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

Gaidai, Oleg; Xing, Yihan; Wang, Fang; Wang, Shuaishuai; Yan, Ping; Næss, Arvid

doi:10.3389/fmech.2022.888497

Cited by 12 publications

(13 citation statements)

References 23 publications

(18 reference statements)

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“…( 5) follows from Eq. ( 1) by neglecting Prob R 1 ≤ η 1 ≈ 1 , as design failure probability must of a small order of magnitude, and N>>k. Equation ( 5) is similar to mean up-crossing rate equation for probability of exceedance [39][40][41][42][43][44][45][46][47][48][49][50][51] . There is convergence with respect to the conditioning parameter k Note that Eq.…”

Section: Methodsmentioning

confidence: 99%

Novel methods for coupled prediction of extreme wind speeds and wave heights

Gaidai

Xing

2023

Sci Rep

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Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and environmental dynamic system responses that have been numerically simulated or observed over a substantial time range, yielding representative ergodic time series. Next, this work introduces a novel deconvolution extrapolation technique applicable to a wide range of environmental and engineering applications. Classical reliability approaches cannot cope with dynamic systems with high dimensionality and responses with complicated cross-correlation. The combined study of wind speed and wave height is notoriously difficult, since they comprise a very complex, multi-dimensional, non-linear environmental system. Additionally, global warming is a significant element influencing ocean waves throughout the years. Furthermore, the environmental system reliability method is crucial for structures working in any particular region of interest and facing actual and often harsh weather conditions. This research demonstrates the effectiveness of our approach by applying it to the concurrent prediction of wind speeds and wave heights from NOAA buoys in the North Pacific. This study aims to evaluate the state-of-the-art approach that extracts essential information about the extreme responses from observed time histories.

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Section: Methodsmentioning

confidence: 99%

Novel methods for coupled prediction of extreme wind speeds and wave heights

Gaidai

Xing

2023

Sci Rep

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“…However, the proposed approach is equally valid for multidirectional seas, leading to a larger eigenvalue problem. Note that, when crossing levels of interest are large, the CPU time needed for exact integration drops rapidly, as compared to the direct Monte Carlo (MC) simulation [1][2][3][4][5][6][7][8][9][10][11][12] . Since extreme up-crossing rates are quite important ones, this is a practical advantage for the exact saddle point integration method, as compared to the direct MC method.…”

Section: Heave Roll Pitchmentioning

confidence: 99%

“…For the TLP structure studied in this paper, the resonant heave and pitch responses have been influenced by the wave loads generated by combination of the first order and the second order (i.e. sum) frequencies, see [1][2][3][4][5][6][7][8][9][10][11][12][13][14] .…”

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confidence: 99%

Offshore tethered platform springing response statistics

Gaidai

et al. 2022

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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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“…This study outlines a general-purpose, robust and straightforward multidimensional bio-system reliability method. The method introduced in this study has been previously successfully validated by application to a wide range of engineering models, 30,[40][41][42][43][44][45][46][47][48][49] but only for one and two-dimensional system responses and, in general, very accurate predictions were obtained. Both measured and simulated time series responses can be analysed using the proposed method.…”

Section: Use Casementioning

confidence: 99%

A novel statistical method for long-term coronavirus modelling

et al. 2022

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Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

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

Cited by 12 publications

References 23 publications

Novel methods for coupled prediction of extreme wind speeds and wave heights

Novel methods for coupled prediction of extreme wind speeds and wave heights

Offshore tethered platform springing response statistics

A novel statistical method for long-term coronavirus modelling

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