Deconvolution approach for floating wind turbines

Liu, Zirui; Gaidai, Oleg; Sun, Jiayao; Xing, Yihan

doi:10.1002/ese3.1485

Cited by 21 publications

(7 citation statements)

References 51 publications

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“…In this study, the winds and waves statistics were built using hindcast data that was obtained from the North Sea between years 2001 and 2010. The long-term combined wind and wave distribution was made up of the 1-h mean wind speed, which was located 10 m above sea level ( U 10 ), the wave spectral peak period ( T p ) along with the significant wave height ( H s ) 41 . The long-term joint wind-wave PDF was where the marginal distribution of U 10 may be described by and , the conditional PDF of H s for a given U 10 and conditional distribution of T p for a given U 10 and H s .…”

Section: Numerical Resultsmentioning

confidence: 99%

Floating wind turbines structural details fatigue life assessment

Gaidai,

Yakimov,

Wang

et al. 2023

Sci Rep

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Fatigue damage prediction is essential for safety of contemporary offshore energy industrial projects, like offshore wind turbines, that are to be designed for sufficiently long operational period of time, with minimal operational disruptions. Offshore structures being designed to withstand environmental loadings due to winds and waves. Due to accumulated fatigue damage, offshore wind floating turbines may develop material cracks in their critical locations sooner than expected. Dataset needed for an accurate assessment of fatigue damage may be produced by either extensive numerical modeling, or direct measurements. However, in reality, temporal length of the underlying dataset being typically too short to provide an accurate calculation of direct fatigue damage and fatigue life. Hence, the objective of this work is to contribute to the development of novel fatigue assessment methods, making better use of limited underlying dataset. In this study, in-situ environmental conditions were incorporated to assess offshore FWT tower base stresses; then structural cumulative fatigue damage has been assessed. Novel deconvolution extrapolation method has been introduced in this study, and it was shown to be able to accurately predict long-term fatigue damage. The latter technique was validated, using artificially reduced dataset, and resulted in fatigue damage that was shown to be close to the damage, calculated from the full original underlying dataset. Recommended method has been shown to utilize available dataset much more efficiently, compared to direct fatigue estimation. Accurate fatigue assessment of offshore wind turbine structural characteristics is essential for structural reliability, design, and operational safety.

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Section: Numerical Resultsmentioning

confidence: 99%

Floating wind turbines structural details fatigue life assessment

Gaidai,

Yakimov,

Wang

et al. 2023

Sci Rep

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“…Novel method was validated versus a well‐established bivariate Weibull method, using both analytical synthetic data and measured GPEH system dynamics. Finally, advocated Using advocated method for systems with more than two dimensions obviously cannot be validated versus bivariate statistical methods; hence, it can be validated versus direct Monte Carlo simulations; for details, see authors’ latest works (Gaidai, Liu, et al., 2023; Gaidai, Wang, Yakimov, Sun, et al., 2023; Gaidai, Xing, Xu, & Balakrishna, 2023; Gaidai, Xu, Yakimov, et al., 2023; Gaidai, Yakimov, and Balakrishna, 2023; Gaidai, Yakimov, Wang, Hu, et al., 2023; Gaidai, Yakimov, Wang, Zhang, et al., 2023; Gaidai, Yan, Xing, Xu, et al., 2023; Liu et al., 2023; Sun et al., 2023; Yakimov, Gaidai, Wang, & Wang, 2023; Yakimov, Gaidai, Wang, Xu, et al., 2023). Suggested methodology may be well used in a wide range of engineering areas of applications.…”

Section: Discussionmentioning

confidence: 99%

Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

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This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.

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“…, R k ≤ η k ) ≈ 1 , as design failure/damage/hazard probability being of small order of magnitude, with N ≫ k , . Regarding the conditioning parameterk , convergence is required The synthetic − → R -vector of the built system exhibits a net 0 data loss, Zhang et al 2023;Liu et al 2023;. Now that the synthetic vector − → R has been presented, the stochastic longevity variable L may be defined along with the corresponding temporally increasing occurrence temporal instants t 1 < • • • < t N where i = 2, .…”

Section: Gaidai Risks Evaluation Methodsmentioning

confidence: 99%

“…The integrated synthetic process R(t) the FOWT system thus contains crucial information about the target LTD PDF. Currently, target FOWT survival chances/probability can be evaluated using the MUR (i.e., Mean Up-crossing Rate) function P = P(1) with ν + ( ) denoting the MUR function of the hazard/ failure/risk/damage threshold level for the non-dimensional synthetic vector R(t) of the FOWT system as previously discussed, (Liu et al 2023;. For the MUR function, Rice's formula is represented by Eq.…”

Section: Gaidai Risks Evaluation Methodsmentioning

confidence: 99%

Lifetime assessment of semi-submersible wind turbines by Gaidai risk evaluation method

Gaidai,

Ashraf,

Cao

et al. 2024

J Mater. Sci: Mater Eng.

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As the global agenda turns more towards the so-called challenge of climate change and lowering carbon emissions, research into green, renewable energy sources becoming nowadays more and more popular. Offshore wind power, produced by FOWTs (i.e., Floating Offshore Wind Turbines), is one such substitute. It is a significant industrial part of the contemporary offshore wind energy industry and produces clean, renewable electricity. Accurate operational lifetime assessment for FOWTs is an important technical safety issue, as environmental in situ loads can lead to fatigue damage as well as extreme structural dynamics, which can cause structural damage. In this study, in situ environmental hydro and aerodynamic environmental loads, that act on FOWT, given actual local sea conditions have been numerically assessed, using the FAST coupled nonlinear aero-hydro-servo-elastic software package. FAST combines aerodynamics and hydrodynamics models for FOWTs, control and electrical system dynamics models, along with structural dynamics models, enabling coupled nonlinear MC simulation in the real time. The FAST software tool enables analysis of a range of FOWT configurations, including 2- or 3-bladed horizontal-axis rotor, pitch and stall regulation, rigid and teetering hub, upwind and downwind rotors. FAST relies on advanced engineering models—derived from the fundamental laws, however with appropriate assumptions and simplifications, supplemented where applicable with experimental data. Recently developed Gaidai reliability lifetime assessment method, being well suitable for risks evaluation of a variety of sustainable energy systems, experiencing nonlinear, potentially extreme in situ environmental loads, throughout their designed service life. The main advantage of the advocated Gaidai risks evaluation methodology being its ability to tackle simultaneously a large number of dynamic systems' degrees of freedom, corresponding to the system's critical components.

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Deconvolution approach for floating wind turbines

Cited by 21 publications

References 51 publications

Floating wind turbines structural details fatigue life assessment

Floating wind turbines structural details fatigue life assessment

Energy harvester reliability study by Gaidai reliability method

Lifetime assessment of semi-submersible wind turbines by Gaidai risk evaluation method

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