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Abstract

Renewable clean energy in some cases may be viewed as an alternative to limited fossil resources. Offshore floating wind turbines (FWTs) are among the most attractive green alternatives. However, FWTs, in particular their essential components, may sustain structural damages from cyclic loads brought on by torque, bending, longitudinal loadings, as well as twisting moments. Multibody simulation tool SIMPACK was utilized to assess structural bending moments and internal forces occurring within the FWT drivetrain during its field operation. The novel risk and damage evaluation method advocated in the current study is intended to serve contemporary FWT design, enabling accurate assessments of structural lifespan distribution, given in situ environmental/field conditions. The approach described in the current study may be utilized to analyze complex multidimensional sustainable energy systems, subjected to excessive stressors during their intended service life. Contemporary risk evaluation approaches, dealing with complex energy systems are not always well-suited for handling dynamic system's high dimensionality, aggravated by nonlinear cross-correlations between structural components, subjected to dynamic nonlinear nonstationary loadings. The current study advocates a novel general-purpose lifetime assessment methodology, having a wide area of potential engineering and design applications, not limited to offshore wind/wave renewable energy systems. Key advantages of the advocated methodology lie within its robust ability to assess damage risks of complex energy and environmental systems, with a virtually unlimited number of system components (dimensions), along with the further potential to incorporate nonlinear cross-correlations between system components in real time. Note that to the author's knowledge, there are no comparable risk evaluation methods that can deal with the system's high dimensionality, utilizing raw/unfiltered simulated/measured datasets, beyond one or two-dimensional dynamic systems—except for computationally expensive direct Monte Carlo (MC) simulations.

References

1.
Gaidai
,
O.
, and
Xing
,
Y.
,
2022
, “
Novel Reliability Method Validation for Offshore Structural Dynamic Response
,”
Ocean Eng.
,
266
(
5
), p.
113016
.
2.
Xu
,
X.
,
Xing
,
Y.
,
Gaidai
,
O.
,
Wang
,
K.
,
Patel
,
K.
,
Dou
,
P.
, and
Zhang
,
Z.
,
2022b
, “
A Novel Multi-Dimensional Reliability Approach for Floating Wind Turbines Under Power Production Conditions
,”
Front. Mar. Sci.
,
9
.
3.
International Energy Agency
,
2020
,
World Energy Outlook 2020
,
OECD Publishing
,
Washington, DC
.
4.
Veers
,
P.
, and
Butterfield
,
S.
,
2001
, “
Extreme Load Estimation for Wind Turbines-Issues and Opportunities for Improved Practice
,”
20th 2001 ASME Wind Energy Symposium
,
Reno, NV
,
Jan. 11–14
, p.
44
.
5.
Igba
,
J.
,
Alemzadeh
,
K.
,
Durugbo
,
C.
, and
Henningsen
,
K.
,
2015
, “
Performance Assessment of Wind Turbine Gearboxes Using in-Service Data: Current Approaches and Future Trends
,”
Renewable Sustainable Energy Rev.
,
50
, pp.
144
159
.
6.
IRENA
,
2012
,
Renewable Energy Technologies: Cost Analysis Series
,
Wind Power
,
USA
.
7.
Sheng
,
S.
,
2012
,
Wind Turbine Gearbox Condition Monitoring Round Robin Study-Vibration Analysis (No. NREL/TP-5000-54530)
,
National Renewable Energy Lab. (NREL)
,
Golden, CO, USA
.
8.
Veers
,
P. S.
, and
Winterstein
,
S. R.
,
2001
, “
Extreme Load Estimation for Wind Turbines: Issues and Opportunities for Improved Practice
,”
2001 ASME Wind Energy Symposium Technical 39th AIAA Aerospace Sciences Meeting and Exhibit
,
Reno, NV
,
Jan. 11–14
, pp.
245
254
.
9.
Dimitrov
,
N.
,
2016
, “
Comparative Analysis of Methods for Modelling the Short-Term Probability Distribution of Extreme Wind Turbine Loads
,”
Wind Energy
,
19
(
4
), pp.
717
737
.
10.
Madsen
,
P.
,
Pierce
,
K.
, and
Buhl
,
M.
,
1999
, “
Predicting Ultimate Loads for Wind Turbine Design
,”
37th Aerospace Sciences Meeting and Exhibit
,
Reno, NV
,
11–14
, p.
69
.
11.
Ronold
,
K. O.
,
Wedel-Heinen
,
J.
, and
Christensen
,
C. J.
,
1999
, “
Reliability-Based Fatigue Design of Wind-Turbine Rotor Blades
,”
Eng. Struct.
,
21
(
12
), pp.
1101
1114
.
12.
Ronold
,
K. O.
, and
Larsen
,
G. C.
,
2000
, “
Reliability-Based Design of Wind-Turbine Rotor Blades Against Hazard/Failure in Ultimate Loading
,”
Eng. Struct.
,
22
(
6
), pp.
565
574
.
13.
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
.
14.
Fitzwater
,
L.
, and
Cornell
,
A. C.
,
2002
, “
Predicting the Long Term Distribution of Extreme Loads From Limited Duration Data: Comparing Full Integration and Approximate Methods
,”
ASME J. Sol. Energy Eng.
,
124
(
4
), pp.
378
386
.
15.
Moriarty
,
P. J.
,
Holley
,
W. E.
, and
Butterfield
,
S.
,
2002
, “
Effect of Turbulence Variation on Extreme Loads Prediction for Wind Turbines
,”
ASME J. Sol. Energy Eng.
,
124
(
4
), pp.
387
395
.
16.
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
.
17.
Barreto
,
D.
,
Karimirad
,
M.
, and
Ortega
,
A.
,
2022
, “
Effects of Simulation Length and Flexible Foundation on Long-Term Response Extrapolation of a Bottom-Fixed Offshore Wind Turbine
,”
ASME J. Offshore Mech. Arct. Eng.
,
144
(
3
), p.
032001
.
18.
McCluskey
,
C. J.
,
Guers
,
M. J.
, and
Conlon
,
S. C.
,
2021
, “
Minimum Sample Size for Extreme Value Statistics of Flow-Induced Response
,”
Mar. Struct.
,
79
, p.
103048
.
19.
Fogle
,
J.
,
Agarwal
,
P.
, and
Manuel
,
L.
,
2008
, “
Towards an Improved Understanding of Statistical Extrapolation for Wind Turbine Extreme Loads
,”
Wind Energy
,
11
(
6
), pp.
613
635
. DOI: 10.1002/we.303
20.
Ernst
,
B.
, and
Seume
,
J. R.
,
2012
, “
Investigation of Site-Specific Wind Field Parameters and Their Effect on Loads of Offshore Wind Turbines
,”
Energies
,
5
(
10
), pp.
3835
3855
.
21.
Gaidai
,
O.
,
Wang
,
F.
,
Wu
,
Y.
,
Xing
,
Y.
,
Medina
,
A.
, and
Wang
,
J.
,
2022
, “
Offshore Renewable Energy Site Correlated Wind-Wave Statistics
,”
Probab. Eng. Mech.
,
68
, p.
103207
.
22.
Gaidai
,
O.
,
Xu
,
J.
,
Yakimov
,
V.
, and
Wang
,
F.
,
2023
, “
Liquid Carbon Storage Tanker Disaster Resilience
,”
Environ. Syst. Dec.
,
43
(
4)
, pp.
746
757
.
23.
Gaidai
,
O.
,
Xing
,
Y.
, and
Balakrishna
,
R.
,
2022
, “
Improving Extreme Response Prediction of a Subsea Shuttle Tanker Hovering in Ocean Current Using an Alternative Highly Correlated Response Signal
,”
Results Eng.
,
15
, p.
100593
.
24.
Cheng
,
Y.
,
Gaidai
,
O.
,
Yurchenko
,
D.
,
Xu
,
X.
, and
Gao
,
S.
,
2022
, “
Study on the Dynamics of a Payload Influence in the Polar Ship
,”
The 32nd International Ocean and Polar Engineering Conference, Paper Number: ISOPE-I-22-342
.
25.
Gaidai
,
O.
,
Fu
,
S.
, and
Xing
,
Y.
,
2022
, “
Novel Reliability Method for Multidimensional Nonlinear Dynamic Systems
,”
Mar. Struct.
,
86
, p.
103278
.
26.
Gaidai
,
O.
,
Yan
,
P.
, and
Xing
,
Y.
,
2022
, “
A Novel Method for Prediction of Extreme Windspeeds Across Parts of Southern Norway,”
Front. Environ. Sci.
,
10
.
27.
Gaidai
,
O.
,
Yan
,
P.
, and
Xing
,
Y.
,
2022
, “
Prediction of Extreme Cargo Ship Panel Stresses by Using Deconvolution
,”
Front. Mech. Eng.
,
8
.
28.
Bak
,
C.
,
Zahle
,
F.
,
Bitsche
,
R.
,
Kim
,
T.
,
Yde
,
A.
,
Henriksen
,
L. C.
,
Hansen
,
M. H.
,
Blasques
,
J. P. A. A.
,
Gaunaa
,
M.
,
Natarajan
,
A.
,
2013
, “The DTU 10-MW Reference Wind Turbine.” https://backend.orbit.dtu.dk/ws/portalfiles/portal/55645274/The_DTU_10MW_Reference_Turbine_Christian_Bak.pdf
29.
Falzarano
,
J.
,
Su
,
Z.
, and
Jamnongpipatkul
,
A.
,
2012
, “
Application of Stochastic Dynamical System to Nonlinear Ship Rolling Problems
,”
Proceedings of the 11th International Conference on the Stability of Ships and Ocean Vehicles
,
Athens, Greece
, Sept. 23–28.
30.
Gaidai
,
O.
,
Xu
,
J.
,
Yan
,
P.
,
Xing
,
Y.
,
Zhang
,
F.
, and
Wu
,
Y.
,
2022
, “
Novel Methods for Windspeeds Prediction Across Multiple Locations
,”
Sci. Rep.
,
12
(
1
), p.
19614
.
31.
Gaidai
,
O.
,
Wu
,
Y.
,
Yegorov
,
I.
,
Alevras
,
P.
,
Wang
,
J.
, and
Yurchenko
,
D.
,
2022
, “
Improving Performance of a Nonlinear Absorber Applied to a Variable Length Pendulum Using Surrogate Optimization
,”
J. Vib. Control.
,
30
(
1–2
), pp.
156
168
.
32.
Gaidai
,
O.
,
Wang
,
K.
,
Wang
,
F.
,
Xing
,
Y.
, and
Yan
,
P.
,
2022
, “
Cargo Ship Aft Panel Stresses Prediction by Deconvolution
,”
Mar. Struct.
,
88
, p.
103359
.
33.
Gaidai
,
O.
,
Xu
,
J.
,
Xing
,
Y.
,
Hu
,
Q.
,
Storhaug
,
G.
,
Xu
,
X.
, and
Sun
,
J.
,
2022
, “
Cargo Vessel Coupled Deck Panel Stresses Reliability Study
,”
Ocean Eng.
,
268
, p.
113318
.
34.
Gaidai
,
O.
,
Yakimov
,
V.
,
Wang
,
F.
,
Hu
,
Q.
, and
Storhaug
,
G.
,
2023
, “
Lifetime Assessment for Container Vessels
,”
Appl. Ocean Res.
,
139
, p.
103708
.
35.
Yakimov
,
V.
,
Gaidai
,
O.
,
Wang
,
F.
,
Xu
,
X.
,
Niu
,
Y.
, and
Wang
,
K.
,
2023
, “
Fatigue Assessment for FPSO Hawsers
,”
Int. J. Nav. Archit. Ocean Eng.
,
15
, p.
100540
.
36.
Graf
,
P. A.
,
Stewart
,
G.
,
Lackner
,
M.
,
Dykes
,
K.
, and
Veers
,
P.
,
2016
, “
High-Throughput Computation and the Applicability of Monte Carlo Integration in Fatigue Load Estimation of Floating Offshore Wind Turbines
,”
Wind Energy
,
19
(
5
), pp.
861
872
.
37.
Fitzwater
,
L. M.
, and
Winterstein
,
S. R.
,
2001
, “
Predicting Design Wind Turbine Loads From Limited Data: Comparing Random Process and Random Peak Models
,”
ASME J. Sol. Energy Eng.
,
123
(
4
), pp.
364
371
.
38.
Moriarty
,
P. J.
,
Holley
,
W. E.
, and
Butterfield
,
S. P.
,
2004
,
Extrapolation of Extreme and Fatigue Loads Using Probabilistic Methods (No. NREL/TP-500-34421)
,
National Renewable Energy Lab
,
Golden, CO
.
39.
Freudenreich
,
K.
, and
Argyriadis
,
K.
,
2007, July
, “
The Load Level of Modern Wind Turbines According to IEC 61400-1
,”
J. Phys. Conf. Ser.
,
75
(
1
), p.
012075
.
40.
Ragan
,
P.
, and
Manuel
,
L.
,
2008
, “
Statistical Extrapolation Methods for Estimating Wind Turbine Extreme Loads
,”
ASME J. Sol. Energy Eng.
,
130
(
3
), p. 031011.
41.
Peeringa
,
J. M.
,
2009
,
Comparison of Extreme Load Extrapolations Using Measured and Calculated Loads of a MW Wind Turbine
,
ECN
,
Petten
.
42.
Abdallah
,
I.
,
2015
,
Assessment of Extreme Design Loads for Modern Wind Turbines Using the Probabilistic Approach
,
DTU Wind Energy
,
Denmark
.
43.
Stewart
,
G. M.
,
Lackner
,
M. A.
,
Arwade
,
S. R.
,
Hallowell
,
S.
, and
Myers
,
A. T.
,
2015
, “
Statistical Estimation of Extreme Loads for the Design of Offshore Wind Turbines During Non-Operational Conditions
,”
Wind Eng.
,
39
(
6
), pp.
629
640
.
44.
Sun
,
J.
,
Gaidai
,
O.
,
Xing
,
Y.
,
Wang
,
F.
, and
Liu
,
Z.
,
2023
, “
On Safe Offshore Energy Exploration in the Gulf of Eilat
,”
Qual. Reliab. Eng. Int.
,
39
(
7
), p.
2957
2966
.
45.
Yakimov
,
V.
,
Gaidai
,
O.
,
Wang
,
F.
, and
Wang
,
K.
,
2023
, “
Arctic Naval Launch and Recovery Operations, Under ice Impact Interactions
,”
Appl. Eng. Sci.
,
15
,
100146
.
46.
Gaidai
,
O.
,
Xu
,
J.
,
Hu
,
Q.
,
Xing
,
Y.
, and
Zhang
,
F.
,
2022
, “
Offshore Tethered Platform Springing Response Statistics
,”
Sci. Rep.
,
12
, p.
21182
.
47.
Gaidai
,
O.
,
Xing
,
Y.
, and
Xu
,
X.
,
2023
, “
Novel Methods for Coupled Prediction of Extreme Windspeeds and Wave Heights
,”
Sci. Rep.
,
13
(
1)
.
48.
Gaidai
,
O.
,
Cao
,
Y.
,
Xing
,
Y.
, and
Wang
,
J.
,
2023
, “
Piezoelectric Energy Harvester Response Statistics
,”
Micromachines
,
14
(
2
), p.
271
.
49.
Gaidai
,
O.
,
Yakimov
,
V.
,
Wang
,
F.
,
Zhang
,
F.
, and
Balakrishna
,
R.
,
2023
, “
Floating Wind Turbines Structural Details Fatigue Life Assessment
,”
Sci. Rep.
,
13
(
1
).
50.
Gaidai
,
O.
,
Cao
,
Y.
,
Xing
,
Y.
, and
Balakrishna
,
R.
,
2023
, “
Extreme Springing Response Statistics of a Tethered Platform by Deconvolution
,”
Int. J. Nav. Archit. Ocean Eng.
,
15
, p.
100515
.
51.
Gaidai
,
O.
,
Xing
,
Y.
,
Balakrishna
,
R.
, and
Xu
,
J.
,
2023
, “
Improving Extreme Offshore Windspeed Prediction by Using Deconvolution
,”
Heliyon
,
9
(
2
), p.
e13533
.
52.
Gaidai
,
O.
,
Wang
,
F.
,
Yakimov
,
V.
,
Sun
,
J.
, and
Balakrishna
,
R.
,
2023
, “
Lifetime Assessment for Riser Systems
,”
Green Tech. Res. Sustain.
,
3
(
1
).
53.
Gaidai
,
O.
,
Sheng
,
J.
,
Cao
,
Y.
,
Zhu
,
Y.
, and
Liu
,
Z.
,
2024
, “
Evaluating Areal Windspeeds and Wave Heights by Gaidai Risk Evaluation Method
,”
Nat. Hazard. Rev.
,
25
(
4
).
54.
Gaidai
,
O.
,
Li
,
H.
,
Cao
,
Y.
,
Ashraf
,
A.
,
Zhu
,
Y.
, and
Liu
,
Z.
,
2024
, “
Shuttle Tanker Operational Reliability Study by Gaidai Multivariate Risk Assessment Method, Utilizing Deconvolution Scheme
,”
Transp. Res. Interdiscip. Perspect.
,
26
, p.
101194
.
55.
Gaidai
,
O.
,
Li
,
H.
,
Cao
,
Y.
,
Liu
,
Z.
,
Zhu
,
Y.
, and
Sheng
,
J.
,
2024
, “
Wind Turbine Gearbox Reliability Verification by Multivariate Gaidai Reliability Method
,”
Results Eng.
,
23
, p.
102689
.
56.
Gaidai
,
O.
,
Cao
,
Y.
,
Wang
,
F.
, and
Zhu
,
Y.
,
2024
, “
Applying the Multivariate Gaidai Reliability Method in Combination With an Efficient Deconvolution Scheme to Prediction of Extreme Ocean Wave Heights
,”
Mar. Syst. Ocean Technol.
57.
Gaidai
,
O.
,
Ashraf
,
A.
,
Cao
,
Y.
,
Sheng
,
J.
,
Zhu
,
Y.
, and
Li
,
H.
,
2024
, “
Panamax Cargo-Vessel Excessive-Roll Dynamics Based on Novel Deconvolution Method
,”
Probab. Eng. Mech.
,
77
, p.
103676
.
58.
Gaidai
,
O.
,
Liu
,
Z.
,
Cao
,
Y.
,
Sheng
,
J.
,
Zhu
,
Y.
, and
Zhang
,
F.
,
2024
, “
Novel Multivariate Design Concept for Floating Wind Turbines by Gaidai Multivariate Reliability Method and Deconvolution Scheme
,”
J. Low Freq. Noise Vibr. Act. Control
.
59.
Gaidai
,
O.
,
Ashraf
,
A.
,
Cao
,
Y.
,
Zhu
,
Y.
,
Sheng
,
J.
,
Li
,
H.
, and
Liu
,
Z.
,
2024
, “
Multivariate Ocean Waves Dynamics in North Sea and Norwegian Sea by Gaidai Reliability Method
,”
Energy Rep.
,
12
, pp.
2346
2355
.
60.
Nejad
,
A.
,
Guo
,
Y.
,
Gao
,
Z.
, and
Moan
,
T.
, “
Development of a 5 MW Reference Gearbox for Offshore Wind Turbines
,”
Wind Energy
,
19
(
6
), pp.
1089
1106
.
61.
Gaidai
,
O.
,
Yakimov
,
V.
,
Wang
,
F.
, and
Zhang
,
F.
,
2023
, “
Safety Design Study for Energy Harvesters
,”
Sustainable Energy Res.
,
10
(
1
).
62.
Gaidai
,
O.
,
Yakimov
,
V.
, and
van Loon
,
E.-J.
,
2023
, “
Influenza-Type Epidemic Risks by Spatio-Temporal Gaidai-Yakimov Method
,”
Dialogues Health
,
3
(
2
), p.
100157
.
63.
Gaidai
,
O.
,
Yakimov
,
V.
,
Niu
,
Y.
, and
Liu
,
Z.
,
2023
, “
Gaidai-Yakimov Reliability Method for High-Dimensional Spatio-Temporal Biosystems
,”
Biosystems
,
235
, p.
105073
.
64.
Gaidai
,
O.
,
Yakimov
,
V.
,
Sun
,
J.
, and
van Loon
,
E.-J.
,
2023
, “
Singapore COVID-19 Data Cross-Validation by the Gaidai Reliability Method
,”
npj Viruses
,
1
(1).
65.
Sun
,
J.
,
Gaidai
,
O.
,
Wang
,
F.
, and
Yakimov
,
V.
,
2024
, “
Gaidai Reliability Method for Fixed Offshore Structures
,”
J. Braz. Soc. Mech. Sci. Eng.
,
46
(
27
).
66.
Gaidai
,
O.
,
Wang
,
F.
,
Cao
,
Y.
, and
Liu
,
Z.
,
2024
, “
4400 TEU Cargo Ship Dynamic Analysis by Gaidai Reliability Method
,”
J. Shipp. Trade
,
9
(
1
).
67.
Gaidai
,
O.
,
Wang
,
F.
, and
Sun
,
J.
,
2024
, “
Energy Harvester Reliability Study by Gaidai Reliability Method
,”
Clim. Resilience Sustainability
,
3
(
1
), p.
e64
.
68.
Gaidai
,
O.
,
Sheng
,
J.
,
Cao
,
Y.
,
Zhang
,
F.
,
Zhu
,
Y.
, and
Loginov
,
S.
,
2024
, “
Public Health System Sustainability Assessment by Gaidai Hypersurface Approach
,”
Curr. Probl. Cardiol.
,
49
(
3
), p.
102391
.
69.
Gaidai
,
O.
,
Yakimov
,
V.
,
Hu
,
Q.
, and
Loginov
,
S.
,
2024
, “
Multivariate Risks Evaluation for Complex Bio-Systems by Gaidai Reliability Method
,”
Syst. Soft Comput.
,
6
, p.
200074
70.
Gaidai
,
O.
,
Yakimov
,
V.
,
Wang
,
F.
,
Sun
,
J.
, and
Wang
,
K.
,
2024
, “
Bivariate Reliability Analysis for Floating Wind Turbines
,”
Int. J. Low-Carbon Technol.
,
19
, pp.
63
72
.
71.
Gaidai
,
O.
,
Yan
,
P.
,
Xing
,
Y.
,
Xu
,
J.
, and
Wu
,
Y.
,
2023
, “
Gaidai Reliability Method for Long-Term Coronavirus Modelling
,”
F1000Research
,
11
, p.
1282
.
72.
Gaidai
,
O.
,
Sheng
,
J.
,
Cao
,
Y.
,
Zhu
,
Y.
, and
Loginov
,
S.
,
2024
, “
Generic COVID-19 Epidemic Forecast for Estonia by Gaidai Multivariate Reliability Method
,”
Franklin Open
,
6
,
100075
.
73.
Gaidai
,
O.
,
Sheng
,
J.
,
Cao
,
Y.
,
Zhu
,
Y.
,
Wang
,
K.
, and
Liu
,
Z.
,
2024
, “
Limit Hypersurface State of art Gaidai Reliability Approach for Oil Tankers Arctic Operational Safety
,”
J. Ocean Eng. Mar. Energy
,
10
(
2
), pp.
351
364
.
74.
Gaidai
,
O.
,
Yakimov
,
V.
,
Wang
,
F.
, and
Cao
,
Y.
,
2024
, “
Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety, Given Manufacturing Imperfections
,”
Int. J. Precis. Eng. Manuf.
,
25
(
5
), pp.
1011
1025
.
75.
Gaidai
,
O.
,
Sheng
,
J.
,
Cao
,
Y.
,
Zhang
,
F.
,
Zhu
,
Y.
, and
Liu
,
Z.
,
2024
, “
Gaidai Multivariate Risks Assessment Method for Cargo Ship Dynamics
,”
Urban, Planning and Transport Research
,
12
(
1
).
76.
Gaidai
,
O.
,
2024
, “
Global Health Risks Due to the COVID-19 Epidemic by Gaidai Reliability Method
,”
Sci. Talks
,
10
,
100366
.
77.
Gaidai
,
O.
,
Cao
,
Y.
,
Li
,
H.
,
Liu
,
Z.
,
Ashraf
,
A.
,
Zhu
,
Y.
, and
Sheng
,
J.
,
2024
, “
Multivariate Gaidai Hazard Assessment Method in Combination With Deconvolution Scheme to Predict Extreme Wave Heights
,”
Results Eng.
,
22
, p.
102326
.
78.
Gaidai
,
O.
,
Sun
,
J.
, and
Cao
,
Y.
,
2024
, “
FPSO/FLNG Mooring System Evaluation by Gaidai Reliability Method
,”
J. Mar. Sci. Technol.
79.
Gaidai
,
O.
,
Ashraf
,
A.
,
Cao
,
Y.
,
Sheng
,
J.
,
Zhu
,
Y.
, and
Liu
,
Z.
,
2024
, “
Lifetime Assessment of Semi-Submersible Wind Turbines by Gaidai Risk Evaluation Method
,”
J Mater. Sci. Mater. Eng.
,
19
(
1
), p.
2
.
80.
Gaidai
,
O.
,
Cao
,
Y.
,
Ashraf
,
A.
,
Sheng
,
J.
,
Zhu
,
Y.
, and
Liu
,
Z.
,
2024
, “
FPSO/LNG Hawser System Lifetime Assessment by Gaidai Multivariate Risk Assessment Method
,”
Energy Inform.
,
7
(
1
), p.
51
.
81.
Gaidai
,
O.
,
Cao
,
Y.
,
Zhu
,
Y.
,
Zhang
,
F.
,
Liu
,
Z.
, and
Wang
,
K.
,
2024
, “
Limit Hypersurface State of the Art Gaidai Multivariate Risk Evaluation Approach for Offshore Jacket
,”
Mech. Based Des. Struct. Mach.
,
10
, pp.
351
364
.
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