND TOWER

The example results for LCs 1a and 1c are shown in Figs. 3–

5. Reference values of the SWE model are denoted with black

bars. The results of the Phase III participants are marked with

blue bars and sorted in increasing order from left to right. The

percentage change with respect to the SWE (Flex5) value is

added at the top of each bar. A missing bar indicates that the

results were not provided by the given participant for this particular LC. A very good match of the RNA and tower mass is observed for all project participants in Fig. 3. A small discrepancy

is visible in the case of WEIT (FAST V8), as this participant did

6

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
not use the most recent specification of the tower model which

included the updated value of the material density.

Also, a very good match between the majority of the models

and the reference SWE model is observed in the case of the foreaft overturning moment, My, at the tower bottom in Fig. 4. Larger

discrepancies might indicate misplaced CG of some of the RNA

components. The increase of My would indicate that the CG of

the RNA is shifted upwind from the central vertical axis of the

tower. The reduced My would indicate the opposite—CG shifted

downwind toward the tower center.

8.7%

2.9%

2.3%

1.5%

1.0%

0.9%

0.7%

0.5%

0.1%

0.0%

-0.4%

-0.4%

-0.6%

-1.4%

-1.6%

-1.8%

-2.8%

-2.8%

-3.4%

-3.7%

-16.3%

-5350

-5150

-4950

-4750

-4550

-4350

-4150

-3950

-3750

My - fore-aft overturning moment [kNm]

FIGURE 4: LC 1A – FORE-AFT OVERTURNING MOMENT AT

THE TOWER BOTTOM

Apparent fixity method

– OWT fixed at 8.5 m below the seabed

Full pile length – piles 

extending to 42 m 

below the seabed 

-0.3%

0.0%

4.0%

4.3%

4.7%

6.2%

6.3%

6.4%

6.4%

6.5%

6.5%

6.5%

6.6%

31.2%

31.6%

31.6%

31.7%

1100

1200

1300

1400

1500

1600

Mass [t]

FIGURE 5: LC 1C – MASS OF THE ENTIRE OWT INCLUDING

RNA, TOWER, TP, JACKET SUBSTRUCTURE AND FOUNDATION PILES

Figure 5 shows the mass of the RNA, tower, TP, jacket substructure, and foundation piles. The majority of models are heavier, around 3 to 6.5% compared to the reference SWE model.

The reason is that the reference SWE model does not include

secondary steel masses for the jacket substructure. Those masses

were included in models setup within OC5 Phase III. UPC, ENVISION, ClassNK, and NTNU-M models are around 31% heavier than the reference SWE model. The difference comes from

the modeling of the foundation piles. The majority of participants followed the modeling approach of SWE and used an apparent fixity model by identifying a shortened (fictitious) pile

that extends from the seabed to a clamped point 8.5 m below the

seabed that mimics the flexibility of the real pile penetrating the

soil. The remaining four participants modeled the full length of

the real pile extending to 42 m below the seabed and used p-y

curves provided by OWEC Tower for pile-soil interaction.

Summing up, a very good match between the reference SWE

model and the OC5 Phase III models was achieved in terms of

mass and overturning moments.

Eigenanalysis

Four LCs were defined for eigenmode analysis in Tab. 3.

Some results for LC 2b are shown in Fig. 6. The eigenanalysis

was performed for the flexible RNA, tower, TP, and the jacket

substructure with 4 legs constrained at LAT -29.5 m.

The results for the first global fore-aft mode average around

2% lower compared to the reference SWE model, as shown in

Fig. 6a. The secondary steel masses, which are included in the

OC5 Phase III models, might be responsible for this general

trend. Note that the reference SWE model is slightly lighter, as

it does not include secondary steel masses in the jacket substructure (see Fig. 5).

Figures 6b–6f show diverse coupled modes, which are dominated by flap- and edgewise deflections of the blades. The majority of the participants matched quite well with the SWE reference

model. The results are very good considering the fact that the

definition of the real blade was not available for the OC5 Phase

III project. They prove that OWT models with tuned NREL 5-

MW blades can mimic the behavior of the reference SWE model

that contains the structural definition of the real blades. Some

discrepancies are always expected, as different simulation tools

incorporate a different number of DOFs and different ways of

modeling the structure. Furthermore, for higher modes, it is

challenging to identify which coupled mode is actually induced,

purely based on the modes visualization. Figure 7 shows visualizations of the first fore-aft global mode from four arbitrarily

chosen tools.

Deterministic Load Cases with Stepped Wind

To investigate the transient response of the system at all operating wind speeds, a stepped wind was increased from Vcut-in to

Vcut-out with a step of 1 m/s, lasting for 50 s. Figure 8 shows the

rotor speed for LC 3.1 where only the rotor rotational DOF and

the pitch system rotational DOF were enabled (see Tab. 3). A rel7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
-3.8%

-3.1%

-2.9%

-2.2%

-2.1%

-2.1%

-1.9%-1.9%

-1.6%

-1.5%

-0.6%

-0.2%

0.0%

0.3%

0.295

0.300

0.305

0.310

0.315

First fore-aft [Hz]

(a) first global fore-aft

-7.3%

-2.5%

-2.1%

-1.8%

-1.3%

-0.9%

-0.4%

-0.2%

0.0%

0.1%

0.2%

0.58

0.60

0.62

0.64

0.66

0.68

0.70

First asymmetric flapwise tilt [Hz]

(b) first asymmetric flapwise tilt

-7.4%

-6.4%

-2.5%

-2.1%

-1.8%

-1.2%

-0.5%

-0.4%

0.0%

0.0%

0.5%

0.7%0.7%

5.4%

0.58

0.60

0.62

0.64

0.66

0.68

0.70

First flapwise collective [Hz]

(c) first flapwise collective

-1.9%

-1.7%

-1.3%

-0.9%

-0.8%

-0.6%

-0.1%

0.0%

0.2%

0.3%

0.4%

1.13

1.14

1.15

1.16

1.17

First asymmetric edgewise yaw [Hz]

(d) first asymmetric edgewise yaw

-9.6%

-7.0%

-1.4%

-1.1%

-0.8%

0.0%

1.2%

2.1%

2.4%

2.4%

2.6%

2.9%

3.4%

4.7%

1.45

1.50

1.55

1.60

1.65

1.70

1.75

Second flapwise collective [Hz]

(e) second flapwise collective

-27.3%

-16.1%

-7.5%

-7.2%

-6.5%

-4.8%

-4.8%

-1.7%

-0.2%

0.0%

2.9%

2.50

2.70

2.90

3.10

3.30

3.50

3.70

3.90

Second edgewise collective [Hz]

(f) second edgewise collective

FIGURE 6: LC 2B – SELECTED EIGENFREQUENCIES NAMED

ACCORDING TO THE DOMINANT MODES

atively good match of the rotor speed is achieved in the partialloading region for wind speeds between 6 and 8 m/s, (time between 150 and 250 s, in Fig. 8) and in the full-loading region

between 15 and 17 m/s (time between 600 and 750 s in Fig. 8).

In the transition region from around 8 to 13 m/s (time between

250 and 550 s in Fig. 8), there are discrepancies due to unavoidable differences in the blade aerodynamics and controller. In this

region, the majority of the models reach larger rotational speeds

than the reference SWE model, which is denoted with a black,

solid curve. Tuning of the NREL blade aerodynamic properties to achieve similar response to the real blade in all operatFIGURE 7: LC 2B – FIRST GLOBAL FORE-AFT MODE, FROM

LEFT: SWE (SIMPACK) , ENVISION (SWT), 4S (ASHES), NTNUM (SIMA)

ing conditions would be very time consuming, and therefore not

doable within the time frame of Phase III. Therefore, the transition operating region will not be used for the intended validation

task. Rotor speed oscillations with a time period of 16.1 s are

visible in the full-loading region (from around 500 s onward) in

the case of CENER, WEIT (both FAST V8), and WMC (Focus6

Offshore). Those participants did not use the most recent version of the pitch proportional-integral-derivative (PID) controller

provided by Fraunhofer IWES. POLIMI (FAST V8) reached the

rotor rated speed already at the wind speed of 9 m/s. POLIMI

decided to adapt the Basic DTU Wind Energy Controller [21]

for its own simulations. This controller requires further tuning

to match the reference SWE results. The results of the remaining participants match well with the reference SWE results—a

sudden increase of the rotor speed caused by a rapid wind speed

change is smoothly mitigated by the PID pitch controller.

Time [s]

Rotor speed [rpm]

Wind speed [m/s]

3-4 5-6 7-8 9-10 11-12 13-14 15-16

Rotor speed 

oscillation above the 

rated wind speed –

participants who used 

PI pitch controller

SWE results and 

those participants 

who used the most 

recent PID pitch 

controller 

4Subsea - ASHES

CENER - FAST V8

CGC - Bladed V4.8

ClassNK - NK-UTWind

DTU - HAWC2

EDF-RD - FAST V8

ENVISION - SWT

IFPEN-PR - DeepLWind

IWES - MoWiT

NREL - FAST V8

NTNU-M - SIMA

POLIMI - FAST V8

SGRE - SWT

SWE - Flex5

SWE - Simpack

TECNALIA - FAST V8

UOU - FAST V8

UPC - FloaWDyn

URO-Windrad - SiWEC

WEIT - FAST V8

WMC - FOCUS6 Offshore

FIGURE 8: LC 3.1 – ROTOR SPEED CHANGING DUE TO DETERMINISTIC STEPPED WIND

Figure 9 shows the fore-aft shear force, Fx, at the tower top

8

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for LC 3.1 (see Tab. 3). The time series is trimmed at the fullloading region for the wind speed of 16 m/s. The force peak at

650 s is caused by the rapid change in the wind speed from 15

to 16 m/s at this particular time point. The majority of the tools

match quite well with the maximum peak value from the reference SWE results. Also, their transient decays are comparable

to the SWE (Flex5) results. This proves that the tuned pitch PID

controller is performing well. Oscillations at 3P frequency due to

the tower shadow are visible in the majority of the results. Their

frequency and amplitude match the reference SWE results very

well. ClassNK (NK-UTWind), UOU (FAST V8), and TECNALIA (FAST V8) do not include the tower shadow in their simulations. Note that the SGRE (SWT) results are not visible, as they

were around two times larger compared to other codes, though

the oscillation pattern seemed to match the other simulation tools

well.

Time [s]

Fx [kN]

4Subsea - ASHES

CENER - FAST V8

CGC - Bladed V4.8

ClassNK - NK-UTWind

DTU - HAWC2

EDF-RD - FAST V8

ENVISION - SWT

IFPEN-PR - DeepLWind

IWES - MoWiT

NREL - FAST V8

NTNU-M - SIMA

POLIMI - FAST V8

SGRE - SWT

SWE - Flex5

SWE - Simpack

TECNALIA - FAST V8

UOU - FAST V8

UPC - FloaWDyn

URO-Windrad - SiWEC

WEIT - FAST V8

WMC - FOCUS6 Offshore

NRMSE [%]

FIGURE 9: LC 3.1 – TOWER TOP FORE-AFT SHEAR FORCES

AND NRMSE – DETERMINISTIC WIND SPEED OF 16 M/S

The NRMSE is shown below the time series in Fig. 9. The

values are sorted left to right in ascending order—from the best

fit to the worse. The NRMSE varies between 6 and 15% for

the majority of the participants, which is considered to be a very

good result.

Figures 10 and 11 show the tower top fore-aft displacement,

x, in the partial- and the full-loading region, respectively, for LC

4.1 (see Tab. 3). In LC 4.1, the RNA and tower are flexible.

Likewise, in LC 3.1, the stepped wind is applied from Vcut-in to

Vcut-out. Mean values of the displacement match quite well with

the reference SWE model. Tower fore-aft displacement oscillaTime [s] x [m] NRMSE [%]

4Subsea - ASHES

CENER - FAST V8

CGC - Bladed V4.8

ClassNK - NK-UTWind

DTU - HAWC2

EDF-RD - FAST V8

ENVISION - SWT

IFPEN-PR - DeepLWind

IWES - MoWiT

NREL - FAST V8

NTNU-M - SIMA

POLIMI - FAST V8

SGRE - SWT

SWE - Flex5

SWE - Simpack

TECNALIA - FAST V8

UOU - FAST V8

UPC - FloaWDyn

URO-Windrad - SiWEC

WEIT - FAST V8

FIGURE 10: LC 4.1 – TOWER TOP FORE-AFT DISPLACEMENTS AND NRMSE – DETERMINISTIC WIND SPEEDS OF

6 AND 7 M/S

tions with a time period of around 2.7 s (0.375 Hz) corresponding to the first fore-aft global mode are visible in all the results.

Note that the results of WEIT (FAST V8) are shifted in time—

the stepped wind speed was applied with a delay of 50 s with

respect to other results. Note that POLIMI (FAST V8) results

are not depicted in Fig.11 due to the severe oscillations caused

by the controller. The NRMSE values are shown in ascending

order below the time series in Figs. 10 and 11. The NRMSE

varies between 6 and 15% for the majority of the participants,

which is considered to be a very good match.

Stochastic Load Cases at Selected Operating Points

Stochastic LCs are analyzed in terms of PSD and PDF plots,

which were created from an aggregated time series of 6 independent seeds for each data set.

Figure 12 shows the PSD and PDF of the pitch angle for

LC 3.2d (see Tab. 3). Probability distributions of the majority

of the OWT models set up within OC5 Phase III match well

with the reference SWE model—denoted with the black, solid

curve. This proves that the tuned controller performs well. Subsequent rotor harmonics, such as 3P, 6P and 9P are visible in

PSD plots for the majority of the results. Some discrepancies

are visible in PSD plots of DTU (HAWC2) and UPC (FloaWDyn) results. Even though DTU used the most recent controller,

there is a discrepancy in their results. The energy spectrum of the

DTU results is relatively flat from around 2 Hz—probably due to

the provided controller parameters that are not optimized for the

9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
Time [s]

x [m]

NRMSE [%]

4Subsea - ASHES

CENER - FAST V8

CGC - Bladed V4.8

ClassNK - NK-UTWind

DTU - HAWC2

EDF-RD - FAST V8

ENVISION - SWT

IFPEN-PR - DeepLWind

IWES - MoWiT

NREL - FAST V8

NTNU-M - SIMA

POLIMI - FAST V8

SGRE - SWT

SWE - Flex5

SWE - Simpack

TECNALIA - FAST V8

UOU - FAST V8

UPC - FloaWDyn

URO-Windrad - SiWEC

WEIT - FAST V8

FIGURE 11: LC 4.1 – TOWER TOP FORE-AFT DISPLACEMENTS AND NRMSE – DETERMINISTIC WIND SPEEDS OF

17 AND 18 M/S

structural model in HAWC2.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Frequency [Hz]

10 -6

10 -4

10 -2

10 0

10 2

Pitch angle [db/Hz]

Pitch angle [deg]

PDF [-]

0.6 Hz – 3P frequency

1.2 Hz – 6P frequency

1.8 Hz – 9P 

frequency

CGC - Bladed V4.8

ClassNK - Nk-UTWind

DTU - HAWC2

EDF-RD - FAST V8

ENVISION - SWT

IFPEN-PR - DeepLWind

IWES - MoWiT

NREL - FAST V8

NTNU-M - SIMA

POLIMI - FAST V8

SWE - Simpack

SWE - Flex5

UOU - FAST V8

UPC - FloaWDyn

URO-Windrad - SiWEC

FIGURE 12: LC 3.2D – PSD AND PDF OF PITCH ANGLE FOR

TURBULENT WIND WITH A MEAN WIND SPEED OF 16 M/S

Figure 13 shows the PSD and PDF plots of the fore-aft

overturning moment, My, at the tower bottom for LC 4.2d (see

Tab. 3). Probability distributions of the majority of tools match

relatively well with the reference SWE model. Some discrepancies are visible in the case of POLIMI (FAST V8) results, where

probability distributions are much flatter compared to other results. The reason is that POLIMI did not use the provided DLL

controller. In the PSD results, energy peaks are visible at the

frequencies corresponding to the first fore-aft global mode, and

flap- and edgewise modes. Higher frequency peaks at around

3.4 Hz, 3.7 Hz, and 4.4 Hz are visible in the SWE (Flex5) results

and in the results of some participants. Those peaks correspond

to two different second blade edgewise modes and the tower second fore-aft mode, respectively. Note that the second edgewise

mode is not reproduced by FAST V8 models that do not use the

BeamDyn nonlinear FEM-based blade structural-dynamics module.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Frequency [Hz]

10 2

10 4

10 6

10 8

10 10

My [db/Hz]

My [kNm]

PDF [-]

ca. 0.375 Hz – first fore-aft 

global mode

ca. 0.62 Hz – first flapwise 

asymmetric modes / 3P freq.

ca. 1.20 Hz – first edgewise 

asymmetric modes / 6P freq.

ca. 1.78 Hz – second 

flapwise asymmetric modes

4Subsea - ASHES

CGC - Bladed V4.8

ClassNK - NK-UTWind

DTU - HAWC2

EDF-RD - FAST V8

ENVISION - SWT

IFPEN-PR - DeepLWind

IWES - MoWiT

NREL - FAST V8

NTNU-M - SIMA

POLIMI - FAST V8

SWE - Simpack

SWE - Flex5

UOU - FAST V8

UPC - FloaWDyn

URO-Windrad - SiWEC

FIGURE 13: LC 4.2D – PSD AND PDF OF FORE-AFT BENDING

MOMENT AT TOWER BOTTOM FOR TURBULENT WIND WITH

A MEAN WIND SPEED OF 16 M/S

CONCLUSIONS

The verification process was quite challenging as the real

blade design data and the full controller were not available to

the OC5 Phase III participants due to confidentiality. It was also

not the aim to do a detailed re-engineering of the blade structural and aerodynamic properties, and design of the state-of-theart controller that would be able to compete with the proprietary

Senvion 5M controller. Tuning was done on a reasonable level

considering the selected loading scenarios that were used for the

comparison. The subsequent validation task will be mostly focused on the support structure. Therefore, the most important

task was to obtain adequate system responses, such as displacements and forces for the support structure components.

Based on the provided data, OC5 Phase III participants set

up their models and verified them against the reference SWE

model set up in Flex5-Poseidon, which included a definition of

the real blade and the fully functional Senvion 5M controller.

10 This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
Due to the differences in blade aerodynamics between the

tuned NREL 5-MW blades (utilized in OC5 Phase III) and Senvion 5M reference turbine model in Flex5-Poseidon (available

for comparison at SWE), it was not possible to obtain a comparable system response at all operating regions of the turbine. Nevertheless, a satisfactory response could be achieved in the partialand full-loading regions for wind speeds between 6 and 8 m/s,

and 15 and 17 m/s, respectively. Therefore, verification and

validation can be performed at these ranges of operating wind

speeds. In those operating regions, the relatively low NRMSE

values in the range between 6 and 15% were achieved for the

analyzed outputs between the OC5 Phase III models and the reference SWE model for the majority of the participants. It has to

be noted that the NRMSE gives a relatively high weight to large

errors—other statistical measures might provide an even better

fit.

The goal is to now move forward with validation against

the measurements from the alpha ventus wind farm, using the

models developed.

ACKNOWLEDGEMENTS

OWEC Tower is acknowledged for releasing the jacket support structure design data for Phase III. Friedemann Borisade

from University of Stuttgart, SWE is acknowledged for engaging

the SWE team in the verification process with the reference OWT

model in Flex5-Poseidon. Dr. Fanzhong Meng from Fraunhofer

IWES is acknowledged for his help in tuning of the control parameters of the NREL 5-MW controller.

The Alliance for Sustainable Energy, LLC (Alliance) is the

manager and operator of NREL. NREL is a national laboratory of

the U.S. Department of Energy, Office of Energy Efficiency and

Renewable Energy. This work was authored by the Alliance and

supported by the U.S. Department of Energy under Contract No.

DE-AC36-08GO28308. Funding was provided by the Wind and

Water Power Program. The views expressed in the article do not

necessarily represent the views of the U.S. Department of Energy

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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