A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Development and Verification of a 

Fully Coupled Simulator for 

Offshore Wind Turbines 

Preprint 

J. M. Jonkman and M. L. Buhl, Jr. 

To be presented at the 45th AIAA Aerospace Sciences Meeting 

and Exhibit, Wind Energy Symposium 

Reno, Nevada 

January 8–11, 2007 

Conference Paper 

NREL/CP-500-40979 

January 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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Development and Verification of a Fully Coupled Simulator 

for Offshore Wind Turbines*

Jason M. Jonkman† and Marshall L. Buhl Jr.‡

National Renewable Energy Laboratory (NREL), Golden, Colorado, 80401-3393 

The vast deepwater wind resource represents a potential to use floating offshore wind 

turbines to power much of the world with renewable energy. Comprehensive simulation 

tools that account for the coupled excitation and response of the complete system, including 

the influences of wind-inflow, aerodynamics, structural dynamics, controls, and, for offshore 

systems, waves, currents, and hydrodynamics, are used to design and analyze wind turbines. 

Continuing our work presented previously, we outline the development of such an analysis 

tool for floating offshore wind turbines, including a recently added, quasi-static mooring 

system module. The fully coupled simulator was developed with enough sophistication to 

address the limitations of previous frequency and time domain studies and to have the 

features required to perform an integrated loads analysis. It is also universal enough to 

analyze a variety of wind turbine, support platform, and mooring system configurations. 

The simulation capability was tested by model-to-model comparisons to ensure its 

correctness. The results of all of the verification exercises are favorable and give us 

confidence to pursue more thorough investigations into the behavior of floating offshore 

wind turbines. Some of the potential challenges to their design are highlighted through 

sample response simulations. 

Nomenclature 

Aij = (i,j) component of the hydrodynamic added mass matrix 

Bij = (i,j) component of the hydrodynamic damping matrix 

CB = coefficient of the static friction drag between the seabed and a mooring line 

Hydrostatic Cij = (i,j) component of the linear hydrostatic restoring matrix from the waterplane area and the center of 

buoyancy 

Lines Cij = (i,j) component of the linear restoring matrix from all mooring lines 

Dc = effective diameter of a mooring line 

EA = extensional stiffness of a mooring line 

fi = component of the forcing function associated with the i

th degree of freedom Lines Fi = i

th component of the total load on the support platform from the contribution of all mooring lines 

Lines,0 Fi = i

th component of the total mooring line load acting on the support platform in its undisplaced position

Viscous Fi = i

th component of the total viscous drag load acting on the support platform from Morison’s equation 

Waves Fi = i

th component of the total excitation force on the support platform from incident waves 

g = gravitational acceleration constant 

 

*

 Employees of the Midwest Research Institute under Contract No. DE-AC36-99GO10337 with the U.S. Dept. of 

Energy have authored this work. The United States Government retains, and the publisher, by accepting the article 

for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, 

worldwide license to publish or reproduce the published form of this work, or allow others to do so, for the United 

States Government purposes. 

†

 Engineer II-Structural Systems & Dynamics, National Wind Technology Center (NWTC), 1617 Cole Boulevard, 

AIAA Professional Member. 

‡

 Senior Engineer II-Structural Systems & Dynamics, National Wind Technology Center (NWTC), 1617 Cole 

Boulevard, AIAA Professional Member. 

1
HA = horizontal component of the effective tension in a mooring line at the anchor 

HF = horizontal component of the effective tension in a mooring line at the fairlead 

Hs = significant wave height for a given sea state 

Kij = (i,j) component of the wave radiation kernel matrix or impulse response functions of the radiation 

problem 

L = total unstretched length of a mooring line 

LB = unstretched length of a portion of a mooring line resting on the seabed 

Mij = (i,j) component of the inertia mass matrix 

qj = degree of freedom j (without the subscript, q represents the set of degrees of freedom) 

j q& = first time derivative of degree of freedom j (without the subscript, represents the set of first time 

derivatives of the degrees of freedom)

q&

j q&& = second time derivative of degree of freedom j (without the subscript, represents the set of second 

time derivatives of the degrees of freedom) 

q&&

s = unstretched arc distance along a mooring line from the anchor to a given point on the line 

1-Sided Sζ = one-sided power spectral density of the wave elevation per unit time

2-Sided Sζ = two-sided power spectral density of the wave elevation per unit time

t = simulation time 

Te = effective tension at a given point on a mooring line 

u = set of wind turbine control inputs 

VA = vertical component of the effective tension in a mooring line at the anchor 

VF = vertical component of the effective tension in a mooring line at the fairlead 

V0 = displaced volume of fluid when the support platform is in its undisplaced position 

W = Fourier transform of a realization of a White Gaussian Noise time series process with unit variance 

x = horizontal distance between the anchor and a given point on a mooring line 

xF = horizontal distance between the anchor and fairlead of a mooring line 

Xi = i

th component of the complex wave excitation force on the support platform per unit wave amplitude

z = vertical distance between the anchor and a given point on a mooring line 

zF = vertical distance between the anchor and fairlead of a mooring line 

β = incident wave propagation heading direction 

δij = (i,j) component of the Kronecker-Delta function (i.e., identity matrix), equal to unity when i j = and 

zero when i j ≠

μc = mass of a mooring line per unit length 

ρ = water density 

τ = dummy variable with the same units as the simulation time 

ω = for hydrodynamics, this is the frequency of incident waves or frequency of oscillation of a particular 

mode of motion of the platform; 

I 

 for mooring systems, this is the apparent weight of a line in fluid per unit length of line 

I. Introduction 

N Europe, where vacant land is scarce and vast shallow water wind resources are available, more than 800 MW of 

offshore wind energy capacity has been installed in and around the North and Baltic Seas. Although offshore 

wind turbines are not currently installed outside Europe, interest is growing worldwide, because the global offshore 

wind resource is abundant, with the U.S. potential ranked second only to China. For instance, the wind resource 

potential at 5 to 50 nautical miles off the U.S. coast is estimated to be more than the total currently installed 

electrical generating capacity of the United States (more than 900 GW).

Most of the offshore wind resource potential in the United States, China, Japan, Norway, and many other 

countries is available in water deeper than 30 m. In contrast, most of the European offshore wind turbines installed 

to date are fixed-bottom and have been installed in water shallower than 20 m by driving monopiles into the seabed 

or by relying on conventional concrete gravity bases. These technologies are economically infeasible in deeper 

waters. Instead, space frame substructures, including tripods, quadpods, or lattice frames (Jackets), will be required 

to maintain the strength and stiffness requirements at the lowest possible cost. The Beatrice Wind Farm 

Demonstrator Project, where two 5-MW wind turbines will be installed on a Jacket in 45 m of water, is a good 

2
example of this technology.§

 At some depth, however, floating support platforms will be the most economical. 

Without performing a dynamic analysis, Musial, Butterfield, and Boone have demonstrated the economic potential 

of one floating platform design.2

Numerous floating support platform configurations are possible for offshore wind turbines when one considers 

the variety of mooring systems, tanks, and ballast options that are used in the offshore oil and gas industries. Figure 

1 illustrates several of the concepts, which are classified in terms of how the designs achieve static stability. The 

Spar-buoy concept achieves stability by using ballast to lower the center of gravity below the center of buoyancy 

and can be moored by catenary or taut lines. The Tension Leg Platform (TLP) achieves stability through the use of 

mooring line tension brought about by excess buoyancy in the tank. The barge concept achieves stability through its 

waterplane area and is generally moored by catenary lines. Hybrid concepts, which use features from all three 

classes, are also an option.5 

Numerous recent studies have assessed the preliminary design of floating offshore wind turbines. Many of these 

projects used linear frequency domain analysis, which is commonly employed in the offshore oil and gas industry. 

For example, Bulder et al used linear frequency domain hydrodynamics techniques to find the Response Amplitude 

Operators (RAOs) and amplitude standard deviations of the six rigid-body modes of motion for the support platform 

of a tri-floater design for a 5-MW wind turbine. A similar process was used by Lee to analyze a TLP design and a 

taut-leg Spar-buoy design for 1.5-MW wind turbine and by Wayman et al8,9 to analyze multiple TLP designs and a 

shallow drafted barge design for a 5-MW wind turbine. Most recently, through frequency domain analysis, 

Vijfhuizen designed a barge for a 5-MW wind turbine, which is also a platform for an oscillating water column 

(OWC) wave-energy device. In these studies, the attributes of the wind turbine were included by augmenting the 

body mass matrix with the mass properties of the turbine and by augmenting the hydrodynamic damping and 

restoring matrices with damping and restoring contributions from rotor aerodynamics and gyroscopics. 

Figure 1. Floating support platform concepts for offshore wind turbines.

 

§

 Website: http://www.beatricewind.co.uk/home/default.asp. 

3
Additionally, the linearized restoring properties of the mooring system were derived about a mean offset 

displacement of the support platform caused by the aerodynamic thrust on the rotor. The elasticity of the wind 

turbine was ignored. Bulder et al, Lee, Wayman et al, and Vijfhuizen all demonstrated the technical feasibility of 

floating offshore wind turbines by showing that, through proper design, the natural frequencies of the floating 

support platform could be placed where there is little energy in the wave spectrum to ensure that the dynamic 

response is minimized. 

One limitation of these linear frequency domain analyses is that they cannot capture nonlinear dynamic 

characteristics and transient events that are important considerations in the analysis of wind turbines. Several 

floating offshore wind turbine studies have addressed this limitation. Using a state-domain technique, Henderson 

and Patel used RAOs to prescribe the motions of a 700-kW wind turbine to determine the effect platform motions 

have on turbine fatigue loads. They showed that platform motions have little effect on power capture and rotor 

loads; instead, these are dominated by the aerodynamics of the rotor. However, they also showed that platform 

motions have a substantial effect on the nacelle and tower loads, which are dominated by inertia; thus, the tower will 

have to be strengthened if the platform motions cannot be reduced. The same conclusions were drawn 

independently by Fulton et al and Withee, who used different time-domain aeroelastic wind turbine simulators that 

had been adapted to include the effects of platform motion and hydrodynamic loading of TLP designs for a 5-MW 

and 1.5-MW wind turbine, respectively. 

Limitations in these studies must also be addressed. For instance, the time domain dynamic models employed by 

Fulton et al and Withee used Morison’s equation14,15 to compute of the hydrodynamic loading on the TLPs. 

Morison’s equation ignores many of the effects that are important to the analysis of more general support platform 

configurations, such as the effects of platform size in the diffraction problem, wave radiation damping and freesurface memory, and added mass-induced coupling between modes of motion.16,17 Also, the conclusions drawn by 

Henderson and Patel, Fulton et al, and Withee must be verified through a rigorous loads analysis. 

International design standards for wind turbines18,19 require that an integrated loads analysis be performed when 

a machine is certified. Such analysis is also beneficial for design, to obtain cost-effective wind turbines that achieve 

favorable performance and maintain structural integrity. Integrated loads analyses are carried out with 

comprehensive time-domain simulation tools that employ sophisticated models of both turbulent and deterministic 

wind-inflow; aerodynamic, gravitational, and inertial loading of the rotor, nacelle, and tower; elastic effects within 

and between components and in the foundation; and mechanical actuation and electrical responses of the generator 

and of the control and protection systems. For offshore wind turbines, additional models of the hydrodynamic 

loading in regular and irregular seas, the dynamic coupling between the support platform motions and wind turbine 

motions, and the dynamic characterization of mooring systems for compliant floating platforms are also necessary. 

This paper is a continuation of the work presented previously by Jonkman and Sclavounos, which introduces the 

development of a comprehensive simulation tool that can model the fully coupled aero-hydro-servo-elastic response 

of floating offshore wind turbines. Our floating offshore wind turbine simulator was developed with enough 

sophistication to address the limitations of the previous time and frequency domain studies and to have the features 

required to perform an integrated loads analysis. A summary of the model development presented in, as well some 

corrections to, Ref. 20 are given in section II. We then present a verification of the simulation capability and 

demonstrate its potential in loads analysis applications. The results of comprehensive loads analyses will be 

presented in future papers. 

II. Development of a Fully Coupled Simulation Tool 

Although some wind turbine simulators have been expanded to include the additional loading and responses 

representative of fixed-bottom offshore support structures,21-26 only recently has simulation capability for modeling 

the fully coupled aero-hydro-servo-elastic response of floating offshore wind turbines been available.20 This 

simulation capability for floating offshore wind turbines has been developed by interfacing the hydrodynamic 

computer model WAMIT®, which is commonly employed in the offshore oil and gas industry, with the wind 

industry-accepted aerodynamics module AeroDyn and servo-elastic simulation tools FAST and MSC.ADAMS®.

30,31

A rundown of the interface and calculations is given in Fig. 2. See Ref. 20 for a detailed description of these 

calculations and nomenclature. To summarize, the aero-servo-elastic models of FAST and ADAMS with AeroDyn 

contain contributions from wind-inflow, aerodynamics, gravity, controls, and the structural dynamics of the wind 

turbine. This includes elasticity and the dynamic coupling between the motions of the support platform and the 

motions of the wind turbine. The nonlinear restoring loads from the mooring system were obtained by interfacing 

FAST and ADAMS with a quasi-static mooring line module that accounts for the elastic stretching of an array of 

homogenous taut or slack catenary lines with seabed interaction. The hydrodynamic loads on the support platform 

4
include the restoring contributions of buoyancy and the waterplane area from hydrostatics; the viscous drag 

contributions from Morison’s equation; the added mass and damping contributions from wave radiation, including 

free surface memory effects; and the incident wave excitation from diffraction in regular or irregular seas. The 

matrices in the hydrodynamic loading expressions depend on the geometry of the support platform and can be found 

from the solution of the frequency domain, potential flow, radiation/diffraction, hydrodynamics problem14,15 by 

using WAMIT as a preprocessor. Not included in the model were the effects of mooring system inertia and 

damping, vortex-induced vibration and loading from sea ice, or nonlinear hydrodynamic effects, such as mean drift, 

slow-drift, and sum-frequency excitation and high-order wave kinematics. Reference 20 explains the model features 

and limitations in detail. 

Platform Pos. ( ) 

Wave Spectrum

and Direction

Damping

(Radiation Kernel)

Incident Wave

Excitation

Quasi-Static

Mooring Routine

Platform Motions

( , , )

Frequency Domain , Potential Flow , Radiation/Diffraction 

Hydrodynamics Preprocessor (WAMIT)

Mooring Restoring

Aij ( ) ω

Aij ( ) ∞

Bij ( ) ω

j q j q& t

Aij i f

Hydrodynamic Loads

( , )

Aero-Servo-Elastics

(FAST, ADAMS / AeroDyn)

Added Mass

(Radiation Problem)

Damping

(Radiation Problem)

Added Mass

(Infinite Frequency Limit)

qj

ij ( ) ij () ( ) 0

2 K t = B cos t d ω ωω π

∞

∫

Xi (ω,β )

Seed for 

RNG

( ) Lines Lines,0 Lines Fiii = F C q if linear j j ⎡ ⎤ − ⎣ ⎦

Restoring

(Buoyancy)

Incident Wave Excitation

(Diffraction Problem)

White

Noise

W (ω )

Wave

Environment

( ) 1-Sided S , ζ ω β

Mij () ( ) j i q,u,t q f q,q,u,t && & =

Time Domain Hydrodynamics Routine

 ,

A =A ij ij (∞)

Hydrostatic V ,C0 ij

( ) ()

Waves Viscous Lines

ii i i

t

Hydrostatic

0 i3 ij j ij j

0

fF F F

ρ δ gV C q K t q d τ ττ

=+ +

+ − −− ∫ &

( ) () () ( ) Waves 2-Sided j t

i i

-

1 F t= W 2S X , e d 2

ω ω π ω ωβ ω ζ π

∞

∞

∫

Figure 2. Summary of the calculations for the interface of support platform loads to FAST and ADAMS. 

Three corrections to the information presented in Ref. 20 are noted. First, all the inverse Fourier transforms 

presented in Ref. 20 had the wrong sign in the exponent of the complex exponential. That is, the inverse Fourier 

transforms stated with j t e− ω should have employed j t e ω instead. Second, sea current has been introduced in the 

model, contrary to the assumption that is declared in the paper. To include the effects of sea current generated by 

winds, tides, and thermal gradients, the steady, depth-varying current velocity has been combined vectorally with the 

wave-particle velocity to compute the viscous-drag term from Morison’s equation. The ancillary effect of the sea 

current on the radiation and diffraction problems, such as the Doppler-shifted frequency-of-encounter effect,15 is 

ignored. The third correction has to do with the treatment of the mooring system. Reference 20 proposed including 

mooring system behavior into the fully coupled model by interfacing the dynamic mooring system LINES module 

of SML32-35 with FAST and ADAMS. A quasi-static model of the mooring system has replaced this interface 

because it was discovered that LINES is numerically unstable when modeling the slack catenary lines of interest in 

our analyses. The development of this quasi-static mooring system module is presented next. 

A. Quasi-Static Mooring System Module 

Because we discovered that LINES was unsuitable for our use, we developed our own quasi-static module to 

simulate the nonlinear restoring loads from the mooring system of floating platforms. Instead of interfacing LINES, 

we have interfaced our mooring system module to FAST and ADAMS. 

Our module can model an array of homogenous taut or slack catenary mooring lines. Our module accounts for 

the apparent weight in fluid, elastic stretching, and seabed friction of each line. And since our quasi-static module is 

fully coupled with FAST and ADAMS, the nonlinear geometric restoration of the complete mooring system is also 

5
accounted for. By quasi-static, we mean that with the fairlead positions known for a given platform displacement at 

any instant in time, our mooring system module solves for the tensions within, and configuration of, each mooring 

line by assuming that each cable is in static equilibrium at that instant. Using the tensions (and additional loading on 

the platform from hydrodynamics and loading on the turbine from aerodynamics), FAST or ADAMS solve the 

dynamic equations of motion for the accelerations of the rest of the system (platform, tower, nacelle, blades), then 

integrate in time to obtain new platform and fairlead positions at the next time step. (The process repeats at each 

time step.) Our model neglects the bending stiffness of the mooring lines. 

Obviously, this quasi-static approach also ignores the inertia and damping of the mooring system, which may or 

may not be important in various situations. To justify the use of this approach, we used the system mass data 

presented in section III to calculate that the mass of a typical mooring system is eight percent of the combined mass 

of a typical wind turbine and floating support platform. According to our conversations with Dr. Robert Zueck of 

the Naval Civil Engineering Laboratory, about one-quarter of the inertia of a mooring system is important to the 

dynamic response of a floating platform. One-quarter of eight percent is only two percent, which justifies ignoring 

mooring system inertia in our analyses for these turbine/platform configurations. Also, ignoring mooring system 

damping is conservative. 

A layout of the calculations in our quasi-static mooring system module is presented in Fig. 3. Each line of the 

mooring system is analyzed separately. The user must specify the fairlead locations of each mooring line relative 

(and fixed) to the support platform and the anchor locations of each mooring line relative (and fixed) to the inertia 

frame. For each mooring line, the total unstretched length, L, apparent weight in fluid per unit length, ω, extensional 

stiffness, EA, and coefficient of seabed static friction drag, CB, must also be assigned. Because a mooring line is 

buoyant, ω is related to the mass of the line per unit length, μc, by: 

2

c

c

D

g 4

π

ω μρ

⎛ ⎞

= − ⎜

⎜

⎝ ⎠

⎟

⎟ , (1) 

where ρ is the water density, g is the gravitational acceleration constant, and Dc is the effective diameter of the 

mooring line. Since we are limited to modeling homogenous mooring lines, we handle multi-segment lines (i.e. 

chain + wire + chain segments in series) by using an equivalent line with weighted average values of the weight and 

stiffness (weighted based on the unstretched lengths of each segment). 

Quasi-Static Mooring System Module (Calculations Shown for Each Line)

Newton-Raphson Iteration to 

Global to Local Find Fairlead Tension

Transformation

Compute 

Anchor 

Tension

Compute Configuration of,

& Tensions Within, Line

Fairlead Location Relative

to Anchor ( , )

Fairlead Tension ( , )

Local to Global 

Transformation & 

Sum Tensions

Platform Pos.

Mooring Restoring

Anchor Tension

( , )

Configuration of, 

& Tensions Within, Line

[ , , ]

Mooring Line Properties

( , , , )

Fx Fz

L ω EA CB

F ( ) F F B x F H ,V ,L, ,EA,C = ω

zF FF = F H ,V ,L, ,EA ( ) ω

HF VF

HA VA x s( ) z(s) ( ) T s e

Figure 3. Summary of the calculations in our quasi-static mooring system module. 

6
Each mooring line is analyzed in a local coordinate 

system whose origin lies at the anchor. The local zaxis of this coordinate system is vertical and the local 

x-axis is directed horizontally from the anchor to the 

instantaneous position of the fairlead. Figure 4 

illustrates a typical line. When the mooring system 

module is called for a given support platform 

displacement, the module first transforms each 

fairlead position from the global frame to this local 

system to determine its location relative to the anchor, 

xF and zF. 

We take advantage of the analytical formulation 

for an elastic cable suspended between two points, 

hanging under its own weight (in fluid). For brevity, 

the derivation of this analytical formulation is not 

given here. But we derived it following a procedure similar to that presented in Ref. 15. (The derivation is not 

exactly the same because Ref. 15 does not account for seabed interaction nor does it account for taut lines where the 

angle of the line at the anchor is nonzero). The derivation requires that we assume the extensional stiffness of the 

mooring line, EA, is much greater than the hydrostatic pressure at all locations along the line. In the local coordinate 

system, the analytical formulation is given in terms of two nonlinear equations in two unknowns. The two 

unknowns are the horizontal and vertical components of the effective tension in the mooring line at the fairlead, HF

and VF, respectively. [The effective tension is defined as the actual cable (wall) tension plus the hydrostatic 

pressure.] When no portion of the line rests on the seabed, the analytical formulation is as follows: 

Figure 4. Mooring line in a local coordinate system. 

( )

2 2

FF F F F

F FF

FF F F

H V V VL VL HF x H ,V = ln 1 ln 1 L

H HH H

ω ω

ω

⎧ ⎫ ⎡ ⎤ ⎡ ⎤ ⎪ ⎪ ⎛ ⎞ − − ⎛ ⎞ ⎢ ⎥ ++ − ++ + ⎢ ⎥ ⎨ ⎬ ⎜ ⎟ ⎜ ⎟ ⎢ ⎥ ⎢ ⎥ ⎪ ⎪ ⎝ ⎠ ⎝ ⎠ ⎩ ⎭ ⎣ ⎦ ⎣ ⎦ EA

 (2a) 

and 

( )

2 2 2

FF F

F FF F

F F

H V VL 1 z H ,V = 1 1 V L L

H H EA

ω

ω

⎛⎞ ⎛ ⎞ − ⎛ ⎢ ⎥ + −+ + − ⎜⎟ ⎜ ⎟ ⎜ ⎝⎠ ⎝ ⎠ ⎝ ⎠ ⎣ ⎦ 2

ω

⎡ ⎤ ⎞

⎟. (2b) 

This formulation is sometimes cited in terms of the inverse of the hyperbolic sine, that is: 

( ) ( ) 1 2 sinh x ln x 1 x − = ++ . 

The first terms on the right-hand side of Eq. (2) characterize the arc length of the catenary, projected on the xand z- axes. (Even taut mooring lines have a catenary shaped sag.) The second terms on the right hand side of Eq. 

(2) represent the horizontal and vertical stretching of the mooring line. 

The analytical formulation of two equations in two unknowns is different when a portion of the mooring line 

adjacent to the anchor rests on the seabed: 

( )

2

FF F F F

F FF

F F

2

B F FF F F

B B

V H V V HL x H ,V = L ln 1 H H EA

C V VH VH L L MAX L

2EA C C

ω ω

ω

ω ωω ωω

⎡ ⎤ ⎛ ⎞

−+ ++ + ⎢ ⎥ ⎜ ⎟ ⎝ ⎠ ⎣ ⎦

⎡ ⎤ ⎛ ⎞ ⎛ ⎞⎛

+ −− +− − − − ⎢ ⎥ ⎜ ⎟ ⎜ ⎟⎜ ⎣ ⎦ ⎝ ⎠ ⎝ ⎠⎝

,0

⎞

⎟

⎠

 (3a) 

and 

7
( )

2 2

F F

F FF

F

HV V z H ,V = 1 1 F

ω H 2EAω

⎡ ⎤ ⎛ ⎞ ⎢ ⎥ + −+ ⎜ ⎟ ⎢ ⎥ ⎝ ⎠ ⎣ ⎦

. (3b) 

The first two terms on the right-hand side of Eq. (3a) combine to represent the unstretched portion of the 

mooring line resting on the seabed, LB. [LB is zero in Eq. (2).] That is: 

F

B

V L L

ω = − . (4) 

The last term on the right-hand side of Eq. (3a), which involves CB, corresponds to the stretched portion of the 

mooring line resting on the seabed that is affected by static friction. The seabed static friction is modeled simply as 

a drag force per unit length of CBω. The MAX function is needed to handle cases with and without tension at the 

anchor. Specifically, the resultant is zero when the anchor tension is positive; that is, the seabed friction is too weak 

to overcome the horizontal tension in the mooring line. Conversely, the resultant of the MAX function is nonzero 

when the anchor tension is zero, which happens when a long enough section of cable is lying on the seabed to ensure 

that the seabed friction entirely overcomes the horizontal tension in the mooring line. 

The remaining terms in Eq. (3) are similar in form to, and typify the same information as, the terms in Eq. (2). 

They are simpler than the terms in Eq. (2) because the catenary is always tangent to the seabed at the point of 

touchdown. 

Our mooring system module uses a Newton-Raphson iteration scheme to solve nonlinear Eq. (2) and Eq. (3) for 

the fairlead effective tension (HF and VF,), given the line properties (L, ω, EA, and CB) and the fairlead position 

relative to the anchor (xF and zF). The Jacobian in the Newton-Raphson iteration was implemented with the 

analytical derivatives of Eq. (2) and Eq. (3). Our module uses the values of HF and VF from the previous time step 

as the initial guess in the next iteration. As the model is being initialized, we use the starting values documented in 

Ref. 36. Our mooring system module determines which of Eq. (2) or Eq. (3) must be used as part of the solution 

process. The equations were implemented in a slightly different form than shown to, for example, avoid divisionby-zero problems when CB is zero-valued. 

Once the effective tension at the fairlead has been found, determining the horizontal and vertical components of 

the effective tension in the mooring line at the anchor, HA and VA, respectively, is simple. (The blue arrows 

depicting HA and VA in Fig. 4 are the horizontal and vertical components of the effective line tension at the anchor—

they are not the reaction forces at the anchor.) From a balance of external forces on a mooring line, one can easily 

verify that: 

HA = HF (5a) 

and 

VV L A F = −ω , (5b) 

when no portion of the line rests on the seabed, and: 

HA F = − MAX H C L ,0 ( Bω B ) (6a) 

and 

V 0 A = , (6b) 

when a portion of the line does rest on the seabed. Though they do not affect the dynamic response of the floating 

wind turbine system, the anchor effective tensions are computed by our mooring system module and become 

available outputs from the simulation. 

8
Next, our mooring system module solves for the configuration of, and effective tensions within, the mooring 

line. Again, the values of these parameters do not affect the dynamic response of the floating wind turbine system, 

but they are available outputs from the simulation. When no portion of the mooring line rests on the seabed, the 

equations for the horizontal and vertical distances between the anchor and a given point on the line, x and z, and the 

equation for the effective tension in the line at that point, Te, are as follows: 

( )

2 2

F A A AA

F F FF

H Vs Vs V V HF x s = ln 1 ln 1 s

H H HH

ω ω

ω

⎧ ⎫ ⎡ ⎤ ⎡ ⎪ ⎪ + + ⎛ ⎞ ⎛⎞ ⎢ ⎥ ++ − ++ + ⎢ ⎨ ⎬ ⎜ ⎟ ⎜⎟ ⎢ ⎪ ⎪ ⎝ ⎠ ⎝⎠ ⎩ ⎭ ⎣ ⎦ ⎣ EA

⎤

⎥

⎥

⎦

, (7a) 

( )

2 2

s

2

F A A

A

F F

H Vs V 1 zs= 1 1 V s

H H EA 2

ω ω

ω

⎛ ⎞ ⎛⎞ + ⎛ ⎢ ⎥ + −+ + + ⎜ ⎟ ⎜⎟ ⎜ ⎝ ⎠ ⎝⎠ ⎝ ⎠ ⎣ ⎦

⎡ ⎤ ⎞

⎟, (7b) 

and 

() ( )2 2 T s = H +V s e FA +ω , (8) 

where s is the unstretched arc distance along the mooring line from the anchor to the given point. The similarity 

between Eq. (7) and Eq. (2) should be apparent. Of similarity with Eq. (3), the equations with seabed interaction are 

more onerous: 

( ) ( ) ( )

F

B

B

B FF F F 2

B BB B

BB B B

2

F F B B

B

F F

B FF 2

B B B

B B

H s f C

C HH H H B

or 0 s L

s s 2 L s L MAX L ,0 for L s 2EA C C C C

xs= H H sL sL s L ln 1

H HEA

f

C HH L L MAX L ,0 2EA C C

L

ω

ω

ωω ω ω

ω ω

ω

ω

ω ω

≤≤ −

⎡ ⎤ ⎛ ⎞⎛ ⎞ ⎛ ⎞ + − − + − − − ≤≤ ⎢ ⎥ ⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎣ ⎦ ⎝ ⎠⎝ ⎠ ⎝ ⎠

⎡ ⎤ − − ⎛ ⎞ ⎢ ⎥ + + + + ⎜ ⎟ ⎝ ⎠ ⎣ ⎦

⎡ ⎤ ⎛ ⎞⎛ ⎞

+ −+ − − ⎢ ⎥ ⎜ ⎟⎜ ⎟ ⎣ ⎦ ⎝ ⎠⎝ ⎠

B or L s L

⎧

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎨

⎪

⎪ ≤ ≤ ⎪

⎪

⎪

⎪

⎪

⎩

, (9a) 

( ) ( ) ( )

B

2 2

F B B

B

F

zs= H sL sL

ln 1 1 for L s L H 2EA

ω ω

ω

⎧

⎪

⎪ ⎡ ⎤ ⎨ ⎛ ⎞ − − ⎢ ⎥ ⎪ + −+ ≤ ⎜ ⎟ ⎝ ⎠ ⎪⎩ ⎣ ⎦

0 for 0 ≤ ≤s L

≤

, (9b) 

and 

( ) ( ( ) )

( ) ( )

F B B

e 2 2

F B B

MAX H C s L ,0 for 0 s LB

T s=

H s L for L s L

ω

ω

⎧ + − ≤≤ ⎪

⎨

⎪ + − ≤ ⎩ ≤

. (10) 

9
As shown in Fig. 3, the final calculation in our quasi-static mooring system module is to compute the total load 

on the support from the contribution of all mooring lines. This mooring system restoring load is found by first 

transforming each fairlead tension from its local mooring line coordinate system to the global frame, then summing 

up the tensions from all lines. 

III. Wind Turbine and Support Platform Model Descriptions 

To support concept studies aimed at assessing offshore wind technology that is suitable in the deeper waters off 

the U.S. Offshore Continental Shelf and other offshore sites, the National Renewable Energy Laboratory’s 

(NREL’s) National Wind Technology Center (NWTC) has established a set of baseline wind turbine specifications 

and reference site properties. The wind turbine specification consists of definitions of the aerodynamic, structural, 

and control system properties. The rating of this turbine is 5 MW, which is large by today’s standards, but which is 

assumed to be the minimum rating necessary to make a floating system economical because of the large proportion 

of the costs in the support platform. Two floating platform concepts were used for this study. The first is a barge 

with catenary moorings designed by the Department of Naval Architecture and Marine Engineering (NAME) at the 

Universities of Glasgow and Strathclyde through a contract with ITI Energy. The second is a barge with spread 

moorings designed by the Massachusetts Institute of Technology (MIT) through a contract with NREL. Barges 

were chosen because of their simplicity in design, fabrication, and installation. 

A. NREL Offshore 5-MW Baseline Wind Turbine 

The 5-MW turbine design used in this study is typical of large, offshore turbines. We obtained some broad 

design details from the published documents of turbine manufacturers. We used typical, preliminary-design 

methods to arrive at the details required to build sophisticated models. Table 1 summarizes some of these 

properties. Greater detail is available elsewhere.37 

Table 1. Summary of baseline wind turbine properties. 

B. ITI Energy Barge 

We used a preliminary barge design from ITI Energy for some of the verification exercises and for our loads 

analysis demonstration. Not only is the barge designed to support the NREL offshore 5-MW baseline wind turbine, 

but it is also a platform for an OWC wave power device. For simplicity in design and fabrication, the barge is 

square and the wave energy is extracted from a square moonpool located at the center of the barge. This allows the 

OWC to be located within the tower. To prevent it from drifting, the platform is moored by a system of eight 

catenary lines, two of which emanate from each corner of the barge. We modeled the slack mooring lines as being 

parallel to the sides of the barge, so that each pair would be 90° apart at the corner. We modeled the mooring 

system configuration in this way to enable comparisons to work done by NAME, although ITI Energy envisions the 

actual angle to be 45°. Each catenary has roughly 250 m of line resting on the seabed when the barge is in its 

neutral position. We provide some details of the barge and mooring system in Table 2. The concept is documented 

in much greater detail in Ref. 10. 

Rating 5 MW 

Wind Regime IEC 61400-3 (Offshore) Class 1B, Class 6 winds 

Rotor Orientation Upwind 

Control Variable Speed, Collective Pitch, Active Yaw 

Rotor, Hub Diameter 126 m, 3 m 

Hub Height 90 m 

Rated Rotor, Generator Speed 12.1 rpm, 1173.7 rpm 

Rated Tip Speed 80 m/s 

Overhang, Shaft Tilt, Precone 5 m, 5°, 2.5° 

Rotor Mass 110,000 kg 

Nacelle Mass 240,000 kg 

Tower Mass 347,460 kg 

Overall Center of Mass (−0.2 m, 0.0 m, 64.0 m) 

10
C. MIT/NREL Barge 

Elizabeth Wayman of MIT also developed preliminary designs of several floating platforms for the NREL 

offshore 5-MW baseline wind turbine. One of her designs is named the MIT/NREL shallow drafted barge (SDB). 

This design is documented in great detail in her M.S. dissertation.9

 The cylindrical barge has a spread mooring 

system with four pairs of taut lines radiating outward. We list some of the design specifications in Table 3. 

IV. Verification of Simulation Capability 

The aero-servo-elastic features of FAST and ADAMS have been well verified and validated in previous 

studies.38-43 However, the new hydrodynamics and mooring system modules are novel and require verification to 

ensure that the response predictions from the fully coupled aero-hydro-servo-elastic capability are accurate. We 

have performed six verification studies to test the accuracy of the new features: two for the hydrodynamics module, 

two for the mooring system module, and two for the complete system. The last pair of verification exercises 

compared the results from our time domain simulation tool with the results from a frequency domain model. 

Additionally, though not explicitly documented here, the resulting dynamics from the newly added support platform 

degrees of freedom (DOFs) in FAST also agree well with ADAMS. Some examples of this are presented in section 

V. All these verification exercises use model-to-model comparisons. The fully coupled simulation tool will be 

validated later, once experimental data are made available. 

Table 3. Summary of MIT/NREL barge properties. 

Diameter, Height 36 m, 9.5 m 

Draft, Freeboard 5 m, 4.5 m 

Water Displacement 5,089 m3

Mass, Including Ballast 4,519,000 kg 

Center of Mass (CM) below SWL 3.882 m 

Roll Inertia about CM 390,100,000 kg·m2

Pitch Inertia about CM 390,100,000 kg·m2

Yaw Inertia about CM 750,900,000 kg·m2

Anchor Depth 200 m 

Separation Between Opposing Anchors 436 m 

Unstretched Line Length 279.3 m 

Line Diameter 0.127 m 

Line Mass Density 116.0 kg/m 

Line Extensional Stiffness 1,500,000,000 N 

Table 2. Summary of ITI Energy barge properties. 

Size (W×L×H) 40 m × 40 m × 10 m 

Moonpool (W×L×H) 10 m × 10 m × 10 m 

Draft, Freeboard 4 m, 6m 

Water Displacement 3 6,000 m

Mass, Including Ballast 5,452,000 kg 

Center of Mass (CM) below SWL 0.323 m 

Roll Inertia about CM 2 726,900,000 kg·m

Pitch Inertia about CM 2 726,900,000 kg·m

Yaw Inertia about CM 2 1,453,900,000 kg·m

Anchor Depth 150 m 

Separation Between Opposing Anchors 834.3 m 

Unstretched Line Length 473.3 m 

Line Diameter 0.0809 m 

Line Mass Density 130.4 kg/m 

Line Extensional Stiffness 589,000,000 N 

11
A. Verification of the Hydrodynamics Module 

We performed two verification tests to check the hydrodynamics module. First, we verified that the output from 

WAMIT, which is used as input to our hydrodynamics module, is similar to that generated by a different 

radiation/diffraction solver. Second, we verified that the radiation impulse response functions computed within our 

hydrodynamics module are the same as what is computed with WAMIT’s stand-alone frequency-to-time (F2T) 

conversion utility. 

1. WAMIT Output/Module Input 

We use WAMIT as a preprocessor for generating the hydrodynamic added-mass and damping matrices, Aij (ω)

and Bij ( ) ω , and wave-excitation force, Xi (ω,β ) , that are inputs to our simulation tool. WAMIT is a code that 

uses the three-dimensional, numerical panel method to solve the linearized radiation and diffraction problems for the 

interaction of offshore platforms with surface waves. The hydrodynamic added mass and damping matrices come 

from the solution of the radiation problem and depend on the water depth, the geometric shape of the support 

platform, its proximity to the free surface, and its frequency of oscillation in a particular mode of motion. The 

hydrodynamic wave-excitation force comes from the solution of the diffraction problem and depends on the water 

depth, the geometric shape of the support platform, its proximity to the free surface, and the frequency and direction 

of the incident wave. WAMIT ignores the effects of sea current or forward speed on the radiation and diffraction 

problems, as well as higher order effects.27

Since the hydrodynamic solution generated by our simulation tool is only as good as the hydrodynamic inputs, 

verifying the acceptability 

of the WAMIT output is 

beneficial. Consequently, 

we ran a test to ensure 

that the WAMIT output is 

similar to that calculated 

by a different 

radiation/diffraction 

solver. Data used by 

NAME when designing 

the ITI Energy barge 

were available to make 

this comparison. 

In WAMIT, the barge 

was modeled with two 

geometric planes of 

symmetry with 2400 

rectangular panels within 

a quarter of the body. 

Consistent with linear 

theory, we needed to mesh only the wetted portion of the body in its undisplaced position. Figure 5 shows the panel 

mesh. To avoid accounting for the OWC in the WAMIT 

analysis, we covered the moonpool with a fixed plate located 

0.01 m below the free surface. In an attempt to model the 

OWC in NAME’s analysis, they considered that the plate was 

free to move relative to the barge. The panel mesh for 

NAME’s analysis is shown in Fig. 6. 

Figure 5. Panel mesh used within WAMIT. 

To improve the accuracy of the WAMIT results, we chose 

to override default settings to (a) integrate the logarithmic 

singularity analytically, (b) solve the linear system of 

equations using a direct solver, and (c) remove the effects of 

irregular frequencies by automatically projecting the body 

panels to the free surface. These settings were necessary 

since some panels are located in a plane near the free surface, 

the barge has a large waterplane area, and high frequency 

results were needed in subsequent analyses. The barge was 

analyzed in its undisplaced position with infinite water depth Figure 6. Panel mesh used by NAME. 

12
in both codes. The hydrodynamic added mass and damping matrices were compared in all six rigid body modes of 

motion of the barge (in the subscripts, 1 = surge, 2 = sway, 3 = heave, 4 = roll, 5 = pitch, 6 = yaw). As such, the 

matrices at each frequency are size 6 × 6. Since the plate was considered as a separate body in NAME’s analysis, 

the resulting matrices at each were size 12 × 12, but these were reduced down to 6 × 6 matrices via post-processing 

to assist in the comparison. The hydrodynamic wave-excitation force was not considered in this test. 

Figure 7 shows the results in a side-by-side comparison. All data are dimensional as indicated. Only the uppertriangular matrix elements are shown since the hydrodynamic added mass and damping matrices are symmetric in 

the absence of sea current or forward speed.14,15 Also, because of the barge’s symmetries, the surge-surge elements 

of the frequency-dependent added mass and damping matrices, A11 and B11, are identical to the sway-sway elements, 

A22 and B22. Likewise, the roll-roll elements, A44 and B44, are identical to the pitch-pitch elements, A55 and B55. 

Other matrix elements not shown are zero-valued or very close to being zero-valued. 

The WAMIT results are given in even increments of frequency. The NAME results are given in even increments 

0E+0

1E+7

2E+7

3E+7

4E+7

01234

Frequency, rad/s

Frc-Trans Modes, kg

A11 WAMIT

A22 WAMIT

A33 WAMIT

A11 NAME

A22 NAME

A33 NAME

-2E+6

0E+0

2E+6

4E+6

6E+6

8E+6

01234

Frequency, rad/s

Frc-Trans Modes, kg/s

B11 WAMIT

B22 WAMIT

B33 WAMIT

B11 NAME

B22 NAME

B33 NAME

0.0E+0

5.0E+8

1.0E+9

1.5E+9

2.0E+9

0123 4

0E+0

2E+8

4E+8

6E+8

01234

Frequency, rad/s

Mom-Rot Modes, kg·m2/s

B44 WAMIT

B55 WAMIT

B66 WAMIT

B44 NAME

B55 NAME

B66 NAME

Frequency, rad/s

Mom-Rot Modes, kg·m2

A44 WAMIT

A55 WAMIT

A66 WAMIT

A44 NAME

A55 NAME

A66 NAME

-3E+7

-2E+7

-1E+7

0E+0

1E+7

2E+7

3E+7

01234

Frequency, rad/s

Frc-Rot & Mom-Trans Modes, kg·m

A15 WAMIT

A24 WAMIT

A15 NAME

A24 NAME

-3E+7

-2E+7

-1E+7

0E+0

1E+7

2E+7

3E+7

01234

Frequency, rad/s

Frc-Rot & Mom-Trans Modes, kg·m/s

B15 WAMIT

B24 WAMIT

B15 NAME

B24 NAME

Figure 7. Frequency-dependent hydrodynamic added-mass and damping matrices for the ITI Energy barge. 

13
of period, so resolution is lost at the higher frequencies. As expected, all matrix elements peak out at some 

intermediate frequency and level out at higher frequencies. Additionally, the zero- and infinite-frequency limits of 

all elements of the hydrodynamic damping matrix are zero (not all shown), as required by theory.14,15 The 

comparisons between the output of WAMIT and the results of NAME agree very well, in general, and demonstrate 

that WAMIT is an acceptable code for generating the hydrodynamics inputs needed by our simulation tool. The 

biggest discrepancies are in the heave-heave elements of the frequency-dependent added mass and damping, A33 and 

B33. We believe that these differences are artifacts of the dissimilar numerical solutions employed by WAMIT and 

NAME’s radiation/diffraction solver. The differences are not large, however, and are not deemed crucial to the 

accuracy of our hydrodynamics solution. 

2. Computation of Radiation Impulse-Response Functions 

As described in Ref. 20, when a body is forced to move in a fluid, waves are generated on the free surface. As 

time progresses, these waves will propagate, or radiate away from the body and will influence the fluid pressure on 

the body for all subsequent time. The resulting time-dependent hydrodynamic loads are known as memory effects, 

denoting that they depend on the history of motion of the body. The memory effect is captured in our model 

through the convolution term in Fig. 2. The kernel, Kij (t) , in this convolution integral is commonly referred to as 

the impulse response function of the radiation problem. Specifically, the (i,j) component of the radiation impulse 

response function, Kij ( )t , represents the hydrodynamic force at time t in the direction of DOF i due to a unit 

impulse in velocity at time zero of DOF j. In our hydrodynamics module, we used numerical convolution in the 

time domain to implement the memory effect directly. 

Reference 20 also describes how the radiation impulse response functions can be found from the solution of the 

frequency domain, radiation problem. Specifically, they can be found from either the sine or cosine transform of the 

frequency-dependent hydrodynamic added mass or damping matrices, respectively. The solution that uses cosine 

transforms is simpler when the integrals are computed numerically, because without a correction for truncation 

error, the accuracy of the sine transforms is poor near t = 0, where Kij (0) is, in general, not zero [even though 

sin 0( ) is]. Since the cosine transform is simpler to compute accurately, our hydrodynamics module uses it to 

compute the radiation impulse response functions. Nevertheless, verifying that this transform is implemented 

correctly is still advantageous. The cosine transform in our hydrodynamics module was implemented using a 

computationally-efficient Fast Fourier Transform (FFT) routine. 

We perform the test by verifying that the radiation impulse response functions computed within our 

hydrodynamics module are the same as what is computed by WAMIT’s stand-alone F2T conversion utility. We use 

the WAMIT output of the frequency-dependent hydrodynamic-damping matrix for the ITI Energy barge from the 

previous verification test. Because the comparison between F2T and our results is so good, only one set of results is 

presented, as shown in Fig. 8. As before, all data are dimensional as indicated and, because of the symmetries of the 

barge, the surge-surge elements are identical to the sway-sway elements and the roll-roll elements are identical to 

the pitch-pitch elements. Most of the response decays to zero after about 20 s (as shown) and has all but vanished at 

60 s (not shown). Consequently, when we run our simulation tool, we generally truncate the numerical convolution 

after 60 s of memory. 

B. Verification of the Mooring System Module 

We also performed two verification tests to check our quasi-static mooring system module. First, we verified 

that our mooring system module correctly solves a classic benchmark problem for the static equilibrium of a 

suspended cable structure. Second, we verified that the nonlinear force-displacement relationships for a mooring 

system in surge, which are computed by our module, are the same as what was calculated by an independent 

analysis performed by NAME. 

1. Benchmark Problem 

A classical test problem for checking the accuracy of a mooring system program is that of a horizontally 

suspended cable with one support free to slide laterally. The problem is illustrated in Fig. 9. For a cable of 

unstretched length, L = 200, weight per unit length, ω = 0.1, extensional stiffness, EA = 105

, and a horizontal load of 

HF = 5.77 applied at the free end (the fairlead), the theoretical, static equilibrium solution is for a horizontal span 

of, F x = 152.2, and a vertical sag of 58.0. 

This benchmark problem involves finding a static equilibrium position of the fairlead. We tested our mooring 

system module (in its form without seabed interaction) by solving this problem through time-integration of the 

nonlinear equations of motion for as long as it took until the solution converged. We had to solve the problem in 

14
this way because our mooring system module is interfaced to FAST and ADAMS, which both operate in the time 

domain. The platform, where the fairlead attaches, was given one horizontal translation DOF and a small, 

inconsequential mass. A small amount of linear damping was added to the motion to ensure that it eventually settled 

out. If our mooring system module was implemented correctly, the horizontal span and vertical sag should settle out 

at the correct solution regardless of the lateral offset chosen as an initial condition for the DOF. Indeed, this is 

exactly what happens. 

-2E+6

0E+0

2E+6

4E+6

0 5 10 15 20

Time, s

Frc-Trans Modes,

kg/s2

K11

K22

K33

-3E+8

0E+0

3E+8

6E+8

0 5 10 15 20

Time, s

Mom-Rot Modes,

kg·m2/s2

K44

K55

K66

-2E+7

-1E+7

0E+0

1E+7

2E+7

0 5 10 15 20

Time, s

Frc-Rot & Mom-Trans Modes,

kg·m/s2

K15

K24

Figure 8. Time-dependent radiation impulse response functions for the ITI Energy barge.

Figure 10 shows the time series solution when the fairlead was positioned to the left of the anchor at time zero at 

a lateral offset of −100. Both FAST and ADAMS gave identical results. The solution is seen to converge to the 

correct result after about 120 s. Other initial conditions showed similar behavior with the same result. 

15
Figure 9. Benchmark problem for a suspended cable.

2. Nonlinear Force-Displacement Relationships 

-150

0

150

300

0 25 50 75 100 125

Time, s

Horizontal 

Displacement

FAST

ADAMS

Analytical Solution

Figure 10. Solution of the suspended cable benchmark problem using our mooring system module. 

Strong nonlinearities are evident in the force-displacement relationships of most mooring systems. Since these 

nonlinearities may be important in the dynamic response of floating offshore wind turbines, we must check to ensure 

that our quasi-static mooring system module is computing them correctly. 

NAME used an in-house MathCAD workbook to design the mooring system for the ITI Energy barge. Their 

model accounts for homogenous taut or catenary lines with elastic horizontal (but not vertical) stretching. A portion 

of a line may rest on the seabed in NAME’s model, but seabed friction was not accounted for. Even though this 

model was simpler than ours, comparing our results with NAME’s enables us to verify our analysis module in its 

form with seabed interaction. 

The layout of the mooring system for the ITI Energy barge is discussed in section IIIB. NAME computed the 

force-displacement relationships for surge motions of the barge. The relationships were computed for each line 

independently as well as opposing lines jointly. To reproduce NAME’s results, we built a model of the barge and 

mooring system in ADAMS and translated the barge in surge through a time marching simulation. The timedependent motion of the barge does not affect the results of this analysis, because the mooring lines are treated 

quasi-statically in our model. 

As in previous verification tests, the results from this exercise compare very well. Because the agreement is so 

good, again, only our set of results is presented, as shown in Fig. 11. There is a horizontal tension of about 100 kN 

-2,000

0

2,000

4,000

6,000

8,000

-60 -30 0 30

Platform Surge, m

Tension/Force, kN

Horiz. Tension of Upwind Line

Horiz. Tension of Downwind Line

Net Horiz. Restraining Force on Barge

Figure 11. Nonlinear force-displacement relationships for the mooring system of the ITI Energy barge. 

16
in each line when the barge is in its neutral position. The force-displacement curve for opposing lines, which 

represents the net horizontal restraining force, remains fairly linear between +20 m and −20 m of surge motion. 

Beyond a surge displacement of about 40 m, the resistance of the mooring system increases dramatically. At 50 m 

of surge displacement, the horizontal tension in each line is greater than 1,000 kN. 

C. Time Domain Versus Frequency Domain Verification 

Since our fully coupled aero-hydro-servo-elastic simulation tool is the first of its kind, finding independent 

model results to use for verification is difficult. The time domain models that have been developed and used by 

others in the past for analyzing floating offshore wind turbines11-13 were not rigorous enough to provide sufficient 

verification data. Most of the previous studies on floating offshore wind turbines used frequency domain models.6-10 

We can use the results of a frequency domain analysis to verify our simulation tool because the hydrodynamic 

theory in our module was derived from the time domain representation of the frequency domain problem.20 Two 

such verifications are presented. 

Frequency domain solutions describe the sinusoidal steady-state response of the support platform, which arise 

from the interaction of the body with incident waves that propagate at a single amplitude, frequency, and direction. 

The solution to the frequency domain problem is generally given in terms of RAOs, which are the complex-valued 

amplitudes of motions for each DOF of the support platform, normalized per unit wave amplitude. Imaginary 

components indicate that the response is out of phase with the wave elevation. In a time domain model, the 

sinusoidal steady-state response of a floating offshore wind turbine system can be found by introducing regular, 

periodic waves as forcing functions, and simulating in time long enough to ensure all transient behavior has died 

out. As a first verification of our fully coupled model, we used such time series simulations to back-out the RAOs at 

discrete frequencies. (The process was repeated to find the RAOs at each desired frequency.) We use the frequency 

domain results for the MIT/NREL SDB presented by Wayman9

 for this verification test. 

In the frequency domain problem, the response of the system to stochastic sea states can only be characterized 

statistically since the frequency domain representation is not valid for transient analysis. Specifically, the motion of 

a linearized floating offshore wind turbine system will have a response that is Gaussian distributed when it is excited 

by a sea state whose instantaneous wave elevation is Gaussian distributed. (Stochastic sea states are, in general, 

Gaussian distributed.) The standard deviations of the motion response are dictated by the Wiener-Khinchine 

theorem.9

 In a time domain model, the distribution of the motion response can be ascertained by post-processing the 

output of a series of simulations that are long enough to ensure statistical reliability in the results. (The process can 

be repeated to find the distribution at each desired sea state.) We used this procedure as a second verification of our 

fully coupled, time domain model, again using the frequency domain results for the MIT/NREL SDB presented by 

Wayman9

 for comparison. 

For these verification tests, we used the NREL offshore 5-MW baseline wind turbine installed on the MIT/NREL 

SDB. We chose this configuration and Wayman’s frequency domain results because that was the only configuration 

and only study documented with enough information for us to build a system model and compare results for all six 

platform modes of motion. Wayman used WAMIT to compute the frequency domain hydrodynamic properties of 

the MIT/NREL SDB. She modeled the spread mooring system described in section IIIC with linear restoring only 

in the surge and sway DOFs. She used LINES35 to find the linear restoring coefficients of 4 MN/m. The attributes 

of the wind turbine were included in her linearized system model by augmenting the body mass matrix with the 

mass properties of the turbine and by augmenting the hydrodynamic damping and restoring matrices with damping 

and restoring contributions from rotor aerodynamics and gyroscopics. Wayman ignored the elasticity of the wind 

turbine and considered only the six rigid body modes of the SDB.9

1. Verification with Steady-State Response 

For this comparison, we constructed a FAST model of the NREL offshore 5-MW baseline wind turbine installed 

on the MIT/NREL SDB. To ensure reasonable similarity to Wayman’s model and to isolate the behavior of the 

hydrodynamics and mooring system, we modeled the turbine without any DOFs or a control system (only the six 

rigid body DOFs of the platform were included). For environmental conditions, we used a constant, 11.2-m/s 

unsheared wind and regular, periodic waves of unit amplitude (a peak-to-peak height of 2 m). Both the wind and 

waves were codirectional and aligned with the surge coordinate. 

When we first attempted to run our time domain simulations, we modeled the spread mooring system with our 

quasi-static mooring system module interfaced to FAST. However, we soon discovered that the nonlinear restoring 

of the spread mooring system prohibited the response from ever reaching a sinusoidal steady-state condition, which 

eliminated any possibility of backing out the RAOs. To get around this, we decided to remove the interface to our 

mooring module and instead model the mooring system as Wayman did with linear restoring coefficients, only in 

surge and sway. Thus, the results presented next are not useful for verifying the time domain implementation of our 

17
mooring system module. However, they are still useful for verifying the time domain implementation of our 

hydrodynamics module. 

With the linearized mooring system model, we ran a series of 2000-s simulations to give them time to reach a 

periodic steady state. Even after all that time, the platform motion was still not perfectly sinusoidal for the sway, 

roll, and yaw responses. We ran 10 simulations and varied the discrete frequency of the waves from 0.15 to 1.05 

rad/s in even increments. Using the last cycle from each simulation, we computed the amplitudes of the oscillations 

for the three translational and three rotational platform responses. Since our waves were unit amplitude, these 

response amplitudes were the magnitudes of the RAOs. For the rotational responses, we normalized the RAOs by 

the platform radius (18 m), as was done by Wayman. We added our results to the nondimensional RAO plots that 

Wayman had generated. In these tests, we did not compare the phases of the response. 

As seen in Fig. 12, our time domain predictions closely mimic those from Wayman’s frequency domain analysis 

for the platform surge and heave modes. This gives us confidence that our time domain implementation of the 

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

Frequency, rad/s

Platform Surge RAO

Freq. Domain

Time Domain

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

Frequency, rad/s

Platform Roll RAO

Freq. Domain

Time Domain

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

Frequency, rad/s

Platform Sway RAO

Freq. Domain

Time Domain

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

Frequency, rad/s

Platform Pitch RAO

Freq. Domain

Time Domain

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

Frequency, rad/s

Platform Heave RAO

Freq. Domain

Time Domain

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0

Frequency, rad/s

Platform Yaw RAO

Freq. Domain

Time Domain

Figure 12. RAO comparisons for the MIT/NREL SDB. 

18
platform hydrodynamics was correct. The platform pitch curves seem to have a similar character, but portions differ 

in both magnitude and frequency. The other three parameters—sway, roll, and yaw—have such small responses that 

comparison is difficult. The fact that the oscillations of these modes had not completely become sinusoidal after 

2000 s brings into question the meaningfulness of those comparisons. Even though there is no excitation of the 

platform yaw mode from aerodynamics or hydrodynamics in this configuration, the yaw response is nonzero 

because the spinning inertia of the rotor, combined with the pitching motion of the platform, induces a gyroscopic 

yaw moment. 

We believe that the differences in the pitch RAO are caused by the variation between our models for the 

aerodynamic damping in pitch. Wayman showed that the platform damping in pitch is completely dominated by 

rotor aerodynamics, not by wave radiation (see Appendix A.1 of Ref. 9). This is not true for the other modes of 

motion, such as surge and heave. In Wayman’s analysis, the aerodynamic damping in pitch was constant (it was 

derived by linearizing the rotor aerodynamics about the mean pitch orientation of the platform). In our analysis, the 

aerodynamic damping in pitch varies as the turbine oscillates against and with the wind. 

2. Verification with Stochastic Response 

To verify the stochastic response, we built three FAST models of the NREL offshore 5-MW baseline wind 

turbine installed on the MIT/NREL SDB. The first was the same one used in the RAO comparison: it had a rigid 

turbine, no control system, and linearized mooring lines in surge and sway. For the second model, we swapped the 

linearized mooring line model with the standard interface between FAST and our quasi-static mooring system 

module. To see how well these simpler models agree with higher fidelity simulations, for the third model, we 

replaced the rigid turbine model with a fully flexible one and enabled the variable-speed generator torque and blade 

pitch control system. 

The published results9

 of Wayman’s frequency domain study included mean and standard deviations of the 

normally distributed responses at a variety of sea states, wind speeds, and water depths. We chose to make a 

comparison with all three of our models for only one of these cases. The case we chose uses winds roughly at rated 

(11.2 m/s), a water depth of 200 m, and a Pierson-Moskowitz wave spectrum with a significant wave height, Hs, of 

5.49 m and a mean wave period of 11.3 s, which corresponds to a peak spectral period of about 14.6 s or a peak 

spectral frequency of about 0.429 rad/s. We used steady, unsheared winds in the first two models, but for the third 

model with an active control system, we used turbulent, sheared winds, with a mean hub-height speed of 11.2 m/s. 

As before, the wind and waves were codirectional and aligned with the surge coordinate. 

For each model, we computed the probability mass densities for all but the first 30 s of a series of four 10,000-s 

simulations (just shy of 3 h each) and used different random seeds for the stochastic waves. We constructed an 

aggregate of the four cases before computing the probability masses. We plotted our resulting histograms against 

the Normal probability density functions derived from Wayman’s frequency domain analysis. We had to make one 

correction to the results published by Wayman.9

 We discovered that when Wayman computed the standard 

deviations of motion for the rotational modes of the platform, she incorrectly dimensionalized her results. To 

correct for this mistake, all the standard deviations of motion she presented for the rotational modes must be scaled 

up by a factor of 180 / π to reach the values she meant to publish. The results presented below account for this 

correction. 

Figure 13 presents the comparison between our time domain results and Wayman’s frequency domain results. 

The differences between the results of our second and third models were much smaller than the changes brought 

about by the switch to nonlinear mooring lines, so only the results from the first and third models are shown. As 

with the RAOs, the surge and heave predictions for our model with the linearized mooring lines agree very well. 

The spread for the pitch response is narrower for the FAST simulation with the linearized mooring system than it is 

in Wayman’s predictions. This is consistent with what we saw with the pitch RAO comparison: Wayman’s RAO 

was greater at 0.429 rad/s than the magnitude predicted by FAST. 

After introducing our nonlinear mooring system module in the FAST simulations, the mean surge, pitch, and 

heave responses decreased considerably as shown. This is because once the lines go taut, the stiffness increases 

dramatically and the mooring system essentially acts as a four bar linkage. This keeps the platform from rising as 

high or from traveling as far downwind. The thrust on the rotor tries to pitch the turbine downwind, but the much 

stiffer upwind lines prevent the upwind end of the barge from lifting so far out of the water; the platform, in turn, is 

pushed slightly upwind. The spread of values for the sway, roll, and yaw is also much greater for the higher fidelity 

model than for the simpler model. This is because there is more coupling in the system. 

Though not shown, we also developed a histogram for the instantaneous wave elevation record computed by, 

and output from, our simulation tool. As expected, this histogram is Gaussian distributed with zero mean, and a 

standard deviation for this test case of Hs/4 = 1.37 m. 

19
0.0

0.1

0.2

0.3

-8 -4 0 4

Platform Surge, m

Probability Mass/Density, 1/m

Rig, Lin Lines

Flex, Cont, Nonlin Lines

Freq Domain

0.0

0.2

0.4

0.6

0.8

-40 -20 0 20

Platform Roll, deg

Probability Mass/Density, 1/deg

Rig, Lin Lines

Flex, Cont, Nonlin Lines

Freq Domain

0.00

0.05

0.10

0.15

-20 -10 0 10

Platform Pitch, deg

Probability Mass/Density, 1/deg

Rig, Lin Lines

Flex, Cont, Nonlin Lines

Freq Domain

0

5

10

15

20

-8 -4 0 4

Platform Sway, m

Probability Mass/Density, 1/m

Rig, Lin Lines

Flex, Cont, Nonlin Lines

Freq Domain

0.0

0.2

0.4

0.6

-30 -15 0 15

Platform Yaw, deg

Probability Mass/Density, 1/deg

Rig, Lin Lines

Flex, Cont, Nonlin Lines

Freq Domain

0.0

0.1

0.2

0.3

0.4

0.5

-6 -3 0 3

Platform Heave, m

Probability Mass/Density, 1/m

Rig, Lin Lines

Flex, Cont, Nonlin Lines

Freq Domain

Figure 13. Comparison of frequency domain-generated probability densities to time domain-generated 

probability masses. 

V. Demonstration of Simulation Capability for Full Coupled Loads Analysis 

To give examples of what can be done with our newly developed simulation capability with regards to loads 

analysis, we show results from two simulations of the NREL offshore 5-MW baseline turbine mounted on the 

preliminary ITI Energy barge. The examples demonstrate the capability of our simulation tools, and the potential 

challenges in the design of floating offshore wind turbines. 

As will be shown, there are differences between the FAST and ADAMS predictions. We believe these are 

mostly caused by the greater fidelity of the ADAMS simulator, which includes torsion and mass offsets in the 

blades. The blade torsion DOFs allow the blades to twist, which has an effect similar to pitching the outboard 

portion of the blades. This reduces the amount the blade control system has to pitch the blades. In general, the 

20
blade pitch values are smaller for ADAMS than for FAST because the control system compensates for the blade 

twist when regulating speed in Region 3 (above rated). 

A. Normal Operation 

The first simulation was for normal operation in 18-m/s stochastic winds and irregular waves with a 3.3-m 

significant wave height and an 11.3-s peak spectral period. The incident waves were misaligned with the mean wind 

direction and nacelle yaw angle by 20°. The first chart of Fig. 14 shows that the waves cause the barge to pitch back 

and forth. This causes a large translation of the rotor, which results in an oscillating inflow to the wind turbine rotor. 

As the platform pitches downwind (positive slope), the rotor’s relative wind speed decreases, which causes the 

aerodynamic torque to drop and the control system responds by driving the pitch toward zero. So interestingly, even 

when the wind speed is significantly above rated, there are still three short periods of Region 2 (below rated) 

operation. Also, as the aerodynamic torque drops, there is a mismatch with the generator torque, so the rotor speed 

decreases. The rotor speed is 90° out of phase with the barge pitch and exhibits much more speed variation than one 

would see on a land-based wind turbine. 

In the second chart of Fig. 14, the tension in the upwind fairlead is lower from FAST than from ADAMS. This 

is due to the elastic twist DOFs that only the ADAMS model has. Although the control system compensates for the 

twist by commanding a lower pitch, the control system was not trying to hold the thrust constant. This results in a 

higher thrust prediction from ADAMS, which then increases the downwind surge of the platform, and also the 

tension in the upwind fairlead. 

-10

0

10

20

30

0 20 40 60 80

Time, s

ADAMS, Blade 1 Pitch, deg FAST, Blade 1 Pitch, deg

ADAMS, Rotor Speed, rpm FAST, Rotor Speed, rpm

ADAMS, Platform Pitch, deg FAST, Platform Pitch, deg

Region 2

0

100

200

300

400

500

600

700

800

0 20 40 60 80

Time, s

Fairlead Tension, kN

ADAMS, Downwind

ADAMS, Upwind

FAST, Downwind

FAST, Upwind

Figure 14. System response during normal operation at 18 m/s mean wind speed. 

B. Extreme Coherent Gust with Direction Change 

The second simulation was of the International Electrotechnical Commission’s (IEC’s) standard Extreme 

Coherent Gust with Direction Change load case. The 15-m/s gust was added to a steady 10-m/s wind concurrently 

21
with a direction change of 72°. The event started at 60 s and took 10 s to reach the maximum values, where they 

remained. We used irregular waves with a significant wave height of 2.1 m and a peak spectral period of 9.6 s in 

this simulation. Before the event, the incident waves were misaligned with the mean wind direction and nacelle yaw 

angle by 20°, as in the previous example. 

We show three charts from this simulation in Fig. 15. Of note is the platform yaw in the middle chart, which 

shows that the platform begins to yaw slightly into the wind after the wind direction change. There is still a 64°

nacelle yaw error at 90 s into the simulation, but the mooring lines eventually restrain the platform from yawing any 

farther. One can also see the phase difference between the FAST and ADAMS results for the oscillating out-of-

-2

0

2

4

6

8

10

50 60 70 80 90

Time, s

Blade 1 Tip Deflection, m

ADAMS, Out-of-Plane Defl

ADAMS, In-Plane Defl

FAST, Out-of-Plane Defl

FAST, In-Plane Defl

Start of Event

-10

-8

-6

-4

-2

0

2

50 60 70 80 90

Time, s

Platform Yaw, deg

ADAMS

FAST

Start of Event

-3

-2

-1

0

1

2

3

50 60 70 80 90

Time, s

Nacelle Acceleration, m/s2

ADAMS, Fore-Aft FAST, Fore-Aft

ADAMS, Lateral FAST, Lateral

ADAMS, Vertical FAST, Vertical Start of Event

Figure 15. System response during an extreme coherent gust with direction change event. 

22
plane and in-plane deflection parameters. This shift is likely due to the way ADAMS models the blades differently 

than FAST. The nacelle acceleration at the main shaft bearing remains within a third of a g during the entire event. 

VI. Conclusion 

Limitations of previous time and frequency domain studies on floating offshore wind turbines motivated our 

development of simulation capability for modeling the fully coupled aero-hydro-servo-elastic response of such 

systems. This capability has been developed by combining the computational methodologies of the onshore wind 

turbine and offshore oil and gas industries. The aero-servo-elastic onshore wind turbine simulation capability of 

FAST and ADAMS with AeroDyn has been interfaced with the hydrodynamic wave-body interaction program 

WAMIT. This interface has been supported by the development of modules for treating time domain 

hydrodynamics and quasi-static mooring system responses. To be useful for studying the technical feasibility of 

multiple floating offshore wind turbine concepts, the developed simulation tool is universal enough to analyze a 

variety of wind turbine, support platform, and mooring system configurations. 

Through model-to-model comparisons, we tested our newly developed simulation capability to ensure its 

correctness. We showed that WAMIT produces acceptable input for our hydrodynamics module, and from this 

hydrodynamic input, we showed that our module correctly generates the radiation impulse response functions. We 

showed that our quasi-static mooring system module correctly solves a classic benchmark problem for the static 

equilibrium of a suspended cable structure. Moreover, we showed that our mooring system module predicts 

nonlinear force-displacement relationships consistent with an independent analysis. Finally, the results from our 

fully coupled time domain analysis were shown to agree with results generated from a frequency domain approach. 

The results of all of the verification exercises were favorable and give us confidence to pursue more thorough 

investigations into the behavior of floating offshore wind turbines. 

We presented sample simulation results that demonstrate the capability of our simulation tools and highlighted 

potential challenges to the design of floating offshore wind turbines. 

Future Work 

We plan to use the simulation capabilities described in this work to perform loads analyses on a few of the 

promising floating offshore support platform configurations. Our first loads analysis will be for the NREL offshore 

5-MW baseline wind turbine mounted on the ITI Energy barge. The results will help identify critical loads and 

instabilities brought about, in contrast to onshore wind turbines, by the dynamic couplings between and within the 

turbine and support platform in the presence of combined wind and wave loading. We will assess the critical loads 

and instabilities to identify the technical and economic feasibility of the various system concepts and to determine 

areas where advanced controls development can be used to improve the coupled system dynamic response. 

Additional code enhancements to improve the simulation of floating offshore wind turbines are possible. For 

example, we would like to introduce second-order effects into our hydrodynamics module, including the effects of 

intermittent wetting and mean-drift, slow-drift, and sum-frequency excitation, which are necessary for accurate 

modeling of TLP designs and in the analysis of general support platforms subject to large and/or steep waves. We 

would also like to replace our quasi-static mooring system module with a fully coupled module that can handle the 

dynamics of the lines. Though not specific to the modeling of offshore wind turbines, we also plan to add a torsion 

DOF to the modal representation of the tower in FAST and to extend the modal representation of the blades to 

include mass and elastic offsets, torsion DOFs, and coupled mode shape properties. 

Acknowledgments 

We would like to thank everyone who helped with this work, including Elizabeth Wayman of MIT for designing 

and analyzing the MIT/NREL SDB and Nigel Barltrop and Willem Vijhuizen of NAME for designing and analyzing 

the ITI Energy barge. We also thank Walt Musial and Sandy Butterfield of NREL for leading the offshore wind 

energy program there; Jason Jonkman’s Ph.D. committee members at the University of Colorado at Boulder, the 

University of Wyoming, NREL, and MIT for evaluating the project; and Kathleen O’Dell and Stefanie Woodward 

of NREL, for editing this paper to make it much more readable. 

This work was performed at NREL in support of the U.S. Department of Energy under contract number DEAC36-99-GO10337 and in support of a Cooperative Research and Development Agreement with ITI Energy, 

number CRD-06-178. 

23
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25
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DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Development and Verification of a Fully Coupled Simulator for 

Offshore Wind Turbines: Preprint 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/CP-500-40979 

5e. TASK NUMBER

WER7.2902 

6. AUTHOR(S)

J.M. Jonkman and M.L. Buhl, Jr. 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/CP-500-40979 

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

This report outlines the development of an analysis tool capable of analyzing a variety of wind turbine, support 

platform, and mooring system configurations. The simulation capability was tested by model-to-model comparisons to 

ensure its correctness. 

15. SUBJECT TERMS

wind energy; offshore wind turbines; simulation tools; computer modeling tools 

16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified

17. LIMITATION 

OF ABSTRACT

UL 

18. NUMBER 

OF PAGES 

19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18