Alternative Mooring Systems for a Very Large Offshore Wind Turbine Supported by a Semisubmersible Floating Platform

Liu, Jinsong; Manuel, Lance

doi:10.1115/1.4039984

As offshore wind turbines supported on floating platforms extend to deep waters, the various effects involved in the dynamics, especially those resulting from the influence of moorings, become significant when predicting the overall integrated system response. The combined influence of waves and wind affect motions of the structure and induce tensile forces in mooring lines. The investigation of the system response under misaligned wind-wave conditions and the selection of appropriate mooring systems to minimize the turbine, tower, and mooring system loads is the subject of this study. We estimate the 50-year return response of a semisubmersible platform supporting a 13.2 MW wind turbine as well as mooring line forces when the system is exposed to four different wave headings with various environmental conditions (wind speeds and wave heights). Three different mooring system patterns are presented that include 3 or 6 mooring lines with different interline angles. Performance comparisons of the integrated systems may be used to define an optimal system for the selected large wind turbine.

Introduction

There is a growing interest in the development of floating offshore wind turbines (FOWTs) to harness the stronger and more consistent offshore wind resource in deeper waters. A few FOWT concepts supporting an offshore baseline wind turbine model, such as the NREL 5 MW wind turbine model [1], have been proposed and studied in the past decade [2–5]. Recently, large-scale wind turbines, such as the 13.2 MW wind turbine model developed at Sandia National Laboratories (SNL) [6–8], have gained interest because of their potential to bring down overall system costs. Related studies have been carried out to evaluate the system's response including extreme and fatigue loads, global dynamic response, etc., usually under simplifying assumptions of co-aligned wind and waves. It is not uncommon, however, for wind and waves to be significantly misaligned, particularly in stable atmospheric conditions [9]. Besides, as per the requirements of IEC 61400-3 [10], multidirectional distributions of wind and wave directions can have an important influence on loads acting on the support structure. This influence, in general, is site-specific and structure-dependent.

The influence of misaligned wind-wave conditions is directly affected by the directional stiffness and damping properties of the integrated FOWT system, which is composed of the wind turbine, floating platform, and mooring system. To date, only a few studies have been conducted to investigate the influence of wind-wave misalignment on the response of floating platforms for wind turbines. Philippe et al. [11] studied the impact of wind-wave misalignment on a floating wind turbine located on a barge platform and discovered that the sway, roll, and yaw response amplitude operators increased with misalignment and were large especially for a misalignment of 90 deg. Ramachandran et al. [12] studied the importance of wind-wave misalignment on a tension-leg platform platform under a wind excitation of 10 m/s, and showed the significance of 90 deg and 180 deg misalignments on both the spectral energy content and on time series of the platform motions.

In this study, wind-wave misalignment effects are studied for a semisubmersible FOWT supporting a large-scale 13.2 MW SNL wind turbine model. The 50-year return extreme response for different response measures are obtained using the environmental contour (EC) method under selected wind-wave misalignment conditions. In addition to the structural properties of the platform, the mooring lines also provide directional stiffness and damping to the integrated system. Different mooring patterns are studied to investigate the effect of mooring system contributions under misaligned wind-wave directions and their influence on the 50-year return extreme response. Finally uncertainties in the response (conditional on wave and wind conditions) are also studied by employing a correction to the two-dimensional (2D) EC method.

Structural Reliability and the Environmental Contour Method

Structural Reliability Problem.

In probability-based design against ultimate limit states, one is interested in establishing the appropriate response level for design so as to guarantee a specified maximum target probability of failure. Let

X = (X_{1}, X_{2}, \dots, X_{n})

denote an n-dimensional environmental random variable vector and Y the response of interest. The probability of failure p_f can be written formally as

p_{f} = P [Y > Y_{T}] = \int_{\forall x} P [Y > Y_{T} | X = x] f_{X} (x) d x

(1)

where Y_T represents the T-year return period response and $f_{X} (x)$ denotes the joint probability density function of the environmental variables. Direct evaluation of the integral in Eq. (1) is usually expensive as it involves extensive computations to establish the short-term conditional probability, $P [Y > Y_{T} | X = x]$ ⁠. Agarwal and Manuel [13] used a parametric method to estimate the extreme loads for a 2 MW offshore wind turbine based on a Gumbel short-term load distribution. Ronold et al. [14] described a quadratic Weibull distribution model to describe wind turbine load distributions that were adapted and applied in other studies, such as by Manuel et al. [15]. Recently, Lee et al. [16] proposed a Bayesian spline method to establish a nonhomogeneous generalized extreme value distribution to represent the extreme load distributions of land-based wind turbines. In this method, the short-term distribution $f_{Y | X} (y | X = x)$ was estimated, instead of fitting generalized extreme value distributions for each x bin separately, using a spline model that connects all the bins across the random variable space. The advantage of this method is that it is not necessary to have large amounts of data to define the tails of each short-term distribution in order to reduce the uncertainty in estimation.

Note that Eq. (1) suggests a separation between the environmental random variable distribution $f_{X} (x)$ ⁠, which is site-specific from the conditional load distribution, $f_{Y | X} (y | X = x)$ ⁠, which is model-specific. Accordingly, an alternate method for establishing the turbine T-year return period response that takes advantage of this separation is the EC method. This method belongs to the class of inverse reliability problems that can yield (approximately) the T-year return period response with far less computational effort than is possible with parametric methods, direct integration, or Monte Carlo simulation.

Environmental Contour Method.

The environmental contour method was proposed by Winterstein et al. [17] and has been widely used to derive the extreme responses for fixed and floating offshore structures. Details related to the theoretical framework for the application of the method can be found elsewhere [17–19]. The EC method, which is built upon classical first-order reliability method principles, makes the assumption that the variability of the response (conditional on the environment) is relatively small compared to the variability in environmental random variables themselves. Also, in the EC method, an environmental contour associated with the target return period or target probability of failure, p_f, is constructed. The desired response Y_T is approximately equal to the largest median response considering all environmental conditions defining the environmental contour.

It is convenient, in the EC method, to transform the random variables defined in the physical space X into independent standard normal space U via methods such as the Rosenblatt transformation [20]. Associated with the target probability of failure, p_f, defined before in terms of the return period, T, it is easy to construct the “environmental contour” in U space. It is defined by an n-dimensional hypersphere,

| | u | |_{2} = \sum_{i = 1}^{n} u_{i}^{2} = β_{T}^{2}

⁠, where β_T, has the notion of a conventional reliability index and is such that

Φ (β_{T}) = 1 - p_{f}

⁠, where

Φ (\cdot)

refers to the standard normal cumulative distribution function. Using a Rosenblatt transformation scheme, we have

Φ (u_{1}) = F_{X_{1}} (x_{1}) Φ (u_{2}) = F_{X_{2} | X_{1}} (x_{2} | x_{1}) \dots Φ (u_{n}) = F_{X_{n} | X_{n - 1}, \dots, X_{1}} (x_{n} | x_{n - 1}, \dots, x_{1})

(2)

In the numerical studies that follow, we employ the EC method to derive the 50-year period return response under selected wind-wave misalignment conditions for three different mooring systems. These results are compared both to assess the wind-wave misalignment influence and to evaluate alternate mooring patterns for the FOWT integrated system.

Integrated Sandia 13.2 MW Floating Offshore Wind Turbines Model

Integrated Floating Offshore Wind Turbines Model.

The Sandia 13.2 MW semisubmersible FOWT model, as summarized in Table 1 and depicted in Fig. 1, is the subject of ongoing study. Please refer to Liu et al. [7] for details regarding this model.

Fig. 1

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Integrated SNL semisubmersible wind turbine model: (a) three-dimensional (3D) View and (b) 2D View

Table 1

Selected properties of the SNL 13.2 MW semisubmersible floating wind turbine

Rated generator power

13.2 MW

Rotor configuration

3 blades, 102.5 m

Rotor, hub diameter

205 m, 5 m

Cut-in, rated, cut-out wind speed

3 m/s, 11.3 m/s, 25 m/s

Hub height

133.5 m

Nacelle mass

1,030,000 kg

Tower mass

553,995 kg

Blade mass (per blade)

59,047 kg

Platform draft

30 m

Tower base above SWL

15 m

Offset columns above SWL

18 m

Upper columns length

39 m

Base columns length

9 m

Main column diameter

9.75 m

Offset (upper) columns diameter

18 m

Base columns diameter

36 m

Pontoons diameter

2.4 m

Platform mass, including ballast

4.547 × 10⁷ kg

The platform model was developed for use in 200 m of water and was sized so as to support Sandia's 13.2 MW wind turbine [6] with SNL100-02 blades [21]. The wind turbine is located over the center column. There are three offset columns, with pontoons around the center column, each of which has attached catenary mooring lines. Braces are used to connect all of the columns as an integrated body. The large waterplane area moment of inertia provides good stability and stiffness, thus limiting the platform pitch angle in wind and waves. The blade pitch and generator control routines for the NREL 5 MW wind turbine [1] are modified for the larger rotor based on specific performance considerations.

Hydrodynamic loads on the platform are calculated via a combination of potential flow theory and Morison's equation. The first-order potential flow solution including added mass, radiation damping, and wave excitation is obtained using a panel method solver, WAMIT [22], in which the columns and pontoons are modeled as rigid bodies. Additional viscous forces on large-volume components are accounted for by including the nonlinear drag terms in Morison's equation.

Alternative Mooring Systems.

Three alternative mooring system configurations are proposed based on an earlier study [7]. Figure 2 shows the three patterns, SNL300, SNL630, and SNL660, that are studied here (where the notation SNLXYY indicates a mooring system for the SNL model with X mooring lines where the angle between adjacent mooring lines is YY degrees.) SNL300 is the baseline mooring system selected in a previous study based on steady-state analysis [8]. As seen in Fig. 2, SNL630 and SNL660 have six mooring lines connected to the platform compared to the three lines used in SNL300. The integrated turbine system with these three mooring patterns is subjected to misaligned wind and wave input conditions, and the 50-year return system response is studied in each case.

Fig. 2

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Three alternative mooring line configurations (diameter in meters)

Numerical Studies

Time-domain stochastic simulations are carried out to investigate the effect of wind-wave misalignment and mooring system configuration on the integrated system with the SNL 13.2 MW turbine model. Four wind-wave misalignment conditions are considered as shown in Fig. 3; specifically, these are 0 deg wind (β_wind: 0 deg) and four wave directions (β_wave: 0 deg, 30 deg, 60 deg, and 90 deg). The rotor is assumed to be aligned with the wind (zero yaw), while the wave direction is defined relative to the wind direction.

Fig. 3

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Top view of wind-wave misalignment condition

Ten 1 h simulations (after removal of transients) are carried out for each of the selected sea states (defined later) using the analysis tools described next. Extreme load/response statistics of interest are obtained so as to identify critical sea states and to compare the different mooring system patterns.

Dynamic Analysis Tool.

Fatigue, aerodynamics, structures, and turbulence (FAST), an open-source aero-hydro-servo-elastic time-domain nonlinear dynamic modeling tool, was developed at NREL for the analysis of two- and three-bladed horizontal-axis wind turbines, sited either onshore or offshore. Wind turbines in FAST are modeled as a combination of rigid and flexible bodies in a modal multibody formulation. Stochastic inflow 3D wind velocity fields are generated using turbsim, which employs spectral models to generate zero-mean u, v, and w (i.e., longitudinal, lateral, and vertical) components of the wind velocity vector over a two-dimensional vertical gridded plane that covers the rotor plane. Needed mean wind field profiles are added deterministically. The HydroDyn module in FAST uses potential flow theory, along with imported files from WAMIT, to derive hydrodynamic loads on the semisubmersible platform. It also includes viscous drag components on the platform using Morisons equation as stated before.

Loads on the mooring lines are computed using mooring analysis program [23], which is integrated into FAST. Mooring analysis program accounts for the effects of distributed cable mass, strain, and cable elasticity to provide the line profile and effective forces for a cable suspended at steady-state (static equilibrium). In other words, forces arising from inertia, bending, torsion, viscous drag, and internal damping are not included.

Environmental Contours.

Site no. 14 in the North Sea, designated Norway 5 in Li et al. [24], with a water depth of 202 m, is selected in this study. Long-term joint distributions of mean wind speed at a 10 m height (U_w), significant wave height (H_s), and spectral peak period (T_p) are provided [24] by based on analytical distribution fits to hindcast data. The distributions of the environmental random variables are presented in Table 2.

Table 2

Joint distribution of U_w, H_s, and T_p where $f_{U_{w}, H_{s}, T_{p}} (u, h, t) = f_{U_{w}} (u) \cdot f_{H_{s} | U_{w}} (h | u) \cdot f_{T_{p} | U_{w}, H_{s}} (t | u, h)$

Random variable	Distribution	Conditional relationships	Parameters
U_w	$Weibull \sim (α_{U_{w}}, β_{U_{w}})$	—	$α_{U} = 2.029$
$β_{U} = 9.409$
$H_{s} \| U_{w}$	$Weibull \sim (α_{H_{s}}, β_{H_{s}})$	$α_{H_{s}} = a_{1} + a_{2} u^{a_{3}}$	$a_{1} = 2.136, a_{2} = 0.013, a_{3} = 1.709$
$β_{H_{s}} = b_{1} + b_{2} u^{b_{3}}$	$b_{1} = 1.816, b_{2} = 0.024, b_{3} = 1.787$
$T_{p} \| (H_{s}, U_{w})$	$Lognormal \sim (μ_{T_{p}}, σ_{T_{p}})$	$μ_{T_{p}} = ln [\frac{m}{\sqrt{1 + ν^{2}}}]$	$θ = - 0.255$
		$σ_{T_{p}} = \sqrt{ln [ν^{2} + 1]}$	$γ = 1.0$
		$ν = \frac{σ_{T_{p}}}{μ_{T_{p}}}$	$e_{1} = 8.0, e_{2} = 1.938, e_{3} = 0.486$
		$m = {\bar{T}}_{p} (h) \cdot [1 + θ {(\frac{u - \bar{u} (h)}{\bar{u} (h)})}^{γ}]$	$f_{1} = 2.5, f_{2} = 3.001, f_{3} = 0.745$
		${\bar{T}}_{p} (h) = e_{1} + e_{2} \cdot h^{e_{3}}$	$k_{1} = - 0.001, k_{2} = 0.316, k_{3} = - 0.145$
		$\bar{u} (h) = f_{1} + f_{2} \cdot h^{f_{3}}$
$ν (h) = k_{1} + k_{2} \cdot exp (h k_{3})$

Based on the joint distribution of the environmental random variables, a 3D 50-year contour surface for the selected site, as shown in Fig. 4(a), is obtained by using a sphere in standard normal space that is mapped to the physical variable space using the Rosenblatt transformation, as follows:

Φ (u_{1}) = F_{U_{w}} (u) Φ (u_{2}) = F_{H_{s} | U_{w}} (h | u) Φ (u_{3}) = F_{T_{p} | H_{s}, U_{w}} (t | h, u) and β^{2} = u_{1}^{2} + u_{2}^{2} + u_{3}^{2}

(3)

Fig. 4

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Fifty-year ECs: (a) 3D EC and (b) 2D EC

The target reliability index β₅₀, the radius of the sphere in standard normal space, is computed as $Φ^{- 1} (1 / (50 \times 365.25 \times 24)) = 4.58$ since simulations of 1 h duration are used in this study.

In this study, only the median of T_p conditional on U_w and H_s is used instead of using T_p's full conditional distribution given U_w and H_s. Thus, $u_{3} = 0$ in U space, is used in this study, and the resulting 2D environmental contour for H_s and U_w is shown in Fig. 4(b). A total of 24 sea states including a sea state corresponding to the rated wind speed is selected representing the operating range of the turbine. Since we are interested in the right (upper) tail of the distribution of extreme loads, only the higher of the two H_s values at each U_w is selected. These 24 sea states are presented in Table 3.

Table 3

Representative sea states (defined by U_hub, H_s, and T_p values) identified using the 2D EC

U_hub (m/s)	H_s (m)	T_p (s)	U_hub (m/s)	H_s (m)	T_p (s)	U_hub (m/s)	H_s (m)	T_p (s)
3	5.83	15.15	11	7.34	14.80	18	8.95	14.65
4	6.03	15.10	11.3	7.45	14.79	19	9.19	14.64
5	6.21	15.05	12	7.60	14.77	20	9.43	14.64
6	6.40	15.00	13	7.82	14.74	21	9.67	14.63
7	6.59	14.95	14	8.04	14.72	22	9.92	14.63
8	6.78	14.91	15	8.26	14.70	23	10.17	14.62
9	6.98	14.86	16	8.49	14.68	24	10.42	14.62
10	7.18	14.83	17	8.72	14.67	25	10.67	14.62

U_hub (m/s)	H_s (m)	T_p (s)	U_hub (m/s)	H_s (m)	T_p (s)	U_hub (m/s)	H_s (m)	T_p (s)
3	5.83	15.15	11	7.34	14.80	18	8.95	14.65
4	6.03	15.10	11.3	7.45	14.79	19	9.19	14.64
5	6.21	15.05	12	7.60	14.77	20	9.43	14.64
6	6.40	15.00	13	7.82	14.74	21	9.67	14.63
7	6.59	14.95	14	8.04	14.72	22	9.92	14.63
8	6.78	14.91	15	8.26	14.70	23	10.17	14.62
9	6.98	14.86	16	8.49	14.68	24	10.42	14.62
10	7.18	14.83	17	8.72	14.67	25	10.67	14.62

A JONSWAP wave spectrum with frequencies up to 10 rad/s is used to generated a stochastic sea surface elevation process with a time-step, $Δ t = 0.2 s$ ⁠. A 3D stochastic inflow wind field is generated according to the Kaimal spectrum in Turbsim, using 27 × 27 grid points on the rotor plane and a 0.1 s time-step. The normal turbulence model (NTM) with turbulence category A per IEC definitions is considered. The IEC wind profile defines shear for the mean wind field. In this study, a power law exponent 0.14 and a surface roughness of 0.03 m is used. In the absence of directional information, it should be noted that the same joint distribution for U_w and H_s is assumed, regardless of the wind and wave directions.

Results

For each selected sea state on the 2D environmental contour, ten 1 h simulations are carried out, and the maximum response over that duration for various response measures of interest is computed. As stated before, with the EC method, the largest median 1 h maximum response approximately defines the T-year response (here, T = 50 years).

Next, 50-year values for different response measures and for the different FOWT system models are estimated along with the associated critical sea state that caused the extreme response. First, different wave headings are investigated in order to assess the severity of the wind-wave misalignment on the 50-year extreme response. Response measures for the integrated system that are studied include the rotor thrust, blade and tower base bending moments for the wind turbine, and the fairlead anchor tension for the mooring system. In general, similar conclusions regarding extreme values and associated critical sea state are made for the different mooring systems when different wind-wave misalignments are considered. Thus, for brevity, only the FOWT system with the SNL300 mooring lines is discussed first. Later, to further understand different resistance contributions introduced by alternate mooring configurations, other response comparisons are discussed. Particular attention is paid to those response variables that show significant variation as the configuration of mooring patterns is changed.

Wind-Wave Misalignment Effects on 50-Year Return Extreme Responses

Fifty-Year Return Period Rotor and Tower Loads.

Four different rotor and tower loads are selected to study the influence of wind-wave misalignment on the integrated FOWT system. These include the rotor thrust (LSShftFxa), blade root out-of-plane bending moment (RootMyc1), fore-aft tower base bending moment (TwrBsMyt), and the side-to-side tower base bending moment (TwrBsMxt). The 50-year response values are controlled either by the highest wind speed (25 m/s) and associated wave height or by a wind speed close to rated (11.3 m/s) and associated wave height. Misalignment effects are presented by using the parameter, $R_{β, 0}$ ⁠, which describes the ratio of the extreme response at a wave heading, β_wave, to the corresponding response when β_wave equals zero.

Since the wind direction is the same for all the selected environmental conditions, insignificant variation is expected for LSShftFxa and RootMyc1 as β_wave changes from 0 deg to 90 deg. As can be seen from Table 4, the 50-year RootMyc1 value is not affected by varying the wave heading direction and a very slight reduction in rotor thrust is seen with increasing wind-wave misalignment. For both these response measures, extremes occur for wind speeds at or around rated and with associated wave heights given by the environmental contour.

Table 4

SNL300 50-year return period wind turbine rotor and tower loads along with associated sea states

Response	β_wave (deg)	Y_50–_yea_r	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Response	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)
RootMyc1 (⁠ $MN \cdot m$ ⁠)	0	63.25	1.00	12	7.60	LSShftFxa (MN)	0	3.23	1.00	11.3	7.45
	30	63.27	1.00	11.3	7.45		30	3.06	0.95	11	7.39
	60	62.96	1.00	11.3	7.45		60	3.04	0.94	13	7.82
90	63.81	1.01	11.3	7.45	90	3.10	0.96	13	7.82
TwrBsMxt (⁠ $MN \cdot m$ ⁠)	0	160.20	1.00	25	10.67	TwrBsMyt (⁠ $MN \cdot m$ ⁠)	0	591.90	1.00	13	7.82
	30	308.05	1.92	25	10.67		30	500.10	0.84	12	7.60
	60	459.30	2.87	25	10.67		60	481.50	0.81	13	7.82
90	357.95	2.23	25	10.67	90	493.80	0.83	11.3	7.45

Response	β_wave (deg)	Y_50–_yea_r	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Response	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)
RootMyc1 (⁠ $MN \cdot m$ ⁠)	0	63.25	1.00	12	7.60	LSShftFxa (MN)	0	3.23	1.00	11.3	7.45
	30	63.27	1.00	11.3	7.45		30	3.06	0.95	11	7.39
	60	62.96	1.00	11.3	7.45		60	3.04	0.94	13	7.82
90	63.81	1.01	11.3	7.45	90	3.10	0.96	13	7.82
TwrBsMxt (⁠ $MN \cdot m$ ⁠)	0	160.20	1.00	25	10.67	TwrBsMyt (⁠ $MN \cdot m$ ⁠)	0	591.90	1.00	13	7.82
	30	308.05	1.92	25	10.67		30	500.10	0.84	12	7.60
	60	459.30	2.87	25	10.67		60	481.50	0.81	13	7.82
90	357.95	2.23	25	10.67	90	493.80	0.83	11.3	7.45

Tower base bending moments are of special interest since these response measures affect the overall stability of the integrated system. Critical sea states for the side-to-side tower moment (TwrBsMxt) are those with highest waves and the apparent wave force-driven effects. On the other hand, the fore-aft moment (TwrBsMyt) is largest around the rated wind speed. Large uncertainty in the tower base bending moment is seen in the wind speed range from 15 m/s to 20 m/s but not around the cut-out wind speed, as can be seen in Fig. 5 which shows the 50-year TwrBsMxt and TwrBsMyt values along with their variability as a function of wind speed and for the different wave headings and mooring systems.

Fig. 5

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Fifty-year return period tower base bending moments (left vertical axes, darker lines) and measures of uncertainty (right vertical axes, lighter lines) for different wind-wave misalignments and for different mooring systems: (a) SNL300, (b) SNL630, (c) SNL660, (d) SNL300, (e) SNL630, and (f) SNL660

As β_wave increases from 0 deg to 90 deg, 50-year TwrBsMyt values reduce by around 17% from 591.90 MN m to 493.8 MN m. In contrast, TwrBsMxt increases dramatically, by more than a factor of 2 when β_wave increases from 0 deg to 90 deg. These two tower base bending loads even become somewhat comparable when the wave heading is 90 deg. This suggests that in preliminary design steps, it is not appropriate to neglect or underestimate side-to-side tower bending moments to assess overall system performance. The triangular platform geometry is such that a misalignment (β_wave) of 60 deg leads to synchronized and in-phase broadside wave loads over one entire side of the triangle for long-crested waves, which are assumed here. In all of the other cases (0 deg, 30 deg, and 90 deg), different points on one exposed upstream side of the platform will not all experience wave loads that are in phase. This is why Table 4 shows greater response levels for $β_{wave} = 60$ deg versus all the other cases.

Fifty-Year Return Period Mooring Line Loads.

Anchor and fairlead tensions of mooring line nos. 1 and 2 are studied to investigate the effect of wind-wave misalignment. Different critical sea states were observed for two mooring lines. For mooring line no. 1, the extreme fairlead and anchor tension occur around the rated wind speed, while for mooring line no. 2, no anchor force is detected and the extreme fairlead tension occurs at the lowest wind speeds (around 3 m/s). As shown in Table 5, no significant variation is observed for TFair1, TFair2, and TAnch1 tensions as β_wave changes from 0 deg to 90 deg. Note that TFair1 and TFair2 are the tension forces at the fairleads of mooring lines 1 and 2, respectively; TAnch1 and TAnch2 are the tension forces at the anchors of mooring lines 1 and 2, respectively. The dominant effect on the integrated system is from wind forces as opposed to wave forces. The nonzero mean wind force on the system causes a net surge offset that makes a significant contribution to overall tension forces at the fairlead and anchor of mooring line 1. This is evident in Table 5.

Table 5

SNL300 50-year return period mooring line loads along with associated sea states

Response	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Response	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)
TFair1 (kN)	0	4346.0	1.00	11.3	7.45	TFair2 (kN)	0	2120.5	1.00	3	5.83
	30	4361.5	1.00	12	7.60		30	2127.0	1.00	3	5.83
	60	4363.5	1.00	12	7.60		60	2170.5	1.02	3	5.83
90	4349.0	1.00	13	7.82	90	2196.5	1.04	3	5.83
TAnch1 (kN)	0	2487.0	1.00	11.3	7.45	TAnch2 (kN)	0	0.0	—	—	—
	30	2509.0	1.01	12	7.60		30	0.0	—	—	—
	60	2510.0	1.01	12	7.60		60	0.0	—	—	—
90	2490.0	1.00	13	7.82	90	0.0	—	—	—

Response	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Response	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)
TFair1 (kN)	0	4346.0	1.00	11.3	7.45	TFair2 (kN)	0	2120.5	1.00	3	5.83
	30	4361.5	1.00	12	7.60		30	2127.0	1.00	3	5.83
	60	4363.5	1.00	12	7.60		60	2170.5	1.02	3	5.83
90	4349.0	1.00	13	7.82	90	2196.5	1.04	3	5.83
TAnch1 (kN)	0	2487.0	1.00	11.3	7.45	TAnch2 (kN)	0	0.0	—	—	—
	30	2509.0	1.01	12	7.60		30	0.0	—	—	—
	60	2510.0	1.01	12	7.60		60	0.0	—	—	—
90	2490.0	1.00	13	7.82	90	0.0	—	—	—

Alternative Mooring Systems.

In Tables 6 and 7, selected 50-year response values for all three mooring configurations and associated critical sea states for platform yaw motion are presented. The different mooring line configurations result in different directional stiffness contributions to the integrated system; specifically, SNL660 will lose stiffness in the x direction due to the different interline angle compared to SNL300; however, it will gain stiffness in system yaw especially when the wind and waves are misaligned. Various quantities of interest are selected while focusing on differences introduced by alternative mooring patterns. To compare extreme values for the selected response measures, a ratio of the extreme response for each mooring pattern to that for the corresponding extreme with the SNL300 system, namely, $R_{i, 1}$ is defined, where i = 1, 2, 3 corresponds to SNL300, SNL630, and SNL660, respectively.

Table 6

Fifty-year platform yaw response values for three different mooring configurations along with associated sea states

		SNL300				SNL630				SNL660
Response, Y	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)
Yaw (deg)	0	5.69	1.00	23	10.17	5.49	0.97	21	9.67	5.00	0.88	25.00	10.67
	30	6.17	1.00	25	10.67	5.84	0.95	20	9.43	5.61	0.91	25.00	10.67
	60	5.72	1.00	23	10.17	5.50	0.96	21	9.67	5.02	0.88	25.00	10.67
90	6.31	1.00	24	10.42	5.83	0.92	21	9.67	5.44	0.86	25.00	10.67

		SNL300				SNL630				SNL660
Response, Y	β_wave (deg)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)	Y_50–year	$R_{β, 0}$	U_hub (m/s)	H_s (m)
Yaw (deg)	0	5.69	1.00	23	10.17	5.49	0.97	21	9.67	5.00	0.88	25.00	10.67
	30	6.17	1.00	25	10.67	5.84	0.95	20	9.43	5.61	0.91	25.00	10.67
	60	5.72	1.00	23	10.17	5.50	0.96	21	9.67	5.02	0.88	25.00	10.67
90	6.31	1.00	24	10.42	5.83	0.92	21	9.67	5.44	0.86	25.00	10.67

Table 7

Selected 50-year response values for all three mooring configurations

			Ratio, $R_{i, 1}$					Ratio, $R_{i, 1}$
Response, Y	β_wave (deg)	SNL300 Y_50–year	R_2,1	R_3,1	Response, Y	β_wave (deg)	SNL300 Y_50–year	R_2,1	R_3,1
Surge (m)	0	22.02	1.04	1.14	Sway (m)	0	3.42	0.96	0.80
	30	22.19	1.03	1.15		30	4.10	0.95	0.87
	60	22.27	1.03	1.14		60	4.36	0.96	0.93
90	22.15	1.04	1.14	90	4.52	0.97	0.90
Yaw (deg)	0	5.69	0.97	0.88	TwrBsMxt (kN⋅m)	0	1.60 × 10⁵	0.99	0.99
	30	6.17	0.95	0.91		30	3.08 × 10⁵	1.00	1.01
	60	5.72	0.96	0.88		60	4.59 × 10⁵	1.00	1.00
90	6.31	0.92	0.86	90	3.58 × 10⁵	1.00	1.01
TFair1/2 (kN)	0	4.35 × 10³	1.00	1.01	TFair2/3 (kN)	0	2.12 × 10³	1.00	1.07
	30	4.36 × 10³	1.01	1.02		30	2.13 × 10³	1.02	1.09
	60	4.36 × 10³	1.02	1.04		60	2.17 × 10³	1.01	1.08
90	4.35 × 10³	1.02	1.03	90	2.20 × 10³	1.01	1.06
TFair3/6 (kN)	0	2.17 × 10³	1.05	1.12	TAnch1/2 (kN)	0	2.49 × 10³	1.01	1.01
	30	2.21 × 10³	1.05	1.10		30	2.51 × 10³	1.03	1.05
	60	2.24 × 10³	1.05	1.10		60	2.51 × 10³	1.04	1.08
90	2.24 × 10³	1.05	1.11	90	2.49 × 10³	1.04	1.07

			Ratio, $R_{i, 1}$					Ratio, $R_{i, 1}$
Response, Y	β_wave (deg)	SNL300 Y_50–year	R_2,1	R_3,1	Response, Y	β_wave (deg)	SNL300 Y_50–year	R_2,1	R_3,1
Surge (m)	0	22.02	1.04	1.14	Sway (m)	0	3.42	0.96	0.80
	30	22.19	1.03	1.15		30	4.10	0.95	0.87
	60	22.27	1.03	1.14		60	4.36	0.96	0.93
90	22.15	1.04	1.14	90	4.52	0.97	0.90
Yaw (deg)	0	5.69	0.97	0.88	TwrBsMxt (kN⋅m)	0	1.60 × 10⁵	0.99	0.99
	30	6.17	0.95	0.91		30	3.08 × 10⁵	1.00	1.01
	60	5.72	0.96	0.88		60	4.59 × 10⁵	1.00	1.00
90	6.31	0.92	0.86	90	3.58 × 10⁵	1.00	1.01
TFair1/2 (kN)	0	4.35 × 10³	1.00	1.01	TFair2/3 (kN)	0	2.12 × 10³	1.00	1.07
	30	4.36 × 10³	1.01	1.02		30	2.13 × 10³	1.02	1.09
	60	4.36 × 10³	1.02	1.04		60	2.17 × 10³	1.01	1.08
90	4.35 × 10³	1.02	1.03	90	2.20 × 10³	1.01	1.06
TFair3/6 (kN)	0	2.17 × 10³	1.05	1.12	TAnch1/2 (kN)	0	2.49 × 10³	1.01	1.01
	30	2.21 × 10³	1.05	1.10		30	2.51 × 10³	1.03	1.05
	60	2.24 × 10³	1.05	1.10		60	2.51 × 10³	1.04	1.08
90	2.24 × 10³	1.05	1.11	90	2.49 × 10³	1.04	1.07

It is found that, in general, the different mooring configurations do not change the critical sea state for most of the response measures studied, except for the platform yaw motion. Using the 2D EC method, the critical sea state for the SNL630 model is identified to be that for winds slightly below the cut-out wind speed, i.e., around 20 m/s.

The different mooring configuration options have a relative large influence on platform surge, sway, and yaw motions. The extreme surge motion increases by about 14% when the SNL660 model is used relative to the SNL300 model, but only by about 4% increment with the SNL630 model. Reduction in both sway and yaw motions is achieved with the more spatially spread out mooring configurations; this is especially true for the yaw motion and when β_wave is large (greater wind-wave misalignment).

Different from the significant effects of misalignment on the side-to-side tower base bending moment, mooring line patterns have a limited influence on this response measure. The reason for this is that no significant platform roll motion changes are observed with the different mooring patterns.

Regardless of the reduction in platform sway and yaw motion, only slight increments are observed for the fairlead strain as the mooring lines are spread out more. The increments tend to be larger for the side mooring lines, line nos. 2–3 in SNL300 and line nos. 3–6 in SNL630 and SNL660. As the mooring lines are spread out more, to control the surge motion, greater resistance in the longitudinal surge direction is required. As a result, higher tensions and strains would occur as is shown here for the SNL630 and SNL660 models. Nonzero anchor tensions are only observed at position 1 for SNL300 and at position 1&2 for the SNL630 and SNL660 models. Only a 1% increment is found for Anchor 1/2 when $β_{wave} = 0$ ⁠. This increment becomes larger up to about 7% as β_wave goes from 0 deg to 90 deg. However, one should note that even in the 90 deg misalignment case, no anchor tension results at positions 2–3/4–6. These findings could be potentially used to further optimize the mooring system by decreasing the mooring line lengths for nos. 2–3/4–6 and increasing them for nos. 1/1–2.

Conclusions

The influence of misaligned wind and waves on a large floating offshore wind turbine supported on a semisubmersible platform with three alternative mooring systems and for selected environmental conditions is investigated. Of interest are 50-year return period values for various response measures. A reduced fore-aft tower base bending moment is observed for greatly misaligned wind-wave conditions (i.e., where $β_{wave} = 90$ deg); on the other hand, side-to-side tower base bending moments are more than two times larger at this greatest misalignment.

No significant variation is observed in mooring line tensions due to the long mooring lines used in these models. In the most severe sea states, the mooring lines are still lying mostly on the seabed. Further optimization to the mooring systems can still be achieved in principle.

Sway and yaw motion reduction could be achieved with mooring configurations that are more spatially spread; but this comes at the expense of increased extreme surge motion. Larger fairlead and anchor strains result as the interline angle increases in the mooring system.

In general, additional simulations employed with the 2D EC method and/or the consideration of response uncertainty in 3D Inverse first-order reliability method can help to further refine and justify findings claimed regarding wind-wave misalignment and alternative mooring systems.

Acknowledgment

The authors acknowledge the technical assistance received from Edwin Thomas, Watsamon Sahasakkul, and Mohit Soni.

Funding Data

Sandia National Laboratories (1307455).

Nomenclature

H_s =: significant wave height
T_p =: wave spectrum peak period
U_w =: mean wind speed at a 10 m height
β_T =: reliability index with return period T years

References

1.

Jonkman

,

J.

,

Butterfield

,

S.

,

Musial

,

W.

, and

Scott

,

G.

,

2009

, “

Definition of a 5-MW Reference Wind Turbine for Offshore System Development

,” National Renewable Energy Laboratory, Golden, CO, Technical Report No.

NREL/TP-500-38060

.https://www.nrel.gov/docs/fy09osti/38060.pdf

2.

Stewart

,

G.

,

Lackner

,

M.

,

Robertson

,

A.

,

Jonkman

,

J.

, and

Goupee

,

A.

,

2012

, “

Calibration and Validation of a FAST Floating Wind Turbine Model of the DeepCwind Scaled Tension-Leg Platform

,” National Renewable Energy Laboratory (NREL), Golden, CO, Technical Report No.

NREL/EL-500-54822 (U.S.)

.https://www.nrel.gov/docs/fy12osti/54822.pdf

3.

Jonkman

,

J. M.

, and

Musial

,

W.

,

2010

, “

Offshore Code Comparison Collaboration (OC3) for IEA Task 23 Offshore Wind Technology and Deployment

,” National Renewable Energy Laboratory, Golden, CO, Technical Report No.

NREL/TP-5000-48191

.https://www.nrel.gov/docs/fy11osti/48191.pdf

4.

Vorpahl

,

F.

,

Popko

,

W.

, and

Kaufer

,

D.

,

2011

, “

Description of a Basic Model of the ‘UpWind Reference Jacket’ for Code Comparison in the OC4 Project Under IEA Wind Annex XXX

,” Fraunhofer Institute for Wind Energy and Energy System Technology, Bremerhaven, Germany,

Technical Report

.https://www.researchgate.net/profile/Fabian_Vorpahl/publication/267233701_Description_of_a_basic_model_of_the_UpWind_reference_jacket_for_code_comparison_in_the_OC4_project_under_IEA_Wind_Annex_XXX/links/5465bf6d0cf2f5eb17ff560d/Description-of-a-basic-model-of-the-UpWind-reference-jacket-for-code-comparison-in-the-OC4-project-under-IEA-Wind-Annex-XXX.pdf

5.

Jonkman

,

J.

,

Robertson

,

A.

,

Popko

,

W.

,

Vorpahl

,

F.

,

Zuga

,

A.

,

Kohlmeier

,

M.

,

Larsen

,

T. J.

,

Yde

,

A.

,

Saeterto

,

K.

,

Okstad

,

K. M.

,

Nichols

,

J.

,

Nygaard

,

T. A.

,

Gao

,

Z.

,

Manolas

,

D.

,

Kim

,

K.

,

Yu

,

Q.

,

Shi

,

W.

,

Park

,

H.

,

Vasquez-Rojas

,

A.

,

Dubois

,

J.

,

Kaufer

,

D.

,

Thomassen

,

P.

,

de Ruiter

,

M. J.

,

Peeringa

,

J. M.

,

Zhiwen

,

H.

, and

von Waaden

,

H.

,

2012

, “

Offshore Code Comparison Collaboration Continuation (OC4), Phase I-Results of Coupled Simulations of an Offshore Wind Turbine With Jacket Support Structure

,”

The Twenty-Second International Offshore and Polar Engineering Conference, International Society of Offshore and Polar Engineers

, Rhodes, Greece, June 17–22, Paper No.

NREL/CP-5000-54124

.https://www.nrel.gov/docs/fy12osti/54124.pdf

6.

Griffith

,

D. T.

, and

Resor

,

B. R.

,

2011

, “

Description of Model Data for SNL 13.2-00-Land: A 13.2 MW Land-Based Turbine Model With SNL 100-00 Blades

,” Sandia National Laboratories, Albuquerque, NM, Technical Report No.

SAND2011-9310P

.http://energy.sandia.gov/wp-content/gallery/uploads/SNL13.2-00-SAND2011-9310P.pdf

7.

Liu

,

J.

,

Thomas

,

E.

,

Manuel

,

L.

,

Griffith

,

D. T.

,

Ruehl

,

K. M.

, and

Barone

,

M.

,

2018

, “

Integrated System Design for a Large Wind Turbine Supported on a Moored Semi-Submersible Platform

,”

J. Mar. Sci. Eng.

,

6

(

1

), p.

9

.

Google Scholar

Crossref

8.

Thomas

,

E.

,

Liu

,

J.

,

Goyal

,

A.

, and

Manuel

,

L.

,

2016

, “

Long-Term Loads on a Large Offshore Wind Turbine Supported by a Semi-Submersible Platform

,”

AIAA

Paper No. AIAA 2016-1995.

9.

Fischer

,

T.

,

Rainey

,

P.

,

Bossanyi

,

E.

, and

Kühn

,

M.

,

2011

, “

Study on Control Concepts Suitable for Mitigation of Loads From Misaligned Wind and Waves on Offshore Wind Turbines Supported on Monopiles

,”

Wind Eng.

,

35

(

5

), pp.

561

–

573

.

Google Scholar

Crossref

10.

IEC

,

2009

, “

Wind Turbines—Part 3: Design Requirements for Offshore Wind Turbines

,” International Electrotechnical Commission, Geneva, Switzerland, Standard No.

IEC 61400-3

.https://collections.iec.ch/std/series/iec61400-3%7Bed1.0%7Den.nsf/doc.xsp?open&documentId=5A5C5AD96C9C1BD9C1257CCA0030D028

11.

Philippe

,

M.

,

Babarit

,

A.

, and

Ferrant

,

P.

,

2013

, “

Modes of Response of an Offshore Wind Turbine With Directional Wind and Waves

,”

Renewable Energy

,

49

, pp.

151

–

155

.

Google Scholar

Crossref

12.

Ramachandran

,

G. K.

,

Bredmose

,

H.

,

Sørensen

,

J. N.

, and

Jensen

,

J. J.

,

2014

, “

Fully Coupled Three-Dimensional Dynamic Response of a Tension-Leg Platform Floating Wind Turbine in Waves and Wind

,”

ASME J. Offshore Mech. Arct. Eng.

,

136

(

2

), p.

020901

.

Google Scholar

Crossref

13.

Agarwal

,

P.

, and

Manuel

,

L.

,

2008

, “

Extreme Loads for an Offshore Wind Turbine Using Statistical Extrapolation From Limited Field Data

,”

Wind Energy

,

11

(

6

), pp.

673

–

684

.

Google Scholar

Crossref

14.

Ronold

,

K.

,

Wedel-Heinen

,

J.

, and

Christensen

,

C. J.

,

1999

, “

Reliability-Based Fatigue Design of Wind-Turbine Rotor Blades

,”

Eng. Struct.

,

21

(

12

), pp.

1101

–

1114

.

Google Scholar

Crossref

15.

Manuel

,

L.

,

Veers

,

P. S.

, and

Winterstein

,

S. R.

,

2001

, “

Parametric Models for Estimating Wind Turbine Fatigue Loads for Design

,”

ASME J. Sol. Energy Eng.

,

123

(

4

), pp.

346

–

355

.

Google Scholar

Crossref

16.

Lee

,

G.

,

Byon

,

E.

,

Ntaimo

,

L.

, and

Ding

,

Y.

,

2013

, “

Bayesian Spline Method for Assessing Extreme Loads on Wind Turbines

,”

Ann. Appl. Stat.

,

7

(

4

), pp.

2034

–

2061

.

Google Scholar

Crossref

17.

Winterstein

,

S. R.

,

Ude

,

T. C.

,

Cornell

,

C. A.

,

Bjerager

,

P.

, and

Haver

,

S.

,

1993

, “

Environmental Parameters for Extreme Response: Inverse FORM With Omission Factors

,”

Sixth International Conference on Structural Safety and Reliability

(

ICOSSAR

), Innsbruck, Austria, Aug. 9–13, pp. 1–8.https://ff851e5e-a-62cb3a1a-s-sites.googlegroups.com/site/stevewinterstein/publications-1/archival-publications/papers-file-repository/papers-environmental-contours/icossar93.pdf?attachauth=ANoY7cpaaw-eIOyZ9VV7NI6Ej8nxRqqb7V26AL2qscTJX3hCiNlWhVNLAZmh9PzIdBalgQHTYEEShx1EIPxmDRtWTdUvVQA3OT3TjwWVG2Mmjre9p0F48sJV9inNPIOfTzaSHXmh6ulBYX1X18-MJwr9YcJe32pcq9qu4wT8Y4rq7KuYNwC-ZyziBojozxm-KABGdd7DIEadupuNE8SFWf6WK2nMTvxpEr6eT1tolACSjQCPHBwaBIcWCitfV7z_-xryjC6iy9ubY4HrRgygCH51onzgBVTG9ed5AGXaiI0d-7qvdxIGRow1GaBEIHFgKvm9ow858MRMNe5pThSl_ls1YKuEqtQHdQ%3D%3D&attredirects=0

18.

Saranyasoontorn

,

K.

, and

Manuel

,

L.

,

2004

, “

Efficient Models for Wind Turbine Extreme Loads Using Inverse Reliability

,”

J. Wind Eng. Ind. Aerodyn.

,

92

(

10

), pp.

789

–

804

.

Google Scholar

Crossref

19.

Winterstein

,

S. R.

, and

Engebretsen

,

K.

,

1998

, “

Reliability-Based Prediction of Design Loads and Responses for Floating Ocean Structures

,”

17th International Conference on Offshore Mechanics and Arctic Engineering

, Lisbon, Portugal, July 5–6.

20.

Rosenblatt

,

M.

,

1952

, “

Remarks on a Multivariate Transformation

,”

Ann. Math. Stat.

,

23

(

3

), pp.

470

–

472

.

Google Scholar

Crossref

21.

Griffith

,

D.

,

2013

, “

The SNL100-02 Blade: Advanced Core Material Design Studies for the Sandia 100-Meter Blade

,” Sandia National Laboratories, Albuquerque, NM, Technical Report No.

SAND2013-10162

.http://energy.sandia.gov/wp-content/gallery/uploads/dlm_uploads/1310162.pdf

22.

Lee

,

C.

, and

Newman

,

J.

,

2013

, “

WAMIT User Manual, Version 7.0

,” WAMIT, Chestnut Hill, MA, Technical Report.

23.

Masciola

,

M.

,

Jonkman

,

J.

, and

Robertson

,

A.

,

2013

, “

Implementation of a Multisegmented, Quasi-Static Cable Model

,” National Renewable Energy Laboratory, Golden, CO, Technical Report No.

NREL/CP-5000-57812

.http://cim.mcgill.ca/~mmascio1/ISOPE2013-TPC-0646.pdf

24.

Li

,

L.

,

Gao

,

Z.

, and

Moan

,

T.

,

2013

, “

Joint Environmental Data at Five European Offshore Sites for Design of Combined Wind and Wave Energy Devices

,”

ASME

Paper No. OMAE2013-10156.

Alternative Mooring Systems for a Very Large Offshore Wind Turbine Supported by a Semisubmersible Floating Platform

Introduction

Structural Reliability and the Environmental Contour Method

Structural Reliability Problem.

Environmental Contour Method.

Integrated Sandia 13.2 MW Floating Offshore Wind Turbines Model

Integrated Floating Offshore Wind Turbines Model.

Alternative Mooring Systems.

Numerical Studies

Dynamic Analysis Tool.

Environmental Contours.

Results

Wind-Wave Misalignment Effects on 50-Year Return Extreme Responses

Fifty-Year Return Period Rotor and Tower Loads.

Fifty-Year Return Period Mooring Line Loads.

Alternative Mooring Systems.

Conclusions

Acknowledgment

Funding Data

Nomenclature

References

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Alternative Mooring Systems for a Very Large Offshore Wind Turbine Supported by a Semisubmersible Floating Platform

Introduction

Structural Reliability and the Environmental Contour Method

Structural Reliability Problem.

Environmental Contour Method.

Integrated Sandia 13.2 MW Floating Offshore Wind Turbines Model

Integrated Floating Offshore Wind Turbines Model.

Alternative Mooring Systems.

Numerical Studies

Dynamic Analysis Tool.

Environmental Contours.

Results

Wind-Wave Misalignment Effects on 50-Year Return Extreme Responses

Fifty-Year Return Period Rotor and Tower Loads.

Fifty-Year Return Period Mooring Line Loads.

Alternative Mooring Systems.

Conclusions

Acknowledgment

Funding Data

Nomenclature

References

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