Abstract

It is known that the mooring system response of floating production units subjected to environmental loads is nonlinear. Even though wave elevations can be assumed as Gaussian processes for short-term periods, corresponding line tension responses generally are not, due to second-order slow-drift floater motions and intrinsic nonlinearities of the system. In this work, short-term extreme responses are estimated based on two different approaches. In the first one, a number of probability distributions are fitted to the tension time histories’ peaks samples and classic order statistics is applied to determine the most probable extreme line tension corresponding to a short-time period (3-h) in order to identify the one with best performance. The effect of correlation between consecutive peaks in the extremes estimation is investigated through the one-step Markov chain condition by using a Nataf transformation-based model. In the second approach, a more robust and recently developed method named average conditional exceedance rate (ACER) is investigated, where dependencies between maxima can be easily taken into account. Additionally, effects of major parameters in dynamic analyses, such as simulation length and discretization level of the wave spectrum, are evaluated. All time-series-based extreme estimates are compared with the estimates directly obtained from a sample of epochal maxima (Gumbel method). Numerical examples cover two study cases for mooring lines belonging to Floating Production Storage and Offloading (FPSO) units installed offshore Brazil. It is shown that the consideration of dependence between peaks leads to lower extreme estimates and that both approaches return accurate results.

References

References
1.
Taylor
,
R. E.
, and
Kernot
,
M. P.
,
1999
, “
On Second Order Wave Loading and Response in Irregular Seas
,”
Adv. Coastal Ocean Eng.
,
5
, pp.
155
212
. 10.1142/9789812797544_0003
2.
Sagrilo
,
L. V. S.
,
Gao
,
Z.
,
Naess
,
A.
, and
Lima
,
E. C. P.
,
2011
, “
A Straightforward Approach for Using Single Time Domain Simulation to Assess Characteristic Extreme Response
,”
Ocean Eng.
,
38
(
13
), pp.
1464
1471
. 10.1016/j.oceaneng.2011.07.003
3.
Naess
,
A.
, and
Gaidai
,
O.
,
2009
, “
Estimation of Extreme Values From Sampled Time Series
,”
Struct. Saf.
,
31
(
4
), pp.
325
334
. 10.1016/j.strusafe.2008.06.021
4.
DNV-GL
,
2015
,
DnV-OS-E301, Position Mooring, Offshore Standard
,
Det Norske Veritas
,
Hovik, Norway
.
5.
API
,
2017
,
Recommended Practice for Design and Analysis of Station Keeping Systems for Floating Structures
,
API RP-2SK, American Petroleum Institute
,
Washington
, DC.
6.
Low
,
Y. M.
,
2013
, “
A New Distribution Model for Fitting Four Moments and Its Application to Reliability Analysis
,”
Struct. Saf.
,
42
, pp.
12
25
. 10.1016/j.strusafe.2013.01.007
7.
Stanisic
,
D.
,
Efthymiou
,
M.
,
Kimiaei
,
M.
, and
Zhao
,
W.
,
2017
, “
Evaluation of Conventional Methods of Establishing Extreme Mooring Design Loads
,”
36th International Conference on Offshore Mechanics and Arctic Engineering Proceedings
,
Trondheim, Norway
.
8.
Stanisic
,
D.
,
Efthymiou
,
M.
,
Kimiaei
,
M.
, and
Zhao
,
W.
,
2018
, “
Design Loads and Long Term Distribution of Mooring Lines Response of a Large Weathervaning Vessel in a Tropical Cyclone Environment
,”
Mar. Struct.
,
61
, pp.
361
380
. 10.1016/j.marstruc.2018.06.004
9.
Chen
,
C. Y.
, and
Mills
,
T.
,
2010
, “
On Weibull Response of Offshore Structures
,”
29th International Conference on Offshore Mechanics and Arctic Engineering Proceedings
,
Shanghai, China
.
10.
Naess
,
A.
,
Gaidai
,
O.
, and
Karpa
,
O.
,
2013
, “
Estimation of Extreme Values by the Average Conditional Exceedance Rate Method
,”
J. Probab. Stat.
,
2013
, p.
15
. Article ID 797014. 10.1155/2013/797014
11.
Karpa
,
O.
, and
Naess
,
A.
,
2013
, “
Extreme Value Statistics of Wind Speed Data by the ACER Method
,”
J. Wind Eng. Ind. Aerodyn.
,
112
, pp.
1
10
. 10.1016/j.jweia.2012.10.001
12.
Naess
,
A.
, and
Karpa
,
O.
,
2015
, “
Statistics of Extreme Wind Speeds and Wave Heights by the Bivariate ACER Method
,”
ASME J. Offshore Mech. Arct. Eng.
,
137
(
2
), p.
021602
. 10.1115/1.4029370
13.
Naess
,
A.
, and
Moan
,
T.
,
2017
,
Stochastic Dynamics of Marine Structures
,
Cambridge University Press
,
Cambridge, UK
.
14.
BS EN ISO 19901-7:2013
,
2013
,
Petroleum and Natural Gas Industries—Specific Requirements for Offshore Structures. Part 7: Stationkeeping Systems for Floating Offshore Structures and Mobile Offshore Units
,
ISO
,
UK
.
15.
NORSOK
,
2007
,
NORSOK Standard N-003, Actions and Action Effects
,
Standards Norway
,
Norway
.
16.
Winterstein
,
S.
,
Ude
,
T. C.
,
Cornell
,
C. A.
,
Bjerager
,
P.
, and
Haver
,
S.
,
1993
, “
Environmental Parameters for Extreme Response: Inverse FORM with Omission Factors
,”
Proceedings of 6th International Conference on Structural Reliability (ICOSSAR)
,
Innsbruck, Austria
.
17.
Muliawan
,
M. J.
,
Gao
,
Z.
, and
Moan
,
T.
,
2013
, “
Application of the Contour Line Method for Estimating Extreme Responses in the Mooring Lines of a Two-Body Floating Wave Energy Converter
,”
ASME J. Offshore Mech. Arct. Eng.
,
135
(
3
), p.
031301
. 10.1115/1.4024267
18.
Wirshing
,
P. H.
,
Paetz
,
T. L.
, and
Ortiz
,
K.
,
1995
,
Random Vibrations: Theory and Practice
, 2nd ed.,
Dover Publications, Inc.
,
New York
.
19.
Sagrilo
,
L. V. S.
,
Naess
,
A.
, and
Gao
,
Z.
,
2012
, “
On the Extreme Value Analysis of the Response of a Turret-Moored FPSO
,”
ASME J. Offshore Mech. Arct. Eng.
,
134
(
4
), p.
041603
. 10.1115/1.4006759
20.
Ang
,
A. H. S.
, and
Tang
,
W. H.
,
1984
,
Probability Concepts in Engineering Planning and Design
, 2nd ed.,
John Wiley and Sons
,
New York
.
21.
Sagrilo
,
L. V. S.
,
Siqueira
,
M. Q.
,
Ellwanger
,
G. B.
,
Lima
,
E. C. P.
,
Ferreira
,
M. D. A. S.
, and
Mourelle
,
M. M.
,
2002
, “
A Coupled Approach for Dynamic Analysis of CALM Systems
,”
Appl. Ocean Res.
,
24
(
1
), pp.
47
58
. 10.1016/S0141-1187(02)00008-1
22.
Michelen
,
C. A.
, and
Coe
,
R. G.
,
2015
, “
Comparison of Methods for Estimating Short-Term Extreme Response of Wave Energy Converters
,”
OCEANS 2015—MTS/IEEE
,
Washington
.
23.
Naess
,
A.
,
1984
, “
The Effect of the Markov Chain Condition on the Prediction of Extreme Values
,”
J. Sound Vib.
,
94
(
1
), pp.
87
103
. 10.1016/S0022-460X(84)80007-3
24.
Liu
,
P.
, and
Kiureghian
,
A. D.
,
1996
, “
Multivariate Distribution Models With Prescribed Marginals and Covariances
,”
Probab. Eng. Mech.
,
1
(
2
), pp.
105
112
. 10.1016/0266-8920(86)90033-0
25.
Madsen
,
H. O.
,
Krenk
,
S.
, and
Lind
,
N. C.
,
1986
,
Methods of Structural Safety
,
Prentice-Hall, Englewood Cliffs
,
NJ
.
26.
Karpa
,
O.
,
2012
, “
Development of Bivariate Extreme Value Distributions for Applications in Marine Technology
,”
Ph.D. thesis
,
Norwegian University of Science and Technology (NTNU)
,
Trondheim, Norway
.
27.
Dynasim System Manual—Version 1.4
,
2001
,
Dynamic Analysis of Anchoring Systems
,
Pontificial Catholic University of Rio de Janeiro
,
Rio de Janeiro, Brazil
.
28.
Nishimoto
,
K.
,
Fucatu
,
C. H.
, and
Masetti
,
I. Q.
,
2002
, “
Dynasim—A Time Domain Simulator of Anchored FPSO
,”
ASME J. Offshore Mech. Arct. Eng.
,
124
(
4
), pp.
203
211
. 10.1115/1.1513176
29.
Blackman
,
R. B.
, and
Tukey
,
J. W.
,
1958
,
The Measurement of Power Spectra
,
Dover Publications
,
New York
.
30.
Hasselmann
,
K.
, and
Olbers
,
D.
,
1973
, “
Measurements of Wind-Wave Growth and Swell Decay During the Joint North Sea Wave Project (JONSWAP)
,”
Ergänzungsheft zur Deutschen Hydrographischen Zeitschrift
,
8
(
12
), pp.
1
95
.
You do not currently have access to this content.