Analytical and Computational Modeling for Multi-Degree of Freedom Systems: Estimating the Likelihood of an FOWT Structural Failure

Gaidai, Oleg; Xu, Jinliang; Yakimov, Vladimir; Wang, Fang

doi:10.3390/jmse11061237

Cited by 35 publications

(4 citation statements)

References 60 publications

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“…Figure 5a) presents prediction by Gumbel method, it is seen that advocated methodology in Fig. 5b) a) performs significantly more accurate than Gumbel extrapolation method, in terms of CI, however predicted risk levels are very close, namely near 0.03% [35,[52][53][54][55][56][57][58].…”

Section: Resultsmentioning

confidence: 93%

Dementia death rates prediction

Gaidai,

Yakimov,

Balakrishna

2023

BMC Psychiatry

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Background Prevalence of dementia illness, causing certain morbidity and mortality globally, places burden on global public health. This study primary goal was to assess future risks of dying from severe dementia, given specific return period, within selected group of regions or nations. Methods Traditional statistical approaches do not have benefits of effectively handling large regional dimensionality, along with nonlinear cross-correlations between various regional observations. In order to produce reliable long-term projections of excessive dementia death rate risks, this study advocates novel bio-system reliability technique, that being particularly suited for multi-regional environmental, biological, and health systems. Data Raw clinical data has been used as an input to the suggested population-based, bio-statistical technique using data from medical surveys and several centers. Results Novel spatiotemporal health system reliability methodology has been developed and applied to dementia death rates raw clinical data. Suggested methodology shown to be capable of dealing efficiently with spatiotemporal clinical observations of multi-regional nature. Accurate disease risks multi-regional spatiotemporal prediction being done, relevant confidence intervals have been presented as well. Conclusions Based on available clinical survey dataset, the proposed approach may be applied in a variety of clinical public health applications. Confidence bands, given for predicted dementia-associated death rate levels with return periods of interest, have been reasonably narrow, indicating practical values of advocated prognostics.

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Section: Resultsmentioning

confidence: 93%

Dementia death rates prediction

Gaidai,

Yakimov,

Balakrishna

2023

BMC Psychiatry

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“…[32][33][34][35][36][37][38] The bivariate joint CDF 𝑃(𝜉, 𝜂) ∶ = Prob ( X𝑁 ≤ 𝜉, Ŷ𝑁 ≤ 𝜂) of the vector in two-dimensional (2D) ( X𝑁 , Ŷ𝑁 ), with components X𝑁 = max{𝑋 𝑗 ; 𝑗 = 1, … , 𝑁}, and Ŷ𝑁 = max{𝑌 𝑗 ; 𝑗 = 1, … , 𝑁}, is studied here, hence serving as an illustration of a 2D dynamic system. 31,[39][40][41][42][43][44][45][46][47][48] As critical/hazard thresholds, unidimensional extreme system components values with few months return periods and associated probabilities 𝑝 were selected, see Figure 7. Following scaling was done to combine both measured time series 𝑋, 𝑌, according to Equation (5), resulting in each of the two replies having a failure/hazard limit of 1.…”

Section: Resultsmentioning

confidence: 99%

On safe offshore energy exploration in the Gulf of Eilat

Sun

Gaidai

Xing

et al. 2023

Quality & Reliability Eng

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Gulf of Eilat is rich with energy resources, however any industrial natural resource development requires additional safety, as local eco‐system has to be preserved. In contrast to bivariate reliability approaches, known to their accurate predictions of extreme response and load levels for two‐dimensional dynamic systems, this study suggests and validates novel structural reliability method, which is being appropriate method for high‐dimensional dynamic systems. Conventional reliability methods do not have an advantage of dealing easily with high‐dimensional nonlinear dynamic systems, especially with non‐linear cross‐correlations between different system components. Advocated approach does not have limitations on the system's number of degrees of freedom, and it can accurately assess dynamic system's failure risks. Main purpose of this study was to benchmark state‐of‐the‐art reliability methodology, while utilizing available dataset efficiently. Note that advocated approach is not limited to offshore engineering example, studied here, and it has wide range of potential engineering and design applications.

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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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Analytical and Computational Modeling for Multi-Degree of Freedom Systems: Estimating the Likelihood of an FOWT Structural Failure

Cited by 35 publications

References 60 publications

Dementia death rates prediction

Dementia death rates prediction

On safe offshore energy exploration in the Gulf of Eilat

Energy harvester reliability study by Gaidai reliability method

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