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Contract No. DE-AC36-08GO28308

Simulation-Length 

Requirements in the Loads 

Analysis of Offshore Floating 

Wind Turbines

Preprint

Lorenz Haid and Denis Matha

University of Stuttgart

Gordon Stewart and Matthew Lackner

University of Massachusetts

Jason Jonkman and Amy Robertson

National Renewable Energy Laboratory

Presented at the ASME 2013 32nd International Conference on 

Ocean, Offshore and Arctic Engineering

Nantes, France

June 9-14, 2013

Conference Paper

NREL/CP-5000-58153

June 2013
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SIMULATION-LENGTH REQUIREMENTS IN THE LOADS ANALYSIS OF 

OFFSHORE FLOATING WIND TURBINES

Lorenz Haid

Endowed Chair of Wind Energy,

University of Stuttgart (SWE)

Stuttgart, Germany

Gordon Stewart

University of Massachusetts

Amherst, MA, USA

Jason Jonkman and

Amy Robertson 

National Renewable Energy 

Laboratory (NREL)

Golden, CO, USA

Matthew Lackner

University of Massachusetts

Amherst, MA, USA

Denis Matha

Endowed Chair of Wind Energy, 

University of Stuttgart (SWE)

Stuttgart, Germany

ABSTRACT

The design standard typically used for offshore wind 

system development, the International Electrotechnical 

Commission (IEC) 61400-3 fixed-bottom offshore design 

standard, explicitly states that “the design requirements 

specified in this standard are not necessarily sufficient to ensure 

the engineering integrity of floating offshore wind turbines” 

[1]. One major concern is the prescribed simulation length time 

of 10 minutes for a loads-analysis procedure, which is also 

typically used for land-based turbines. Because floating 

platforms have lower natural frequencies, which lead to fewer 

load cycles over a given period of time, and ocean waves have 

lower characteristic frequencies than wind turbulence, the 10-

min simulation length recommended by the current standards 

for land-based and offshore turbines may be too short for 

combined wind and wave loading of floating offshore wind 

turbines (FOWTs). Therefore, the goal of this paper is to 

examine the appropriate length of a FOWT simulation—a 

fundamental question that needs to be answered to develop 

design requirements.

To examine this issue, we performed a loads analysis of an 

example FOWT with varying simulation lengths, using FAST, 

the National Renewable Energy Laboratory’s (NREL’s) 

nonlinear aero-hydro-servo-elastic simulation tool. The 

offshore wind system used was the OC3-Hywind spar buoy, 

which was developed for use in the International Energy 

Agency (IEA) Offshore Code Comparison Collaborative (OC3) 

project, and supports NREL’s offshore 5-MW baseline turbine. 

Realistic metocean data from the National Oceanic and 

Atmospheric Administration (NOAA) and repeated periodic 

wind files were used to excite the structure. The results of the 

analysis clearly show that loads do not increase for longer 

simulations. In regard to fatigue, a sensitivity analysis shows 

that the procedure used for counting half cycles is more 

important than the simulation length itself. Based on these 

results, neither the simulation length nor the periodic wind files 

affect response statistics and loads for FOWTs (at least for the 

spar studied here); a result in contrast to the offshore oil and gas 

(O&G) industry, where running simulations of at least 3 hours 

in length is common practice.

1. INTRODUCTION

To ensure the structural integrity and stability of a wind 

turbine system, standards and guidelines prescribe a loadsanalysis procedure that uses system-dynamics models to 

determine the extreme and fatigue loads expected over the 

system’s lifetime. IEC offers two standards for that process—

one for land-based wind turbines [2] and one for fixed-bottom 

offshore wind turbines [1]. Both assume that the structures 

supporting the rotor-nacelle assembly are fixed to the ground 

(or sea floor). Those standards explicitly state that the current 

design requirements may not be sufficient for FOWTs, due to

the lower natural frequencies of FOWTs, which lead to fewer 

load cycles over a given period of time, and the lower 

characteristic frequencies of ocean waves compared to wind 

turbulence. Therefore, the loads subgroup (Group 2) of the IEC 

Working Group (WG) 3-2, tasked with developing FOWT 

design requirements, has proposed a research project aimed at 

developing a loads-analysis procedure sufficient to ensure the 

engineering integrity of FOWTs. 
2 

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The work summarized in this paper outlines the first part of 

this overall project to modify existing design-load cases 

(DLCs) and/or develop entirely new DLCs for FOWTs. The 

project goals are to evaluate two principle questions: what is

the required simulation length, and what is the method for 

incorporating metocean conditions from an offshore site in the

loads-analysis process of a FOWT. In this paper, we address the 

first of these issues—the required length of FOWT simulations. 

To answer this question, we performed two separate studies to

examine the dependence on simulation length of loads due to

aerodynamics and loads due to hydrodynamics/floating 

platform motions.

For the simulations presented in this paper, we used the 

OC3-Hywind spar buoy [4], which was developed for use in the 

International Energy Agency (IEA) Offshore Code Comparison 

Collaborative (OC3) project, and supports NREL’s offshore 5-

MW baseline turbine [3]. [4]. Realistic metocean conditions 

were generated by analyzing National Oceanic and 

Atmospheric Administration (NOAA) buoy data, concatenated

into a representative U.S. East Coast site. The expected 

significant wave height and peak-spectral period, dependent on 

the wind speed, were used. Turbulent wind speed data were 

produced using TurbSim [9], which is able to generate periodic 

random turbulent wind fields that can be repeated to generate 

wind files of longer length.

To investigate the dependence of aerodynamically derived 

loads on simulation length, we compared loads statistics from

10x1-hour, 12x50-min, 15x40-min, 20x30-min, 30x20-min, and 

60x10-min FAST [5] simulations. A land-based turbine was 

used for this study to ensure a comparison based on only 

aerodynamic loads. To investigate the dependence on 

simulation length of loads due to hydrodynamics and floating 

platform motion, we evaluated loads statistics from simulations 

with a length of 10 minutes, 20 minutes, 1 hour, 3 hours, and 6 

hours, with the numbers of independent wave and wind seeds 

chosen to yield the same amount of random information in each 

group of simulations.

After these two studies were performed, we investigated 

the minimum requirements for a statistically significant 

ultimate load calculation. So far, the standard recommends “in 

general, at least six 10-min stochastic realizations” [1, 2], but it 

is well known that this minimum number is not always 

sufficient. 

2. LOADS-ANALYSIS APPROACH

To examine the influence of simulation length on the 

resulting loads observed in a FOWT, we conducted a series of 

load analyses, each with varying simulation length. The overall 

procedure for these load analyses consisted of the following 

eight steps: 

1. Identify a suitable FOWT to analyze: This paper used the 

platform model that can most accurately be modeled in the 

present version of FAST, the OC3-Hywind spar buoy with the 

NREL 5-MW turbine. This model was identified as the most 

suitable because second-order hydrodynamics, which have not 

yet been incorporated into FAST at the start of the project, have 

been shown to have little influence on the OC3-Hywind system 

behavior during normal operation (whereas second-order 

hydrodynamics are more important in semisubmersibles and 

tension-leg platforms) [17]. 

2. Identify load-case simulations that address the simulationlength questions best: In this study, we focused on the gap of 

knowledge regarding operational loads. A comparison to loads 

from extreme wind and waves under parked/idling conditions 

will be made in the future.

3. Adapt the nonlinear aero-hydro-servo-elastic tool (FAST), 

the script to run the simulations, and the needed postprocessing scripts to perform the loads-analysis process. 

Modifications were needed because simulations of more than 3

hours have not been performed with these tools and scripts 

before.

4. Perform an initial analysis to examine the needed wind grid 

size and initial conditions to minimize transient start-up time.

5. Identify metocean data to use: We used NOAA data buoys to 

define joint probability distributions of the parameters: mean 

wind speed at hub height, wind-wave misalignment angle, 

significant wave height, and wave peak-spectral period. Three 

ocean sites representative of the U.S. East Coast, Gulf of 

Mexico, and West Coast were developed to ensure realistic 

conditions for the simulations—but only the U.S. East Coast 

site was used in this project so far. 

6. Using a full-system dynamics model in FAST, run a series of 

load simulations in groups with increasing simulation length 

and calculate the ultimate loads and fatigue loads expected over 

the lifetime of the wind turbine system based on these 

simulations. 

7. Compare the load results from the different simulation 

groups (with increasing length) to the 10-min simulations 

(standard), and identify any differences in the ultimate loads 

and/or fatigue loads: The influence of simulation length on both 

aerodynamically derived loads and loads due to hydrodynamics 

and floating platform motions was determined. 

8. Investigate the number of independent simulations (random 

seeds) required for convergence of the ultimate load statistics, a 

topic intrinsically related to the simulation length. 

The paper is organized as follows. The floating platform 

model (Step 1) and simulation tools (Steps 3) are described in 

Section 3. Section 4 presents the DLCs (Step 2), initial analyses 

performed (Step 4), and metocean data used in this project 

(Step 5). The results of Steps 6, 7, and 8 are presented in 

Section 5. 
3 

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3. SIMULATION TOOLS AND MODELS

This section describes the simulation tools and floating 

wind turbine model used. 

3.1 Simulation Tool Capabilities

This work utilized the models and simulation tools 

developed by NREL. The time-marching simulations were 

performed with FAST [5], which is coupled with the rotor 

aerodynamics module, AeroDyn [6], and the platform 

hydrodynamics module, HydroDyn [7, 8]. TurbSim [9] was 

used to generate the random full-field turbulent wind fields. We 

used a modified version of the NREL internal perl-script

RunIEC to submit the required simulations to the NREL’s

Windows-based high-performance computing server cluster 

(HPC) with 240 cores and 2 GB of RAM per core. The outputs 

from the aero-elastic simulations in FAST were analyzed with 

the post-processing tools MExtremes [10] for the ultimate loads 

and MLife [11] for the fatigue loads. We used MLife to 

calculate the fatigue damage and the fatigue damage-equivalent 

loads (DELs) over the lifetime of the turbine. Binary FAST 

output files were used as a necessity for simulations longer than 

1 hour.

3.2 Floating Wind Turbine Model

In this study, we used the NREL offshore 5-MW reference 

wind turbine, a “representative utility-scale, multimegawatt 

turbine” [3] for offshore system development. Equipped with 

variable-speed and collective-pitch control, the machine is rated 

at 5 MW at a wind speed of 11.4 m/s. The turbine has a hub 

height of 90 m and a rotor diameter of 126 m, and is supported 

by the OC3-Hywind spar-buoy floating platform in this study. 

The platform model is based on the spar-buoy concept 

“Hywind,” developed in Norway by StatoilHydro (now called 

“Statoil ASA”). Compared to the Hywind concept, the OC3-

Hywind spar buoy is suitable for public research activity and is 

able to support the NREL 5-MW turbine. The OC3 Hywind

platform model has been selected as the initial platform for this 

project because it can be accurately modeled with the existing 

capabilities of FAST. Thus, the focus can be on the simulationlength question and not on simulation inaccuracy. 

4. ANALYSIS SPECIFICATIONS

In this section, we describe the details of the simulations

that were performed. 

4.1 Initial Analysis

It is important to minimize the effects of the initial

simulation transients on the resulting load statistics. The IEC 

standards recommend that “the first 5 s (or longer if necessary) 

shall be eliminated” to reduce the impact of initial transients [1,

2]. With the long natural periods of the support structure, 

simulations may need more than 10 minutes for start-up 

transients to damp out. We conducted simulations to determine

initial conditions (ICs) that minimized the transients and 

allowed for shorter simulation times. Our analyses determined

that the initial simulation time impacted by transients, which 

should be ignored, is at least 60 seconds when proper ICs are 

used for rotor speed, blade pitch, out-of-plane blade deflection, 

and platform surge, pitch and heave displacements. 

Furthermore, the proper ICs must be chosen specifically for the 

given wind and wave conditions. 

4.2 Periodic Wind Files

To examine the required simulation length for a FOWT, 

long simulations are required, with a maximum length of 6 

hours. Due to the size of the NREL 5-MW turbine and expected 

range of motion when installed on a floating platform, full-field 

turbulent wind files of sufficient size and resolution require 

large amounts of memory to generate and store. A single 10-

min wind file of appropriate size and resolution requires about 

155 MB of storage space, whereas a 6-hour wind file would 

require about 5.6 GB. The entire wind file must be stored in 

memory upon execution of a FAST simulation, and so wind 

files larger than 1 hour are not feasible on most computers (a 

32-bit Windows operating system has a 2-GB limit on RAM). 

Our solution was to use repeated periodic wind files for 

simulations longer than 10 minutes. Using periodic wind files 

also ensures that the total length of stochastic wind is the same 

for the varying length simulations (see Section 4.3). TurbSim 

was modified to output a periodic wind file, comprised of 

repeated shorter wind files. For example, a 1-hour simulation 

can be run successfully by repeating six identical 10-min wind 

files. Due to the inverse FFT method used in TurbSim, the 

resulting time series is periodic with a period equal to the 

length of the wind file. The periodicity means that there are no 

discontinuities in concatenating wind files together, as shown in 

Figure 1.

Figure 1. Two repeated periodic time histories of a 30-s wind 

realization. 

We calculated a grid height of 179.26 m and a grid width 

of 248.26 m for our simulations, based on the rotor diameter of 
4 

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the turbine and the maximum expected value of the platform 

motions in the surge and heave directions (estimated from a set 

of trial simulations), as well as an additional safety factor of 

15%. We used the maximum surge value instead of the 

maximum sway value, because it was larger than the maximum 

sway values in all initial simulations. With the use of a periodic

10-min turbulent wind file, a grid discretization of 40x55 grid 

points with a spacing of 4.6 m (roughly equivalent to the

average blade chord) in the vertical and horizontal directions 

was realized. 

4.3 Reference Metocean Data

We created a database of realistic metocean input 

conditions for use in this and future studies concerning the 

design standards for FOWTs, using data from the NOAA 

floating data buoys. As upcoming studies with this metocean 

dataset will investigate the effect of wind and wave 

misalignment, buoy data with wind and wave directionality was 

needed, which limited the number of available sites. Data from 

sites on the East and West coasts of the United States and the 

Gulf of Mexico were downloaded from the NOAA website 

(http://www.ndbc.noaa.gov/) and were post-processed to 

remove measurement errors in the data. Figure 2 shows the 

geographical distribution, mean wind speeds, and mean 

significant wave heights of the sites.

Figure 2. Geographical distribution of mean wind speeds and 

wave heights.

Using the data from these sites, we created conditional 

probability functions for four variables: wind speed, significant 

wave height, wave peak-spectral period, and wind/wave 

misalignment angle. Wind speed is treated as independent, and 

an unconditioned 2-parameter Weibull distribution was used. 

Wind/wave misalignment angle is conditioned on wind speed 

alone, significant wave height is conditioned on wind speed and 

misalignment angle, and peak-spectral period is conditioned on 

wind speed and significant wave height. The peak-spectral 

period and significant wave height are defined by 2-parameter 

gamma distributions, and a Von-Mises distribution (also known 

as a circular normal distribution) is used for wind/wave 

misalignment. These analytical distributions were chosen based 

on their ability to fit the empirical measured data.

The sites were divided into groups based on geographical 

region, and the conditional probability distributions from each 

site within the regions were averaged to create three 

representative sites. The results presented in this paper used 

data from the averaged East Coast site, and the simulations 

used the expected value of significant wave height and median 

peak-spectral period for each mean wind speed for zero degree 

wind/wave misalignment, as shown in Table 1. 

Table 1. Expected wave parameter values for the U.S. East 

Coast site.

4.4 Load Cases and Simulations

Our research focus in this study was on DLCs 1.1 and 1.2 

from the IEC 61400-3 offshore wind turbine design standard. If 

the loads of a floating wind turbine system are dictated by 

extreme wind and waves when the turbine is parked/idling 

(non-operating), then existing standards may be applicable. But 

it is unclear upfront whether the loads on a given FOWT are

dictated by operational or parked/idling conditions. This study 

focused on the gap of knowledge regarding operational loads.

In our first study, to investigate the dependence of 

aerodynamically derived loads on simulation length, we ran

simulations with increasing length using the land-based system 

of the 5-MW baseline turbine, to eliminate the effects of 

hydrodynamics and platform motion. For three mean wind 

speeds: 8 m/s, 11.4 m/s (rated), and 18 m/s, we ran 10 separate 

1-hour, 12 50-min, 15 40-min, 20 30-min, 30 20-min, and 60 

10-min simulations, so that the same total amount (10 hours) of 

random information existed in each group of simulations. Each 

separate simulation was run with a different wind file, each

with a unique random seed. Periodic wind files were not used 

for these simulations because a smaller wind grid (150 m x 150 

m grid with 13x13 grid resolution) could be used for the landbased version, which has less motion than with the floating 

system, and because a more coarse resolution was deemed 

sufficient for the purposes of this study.
5 

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In our second study, to investigate the dependence on 

simulation length of loads due to hydrodynamics and floating 

platform motion, we ran DLC 1.1 with five different simulation 

lengths: 6 hours, 3 hours, 1 hour, 20 minutes, and 10 minutes

using the generalized reference metocean data from the U.S. 

East Coast from Section 4.3. Table 2 summarizes the applied 

simulation conditions. The FAST simulations for DLC 1.1 were 

run at mean wind speeds of 3 m/s to 25 m/s in steps of 2 m/s 

each (11 wind-speed bins total, using the midpoint of each bin) 

with the expected significant wave height and median peakspectral wave period conditioned on the mean wind speed, as 

shown in Table 1 (i.e., neglecting the range of significant wave 

height and peak-spectral wave period values for a given wind 

speed). DLCs 1.1 and 1.2 were represented by the same wind 

conditions, wave conditions, and simulation times. In the

current IEC standard for offshore wind turbines, six wind seeds 

are recommended. We decided to use 10 wind seeds per wind 

bin to assess the convergence of the loads, as discussed in 

Section 5.3. In this second study, all simulations were run with 

10-min periodic wind files using the normal turbulence wind 

model (NTM). Overall, we generated 100 minutes of unique 

stochastic wind data length for each of the 11 wind-speed bins 

regardless of the simulation group. For the simulation length of 

6 hours, we used 10 wave seeds with a normal sea-state model 

(NSS) for each wind bin. We increased the number of wave 

seeds per wind-speed bin for the shorter simulation groups to 

ensure that each simulation group has the same length of total

unique wave data of 60 hours. Consequently, in each simulation 

group, a total of 110 (6-hour), 220 (3-hour), 660 (1-hour), 1980

(20-min), and 3960 (10-min) simulations were run, for a total 

of 6930 simulations. 

For the ultimate load analysis, extreme-event tables were 

generated for each simulation length group using all 

simulations within the group. Only ratios of the ultimate and 

fatigue loads are presented in this work. Therefore, no 

extrapolation or safety factor is necessary for comparison. 

For the calculation of the fatigue loads, we used the IEC 

standard as a guide. Although the IEC standard [2] demands 

that for DLC 1.2 the full long-term joint-probability distribution 

of wind speed, significant wave height, and peak-spectral wave 

period should be applied, we used the already completed

simulations from DLC 1.1, and changed the post-processing. 

The fatigue analysis used in this study follows a process that is 

explained in more detail in [11] and [12]. The rainflow-cycle 

counting algorithm uses a binning of the cycle ranges and 

means of each load time series. The number of bins is 

computed based on the maximum load-range cycle of each 

channel. All load-range cycles with a varying mean load are 

transformed to a load-range at zero mean with the use of a 

Goodman correction (requiring an ultimate load derived from 

an ultimate load factor (the ULF) and load extremes from the 

simulation results). Three different S/N-curves were used to 

represent the range of assumed Whöler exponents. To ensure a 

comparison of the rainflow cycles only, we used the ultimate 

loads from the 10-min simulation group for all fatigue load 

calculations. The lifetime damage and DELs are extrapolated to 

a 20-year design lifetime and weighted with a Rayleigh 

probability distribution based on a mean wind speed of 10 m/s. 

Matha showed in [12] that the DELs don’t change significantly 

for ULFs of 5 or greater; instead, they asymptotically approach 

a constant value with increasing ULF up to 20. Therefore, we 

chose a ULF of 20.

Table 2. Simulation conditions for the simulation-length study

due to hydrodynamics and floating system dynamics.

5. LOADS-ANALYSIS RESULTS

In Section 5.1, we discuss the loads-analysis results of our 

first study, which investigated the dependence of 

aerodynamically derived loads on simulation length for a landbased turbine. Section 5.2 presents the loads-analysis results

from our second study, based on DLCs 1.1 and 1.2 and using 

the simulation specifications and analysis procedures described 

in the Section 4.4. Section 5.2 analyzes groups of different 

simulation lengths for a FOWT to investigate the dependence 

on simulation length of loads due to hydrodynamics and 

floating platform motion. Section 5.3 focuses on the minimum 

data requirements for mean extreme load calculations, 

investigating the number of simulations required for statistics to 

converge.

About 1 TB of data was created during this project. Due to 

the large amount of data, only a small subset can be presented 

here. A more detailed description of the project’s results will be 

available in Ref. [15].

5.1. The Dependence of Aerodynamically Derived 

Loads on Simulation Length for a Land-Based 

Turbine 

During the analysis of the ultimate loads for the FOWT

simulations, there was concern that changes in the 

discretization of the wind turbulence spectrum that TurbSim 

uses were creating higher maximum wind speeds, and thus 

higher maximum loads, for longer simulations. This concern 
6 

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prompted an investigation that used a land-based turbine model 

to isolate loading from the wind inputs only. 

TurbSim uses a spectral sampling method to create a wind 

time series from a target wind turbulence spectrum. In this 

method, the spectrum is discretized and sine waves at each 

discrete frequency are created with amplitudes corresponding to 

the power in the spectrum at that frequency and randomized 

phases. The number of sine waves that make up the time series 

corresponds to the level of discretization, which increases for 

longer simulations. 

Figure 3. Mean of the maximum wind speeds from each 

simulation.

Figure 4. Absolute maximum wind speed across all simulations 

for each length.

Simulations using a land-based wind turbine were run for 

varying simulation lengths, and maximum loads and wind 

speeds from the simulations were found. We determined that 

the discrepancies that prompted the study were a result of the 

averaging method used to process the results, rather than any 

actual differences in the spectral sampling method. If maximum 

values that are averaged across simulations of a given length 

are compared to maximum values averaged across simulations 

from a different length, the longer simulation length will show 

higher maximums (see Figure 3). This indicates that average 

maximums from a long simulation cannot be compared to the 

average maximum from a short simulation. Instead, either a 

single maximum value from six 10-min simulations should be 

compared to the maximum value from one 1-hour simulation 

(Figure 4), for example, or the average maximum value of the 

six 10-min simulations should be compared to the average 

maximum value from breaking up the one hour into 10-min 

sections (Figure 5). 

The turbine response and loads due to the wind followed 

the same trends as the wind statistics shown in Figures 4 and 5, 

and so these results are omitted. While these conclusions seem 

intuitive, it is important to consider the averaging techniques 

used when analyzing simulations of different lengths, and to 

ensure that the total length of the stochastic input is equal. 

Figure 5. Mean of the maximum wind speed for simulations 

broken into 600-s groups.

5.2 The Dependence on Simulation Length of Loads 

Due to Hydrodynamics and Floating Platform Motion 

In this section, the ultimate loads from the different groups 

of simulation lengths are presented first, followed by the 

fatigue-analysis results. 

5.2.1 Ultimate Loads. We calculated the extreme-events 

statistics of the DLC 1.1 simulations for several FAST output 

channels. Figure 6 shows the statistics for the wave elevation 

and platform pitch for each simulation from the groups with 

simulation lengths of 6 hours (dark blue), 20 minutes (light 

blue), and 10 minutes (green). The differences between the 6-

hour and 20-min simulations and the 10-min simulations are 

very small. Only in the wave elevation (upper chart) are a few 

simulations seen with a slightly greater value: at 22 m/s and 24 

m/s, which could be due to the discretization of the wavespectrum in FAST. For all simulations, a wave spectrum with 

600 1200 1800 2400 3000 3600

0

5

10

15

20

25

30

Mean of Max Windspeed

Simulation Time (s)

Windspeed (m/s)

18 m/s

11.4 m/s

8 m/s

600 1200 1800 2400 3000 3600

0

5

10

15

20

25

30

35

Simulation Time (s)

Wind Speed (m/s)

18 m/s

11.4 m/s

8 m/s

600 1200 1800 2400 3000 3600

0

5

10

15

20

25

30

Simulation Time (s)

Windspeed (m/s)

18 m/s

11.4 m/s

8 m/s
7 

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the length of 6 hours is implemented in HydroDyn, but only the 

first part was used for simulations shorter than 6 hours. 

However, we couldn’t find differences in other ultimate load 

channels that would correspond to these greater values; for 

example, as shown for the platform-pitch motion in the lower 

plot in Figure 6. No pitch motion from the 6-hour simulations 

has a significantly greater maximum value than for the 10-min 

simulations. Spars don’t heave much due to waves, but other 

types of floating platform systems might be more affected by 

larger waves and will be considered in upcoming studies with 

semisubmersible and TLP support structures. For the spar, we 

assume that the differences in the wave elevation are too small 

to create further effects. The same statistics were created for 

other important load channels and for the other simulation 

lengths. The 1-hour and 3-hour simulations indicate

approximately the same results, but are not shown here.

Figure 6. Minimum, mean, and maximum wave-elevation, and 

platform-pitch statistics from each 6-hour, 20-min, and 10-min 

simulation.

Similar conclusions can be drawn from Figure 7, which 

shows ultimate load ratios created by dividing the absolute 

maximum values from the extreme-event tables for the 

simulation lengths of 20 minutes, 1 hour, 3 hours, and 6 hours, 

by the corresponding extreme event from the 10-min 

simulations. A ratio of one represents the same maximum load 

between simulation lengths. A ratio greater than one indicates

an increased ultimate load value. The four transverse bending 

moments shown in Figure 7 were calculated by taking the 

vector sum of the bending moments about the member’s 

transverse axes. Figure 7 shows clearly for the transverse 

bending moments of the low-speed shaft at the main bearing 

(LSSgagMMyz), the root of blade 1 (RootMMxy1), the tower 

base (TwrBsMMxy), and the yaw bearing (YawBrMMxy) that 

longer simulation lengths are not warranted to estimate ultimate 

loads. When the total random time for the wind and waves is 

constant, the ultimate loads do not intensify with increasing

simulation length. 

Figure 7. Ultimate-load ratios of longer simulation lengths to the 

simulation length of 10 minutes.

5.2.2 Fatigue Loads. We calculated the lifetime fatigue 

damage and lifetime DELs for several output channels, and 

divided the 20-min, 1-hour, 3-hour, and 6-hour fatigue loads by 

the 10-min fatigue loads to calculate fatigue ratios. Each DEL 

for a given output channel is calculated using three different 

material exponents (m). For the output channels, low-speed 

shaft bending moments at the main bearing in 0° and 90° 

(LSSGagMya, LSSGagMza), yaw-bearing, and tower-base 

bending moments in side-to-side and fore-aft (YawBrMxp, 

YawBrMyp, TwrBsMxt, TwrBsMyt), and fairlead and anchor 

tensions in the mooring system (Fair1Ten, Anch1Ten for line 

1), material exponents of 3, 4, and 5 were used, corresponding 

to steel components. For the blade root in-plane and out-ofplane bending moments (RootMxc1, RootMyc1) using 

composite material, the DELs were computed using m equal to 

8, 10, and 12.

In Figure 8, the ratios for the lifetime DELs for different 

simulation lengths and a range of material exponents are 

presented. The figure shows slightly higher DELs with 

increasing simulation lengths. The channels RootMyc1, 

Fair1Ten, and Anch1Ten show the greatest differences. 

However, in general, the increase in the DELs of the 6-hour 

simulations compared to the 10-min simulations is very low. 
8 

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National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

The biggest increase can be seen for the output channel 

Fair1Ten, which is about 3.5% greater than unity. 

Figure 8. DEL ratios to the simulation group of 10 minutes for 

different material exponents and an UCMult-factor of 0.5.

5.2.3 Sensitivity Analysis of the Fatigue 

Calculation Due to Unclosed-Cycle Counting. While 

processing the fatigue loads analysis, we identified a sensitivity 

of the results to the unclosed-cycle counting factor (input 

UCMult in MLife). The value of UCMult dictates how 

unclosed (partial) cycles in the rainflow-counting algorithm are

treated. By setting UCMult to zero, MLife neglects unclosed 

cycles. Setting it to one causes MLife to count unclosed cycles 

as full cycles. The first fatigue results presented in Figures 8 

and 9 are calculated with UCMult equal to 0.5. In the MLife 

theory manual, and in most NREL studies, a value of UCMult

of 0.5 is highly recommended [11]. Therefore, we presented the 

DELs with an UCMult setting of 0.5 first, before discussing the 

fatigue results for a range of UCMult values in this section.

Figure 9 shows the binned lifetime damages (LDs) versus 

load range at zero mean load for the root out-of-plane bending 

moment. Because we used a ULF value of 20, the binned 

damage on the Y-axis represents a very small value; therefore, 

only comparisons between the curves are logical. Figure 9, 

which shows the binned lifetime damage versus load range for 

the out-of-plane root bending moment, can be used to explain 

the results of Figure 8. There are no large differences between 

the various simulation lengths except on the far right side at the 

largest load ranges. These differences are enough to create 

slightly higher lifetime damages, as shown in Figure 8. 

The largest load ranges are often where unclosed cycles 

occur. For simulation lengths of 6 hours, there is a higher 

chance of closing these partial cycles. Mouzakis and 

Morfiadakis showed that without counting the unclosed cycles, 

“the lifetime predictions based on segmented data can 

underestimate damage by 40 percent” [13] for a material 

exponent of 10. However, they showed that the difference in 

the predicted service lifetime between the time-series data 

concatenated into a single file and the time-series data counted 

in segments is about 10% for the blade out-of-plane bending 

moment. For other outputs, like the blade in-plane bending 

moment, the differences are only about 3% to 4%. These results 

indicate that the significance of counting the unclosed cycles in 

the signal is an order of magnitude more important than when 

merging all files together and counting the rainflow-cycles 

collectively. Thus, we concentrated our attention on calculating 

the lifetime damage for a range of factors for UCMult to 

determine its influence.

The results in Figure 10 present the LD-ratios for the blade 

out-of-plane root-bending moment. We computed the LD for 

each simulation length and weighted the unclosed cycles in the 

output signal with UCMult values of 0, 0.3, 0.5, 0.7, and 1. In 

Figure 10, it can be clearly seen that the importance of

simulation length on the estimated fatigue life is highly

dependent on the choice of UCMult. If unclosed cycles are 

neglected (UCMult = 0), the lifetime damage for the 6-hour 

simulations is four times greater than the 10-minute

simulations. In contrast, with UCMult set to one, the fatigue 

damage is nearly independent of the simulation length. 

Moreover, the longer simulations of 1-hour, 3-hours, and 6-

hours actually have slightly less damage compared to the 10-

min simulations, and the 20-min simulations have a ratio of 

unity when counting unclosed cycles as full cycles (UCMult of 

1.0). 

Figure 9. Binned lifetime damage versus load range for the outof-plane root bending moment (m = 10, UCMult 0.5).

In our opinion, a value of UCMult between 0.5 and 1.0

should be used to have a balance between conservatism and 

accuracy. With a factor lower than 0.5, the damage induced by 

the (mostly large) unclosed cycles would be otherwise reduced

in an unacceptable way. We have not yet identified if a specific 

UCMult is appropriate to a given simulation length; this will be 

a subject for further studies. However, the fatigue results shown 

for UCMult values larger than 0.7 make a strong case that there 

is no reason for longer simulation lengths, especially when 

coupled with the ultimate load results from Figure 7. The

difference from the O&G industry may result, due to the wind-
9 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

dictated loads of an operational FOWT instead of wave-dictated 

loads for an O&G floating platform. However, these results are 

based on a specific set of simulation conditions of an 

operational system. The recommendations may change for 

other floating platforms and/or for parked/idling conditions.

Figure 10. Lifetime-damage ratios of different simulation lengths 

to the 10-min simulations dependent on different values of 

UCMult for the root out-of-plane bending moment.

5.3 Minimum Data Requirements for Mean Extreme 

Load Calculation

The number of simulations required for statistics to 

converge was also investigated. Using bulk statistics from the 

simulations (mean and maximum loads), a Monte Carlo 

selection of varying size groups was conducted, and the 

statistics of the groups were compared to the average of all of 

the simulations. For this study, we are interested in the effects 

of waves on convergence only, so simulations with the same 

wind seed and varying wave seeds are considered. For a given 

wind speed, there were 360 ten-minute simulations, with 10

unique wind seeds, and 36 unique wave seeds per wind seed. 

Only the results for the maximum loads are presented here. The 

results for the blade root out-of-plane bending moment are 

summarized in Figures 10 to 12. The size of the random groups 

selected in the Monte Carlo sampling is along the X-axis, and 

the percent deviation from the average of the 36 10-min 

simulations is on the Y-axis. The red line shows the 95% 

confidence interval (the green line is the 68% confidence 

interval.

The mean of the maximum loads converge to about a 1% 

difference from the “true” answer between 5−10 simulations. 

These results are consistent for other loads as well. Figures 12 

and 13 show the standard deviation convergence and the 

absolute maximum value convergence for the maximum loads. 

Interestingly, the standard deviation of the maximum values 

(Figure 12) in the randomly selected groups of simulations 

requires a very large group to approach the standard deviation 

of the 36 simulations. However, the absolute maximum value 

(Figure 13) shows that there is 95% confidence to be within 

3.5% of the “true” value with 10 simulations. The results from 

this study suggest that approximately ten 10-min simulations 

for each wind speed bin should be used to obtain converged 

statistics. Further investigations into the cause of the large 

differences in standard deviation are being conducted.

Figure 11. Mean of max root out-of-plane bending moment

at 12-m/s wind speed.

Figure 12. Standard deviation of max root out-of-plane bending 

moment at 12-m/s wind speed.
10

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 13. Absolute max of root out-of-plane bending moment

at 12-m/s wind speed.

6. CONCLUSIONS AND RECOMMENDATIONS

From the research presented in this paper, we have drawn 

some initial recommendations in regard to performing a loads 

analysis of a floating offshore wind turbine. First, we 

recommend using proper initial conditions, as well as 

increasing the amount of initial simulation time that needs to be 

disregarded to 60 s to eliminate the effects of start-up 

transients. In the study regarding the dependence of 

aerodynamically derived loads on simulation length, it was

shown that the length of the wind files did not have an effect on 

the loads produced on the turbine, as long as the total 

simulation time was kept constant. It was also shown that the 

use of repeated periodic wind files could possibly reduce 

computational effort without affecting loads. In addition, in the 

study, regarding the dependence on simulation length of loads 

due to hydrodynamics and floating platform motion, our results 

demonstrated that simulation length did not affect ultimate 

loads for the floating offshore wind turbine OC3-Hywind spar 

buoy; a larger number of shorter simulations (10 minutes at the 

shortest) led to the same loads as longer simulations (provided

that the total simulation time is kept constant and that repeated 

periodic wind files are used). For fatigue loads, the results 

showed that there is greater sensitivity in the loads to the 

method of counting unclosed cycles, as compared to the 

simulation length. This topic will be investigated further in 

future studies. 

ACKNOWLEDGMENTS

The National Renewable Energy Laboratory authors 

performed this work in support of the U.S. Department of 

Energy under contract number DE-AC36-08GO28308.

We also would like to express our appreciation to all of the 

NREL colleagues who supported that project. In particular, the 

help of B. Jonkman, M. L. Buhl Jr., and G. J. Hayman was

essential for the success of this work.

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