Cargo vessel coupled deck panel stresses reliability study

Gaidai, Oleg; Xu, Jinliang; Xing, Yihan; Hu, Qingsong; Storhaug, Gaute; Xu, Xiaosen; Sun, Jiayao

doi:10.1016/j.oceaneng.2022.113318

Cited by 31 publications

(24 citation statements)

References 13 publications

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“…The countless strategies endeavoured included the method presented by Mohande [ 26 ], which predicts wind speeds using neural networks or the different approaches to analyse wind speed estimates by Yan et al, as in Refs. [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Similarly, the authors of this paper have also previously used different statistical approaches to estimate better extreme statistical values such as, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the authors of this paper have also previously used different statistical approaches to estimate better extreme statistical values such as, i.e. wind speed, wave height, response and load [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Furthermore, the authors developed a novel reliability approach to forecast COVID-19 epidemic levels, indicating the approach's versatility and reliability in various fields [ 14 , 16 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, other authors, as in Refs. [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ], have attempted to use novel statistical approaches to estimate wind speed or pattern more accurately. Meanwhile, two recent and pertinent scientific articles [ 8 , 26 ] illustrated the importance of wind forecasting and strategies adopted to maximise efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…This is in contrast to many traditional extrapolation methods widely used in engineering practice where an assumed probability function is used [ [1] , [2] , [3] , [4] , [5] , [6] , 30 ]. Some other examples also include the Pareto-based distribution peak over the threshold (POT) [ 1 ], The Naess-Gaidai (NG) method fitting procedure was used in averaged conditional exceedance rate (ACER) method [ [7] , [8] , [9] , [10] , [11] ], bivariate ACER fitting method [ [12] , [13] , [14] , [15] , [16] , 47 ], Weibull distribution-based fitting method [ 38 ] and Gumbel distribution based fitting method [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Case I) uses Monte Carlo simulations to synthetically manufacture annual peak events. Following [ [27] , [28] , [29] , [30] , [31] ], it is reasonable to assume the use of a stationary Gaussian process

with mean = 0 and standard deviation = 1 to represent this case. Correspondingly,

where

is the mean value up-crossing rate and

year.…”

Section: Introductionmentioning

confidence: 99%

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

with mean = 0 and standard deviation = 1 to represent this case. Correspondingly,

where

is the mean value up-crossing rate and

year.…”

Section: Introductionmentioning

confidence: 99%

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Energy harvester reliability study by Gaidai reliability method

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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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Deconvolution approach for floating wind turbines

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Energy Science & Engineering

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Green renewable energy is produced by floating offshore wind turbines (FOWT), a crucial component of the modern offshore wind energy industry. It is a safety concern to accurately evaluate excessive weights while the FOWT operates in adverse weather conditions. Under certain water conditions, dangerous structural bending moments may result in operational concerns. Using commercial FAST software, the study's hydrodynamic ambient wave loads were calculated and converted into FOWT structural loads. This article suggests a Monte Carlo‐based engineering technique that, depending on simulations or observations, is computationally effective for predicting extreme statistics of either the load or the response process. The innovative deconvolution technique has been thoroughly explained. The suggested approach effectively uses the entire set of data to produce a clear but accurate estimate for severe response values and fatigue life. In this study, estimated extreme values obtained using a novel deconvolution approach were compared to identical values produced using the modified Weibull technique. It is expected that the enhanced new de‐convolution methodology may offer a dependable and correct forecast of severe structural loads based on the overall performance of the advised de‐convolution approach due to environmental wave loading.

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Cargo vessel coupled deck panel stresses reliability study

Cited by 31 publications

References 13 publications

Improving extreme offshore wind speed prediction by using deconvolution

Improving extreme offshore wind speed prediction by using deconvolution

Energy harvester reliability study by Gaidai reliability method

Deconvolution approach for floating wind turbines

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