Wind Farm Support Vessel Extreme Roll Assessment While Docking in the Bohai Sea

Xu, Xiaosen; Gaidai, Oleg; Karpa, Oleh; Wang, Junlei; Ye, Ren-chuan; Cheng, Yong

doi:10.1007/s13344-021-0028-x

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…Where 1{A} 1 if A is true, while it is 0 if not. As shown in (Wang, 2001;Bak et al, 2013;Naess and Karpa, 2015a;Naess and Karpa, 2015b;Gaidai et al, 2016;Jian et al, 2018;Gaidai et al, 2019a;Gaidai et al, 2019b;Xu et al, 2019b;Gaidai et al, 2020;Xu et al, 2021)…”

Section: Acer Methodsmentioning

confidence: 87%

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

et al. 2022

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Extreme value prediction of the load-effect responses of complex offshore structures such as the floating wind turbine (FWT) is crucial in ultimate limit state (ULS) design. This paper considers two cases to understand the feasibility of the bivariate correction on the extreme load and motion responses of a 10-MW semi-submersible type FWT. The empirical anchor tension force and surge motion used in this study are obtained from the FAST simulation tool (developed by the National Renewable Energy Laboratory) with the load cases stimulated at under-rated, rated and above rated speeds. Then, the bivariate correction method is applied to model FWT extreme response for a 5-years return period prediction with a 95% confidence interval (CI), based on just 2 min short response record. The proposed methodology permits accurate correction of the bivariate extreme value in case of, for example, corrupted measurement sensor data. Based on the proposed novel method’s performance, it is concluded that the bivariate correction method can offer better robust and precise bivariate predictions of coupled surge motion and anchor tension of the FWT.

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Section: Acer Methodsmentioning

confidence: 87%

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

et al. 2022

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“…It is seen from Figure 5 left that ACER2D fits different Gumbel copula to the measured data, and there is an inherent mismatch with original data (indicated by red dots) due to a particular copula choice; for more details on the ACER2D method, see Refs. [8–43]. The bivariate nondimensional failure point indicated by the star in Figure 5 was chosen purely as an example.…”

Section: Resultsmentioning

confidence: 99%

“…By comparing it to methods that have been previously benchmarked in various applications, the unique methodology presented in this study has been verified. [ 8–43 ] Note that the novel Gaidai‐Fu‐Xing (GFX) method is MDOF (multi‐degree of freedom), while average conditional exceedance rate 2 dimensional (ACER2D) is only limited to 2 degrees of freedom (2DOF) (two dimensions); moreover, ACER2D does not provide confidence intervals (CIs), while the new GFX method can.…”

Section: Introductionmentioning

confidence: 99%

Novel Reliability Method Validation for Floating Wind Turbines

Gaidai

Wang

Xing

et al. 2023

Adv Energy and Sustain Res

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Wind turbines and associated parts are susceptible to cyclic stresses, including torque, bending, and longitudinal stress, and twisting moments. Therefore, research on the resilience of dynamic systems under such high loads is crucial for design and future risk‐free operations. The method described in this study is beneficial for multidimensional structural responses that have undergone sufficient numerical simulation or measurement. In contrast to established dependability methodologies, the unique technique does not need to restart the numerical simulation each time the system fails. Herein, it is demonstrated that it is also possible to accurately predict the probability of a system failure in the event of a measurable structural reaction. In contrast to well‐established bivariate statistical methods, which are known to predict extreme response levels for 2D systems accurately, this study validates a novel structural reliability method that is particularly suitable for multidimensional structural responses. In contrast to conventional methods, the novel reliability approach does not invoke a multidimensional reliability function in the Monte Carlo numerical simulation case. As demonstrated in this study, it is also possible to accurately anticipate the likelihood of a system failure in the case of a measurable structural reaction.

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“…Since this paper is focused mainly on post-processing of direct Monte Carlo (MC) simulation results, there is no focus on the simulation length and CPU time, since there is no comparison to a direct full MC simulation (as in e.g. 21,[31][32][33][34][35][36][37][38][39] ), but rather a comparison with CPU time efficient analytical solution.…”

Section: Discussionmentioning

confidence: 99%

Offshore tethered platform springing response statistics

Gaidai

et al. 2022

Sci Rep

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This paper demonstrates the validity of the Naess–Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess–Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess–Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess–Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response.

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Wind Farm Support Vessel Extreme Roll Assessment While Docking in the Bohai Sea

Cited by 14 publications

References 23 publications

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

Novel Reliability Method Validation for Floating Wind Turbines

Offshore tethered platform springing response statistics

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