Stochastic analysis of explosion risk for ultra-deep-water semi-submersible offshore platforms

Shi, Jihao; Zhu, Yan; Kong, De‐Xing; Khan, Fasial; Li, Jingde; Chen, Guo-Ming

doi:10.1016/j.oceaneng.2018.12.045

Cited by 27 publications

(8 citation statements)

References 15 publications

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“…Five of their collaborators, each being affiliated with institutions in two different countries (China and other countries), were involved in seven out of their ten articles (Guo et al, 2018;Shi et al, 2018a;Shi et al, 2018b;Shi et al, 2019;Wu et al, 2014;Wu et al, 2016;Yu et al, 2018). The reason why these scholars are affiliated with two institutions is that they carried out academic exchanges and…”

Section: International Cooperationmentioning

confidence: 99%

A bibliometric analysis of process safety research in China: Understanding safety research progress as a basis for making China’s chemical industry more sustainable

Yang

Chen

Reniers

et al. 2020

Journal of Cleaner Production

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Section: International Cooperationmentioning

confidence: 99%

A bibliometric analysis of process safety research in China: Understanding safety research progress as a basis for making China’s chemical industry more sustainable

Yang

Chen

Reniers

et al. 2020

Journal of Cleaner Production

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“…FPSO structures are equipped with many installations and systems for oil and gas production in limited space, and even generate lots of flammable gaseous material during operation. Because of such a complexity, more variables and correlation between them must be considered than onshore plant generally employing the simplified gas dispersion and explosion models [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of a Computational Fluid Dynamics (CFD)-Based Explosion Numerical Analysis of Offshore Facilities

Kang

Wang

et al. 2022

Arch Computat Methods Eng

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In oil and gas industries, the explosive hazards receive lots of attention to achieve a safety design of relevant facilities. As a part of the robust design for offshore structures, an explosion risk analysis is normally conducted to examine the potential hazards and the influence of them on structural members in a real explosion situation. Explosion accidents in the oil and gas industries are related to lots of parameters through complex interaction. Hence, lots of research and industrial projects have been carried out to understand physical mechanism of explosion accidents. Computational fluid dynamics-based explosion risk analysis method is frequently used to identify contributing factors and their interactions to understand such accidents. It is an effective method when modelled explosion phenomena including detailed geometrical features. This study presents a detailed review and analysis of Computational Fluid Dynamics-based explosion risk analysis that used in the offshore industries. The underlying issues of this method and current limitation are identified and analysed. This study also reviewed potential preventative measures to eliminate such limitation. Additionally, this study proposes the prospective research topic regarding computational fluid dynamics-based explosion risk analysis.

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“…Compared with conventional ANN models, it can not only automatically optimize regularization parameters but also automatically adjust the weights of input variables by the priori possibility analysis. Many successful examples for applications of the BRBPNN model in other research fields could be found in the literature (Shi et al, 2018(Shi et al, , 2019a(Shi et al, , 2019bSun et al, 2017;Yan et al, 2017;Zhao et al, 2011). These studies suggested that using the BRBPNN model instead of FEA or physical experiments effectively overcomes the timeconsuming problem on the basis of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Effects of random winds and waves on a long-span cross-sea bridge using Bayesian regularized back propagation neural network

Fang

Tang

et al. 2019

Advances in Structural Engineering

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Uncertainties from random multiple factors bring great challenges for assessing response of long-span bridges. This study proposes a dynamic analysis framework by integrating the wind–wave–bridge system with Bayesian regularized back propagation neural network, and then investigates stochastic response of a cross-sea cable-stayed bridge under extreme wind and wave parameters. The wind–wave–bridge system involving wind–bridge and wave–bridge interactions is constructed to calculate dynamic response of the bridge by Newmark-β method, considering stationary fluctuating wind fields and multidirectional random wave fields. To reduce the calculation cost, a Bayesian regularized back propagation neural network model is introduced, and the model evaluation is carried out to illustrate its accuracy and efficiency. After performing small-scale finite element analyses, the response statistics are obtained and later used as the known samples for training the neural network. The power spectrum analyses of the deterministic results are performed to investigate the contribution mechanism of the wind and wave. Finally, the correlation between the bridge response and the single wind–wave parameter is given by uncertainty analysis of the Bayesian regularized back propagation neural network model. The results show that the proposed framework is capable of capturing the nonlinear bridge response resulting from nonlinear wind and wave loads, which, however, is significant different from that under wind alone. The bridge response receives significant contribution from wind and waves relative to the vibration characteristic of the bridge at smaller wind and wave loads. The contribution from vibration characteristic of the bridge becomes significant at larger wind and wave loads. The uncertainty analyses illustrate the significant effects of four wind and wave parameters on girder, tower, and submerged structure.

show abstract

Stochastic analysis of explosion risk for ultra-deep-water semi-submersible offshore platforms

Cited by 27 publications

References 15 publications

A bibliometric analysis of process safety research in China: Understanding safety research progress as a basis for making China’s chemical industry more sustainable

A bibliometric analysis of process safety research in China: Understanding safety research progress as a basis for making China’s chemical industry more sustainable

A Critical Review of a Computational Fluid Dynamics (CFD)-Based Explosion Numerical Analysis of Offshore Facilities

Effects of random winds and waves on a long-span cross-sea bridge using Bayesian regularized back propagation neural network

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