Improved Fatigue Reliability Analysis of Deepwater Risers Based on RSM and DBN

Xu, Liangbin; Hu, Pengji; Li, Yanwei; Qiu, Na; Chen, Guo-Ming; Liu, Xiuquan

doi:10.3390/jmse11040688

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…analyses. An important line of research considers Artificial Intelligence (AI) methods to build surrogate or meta-models employing neural networks, such as those addressed in [36][37][38].…”

Section: Computational Performancementioning

confidence: 99%

A Coupled, Global/Local Finite Element Methodology to Evaluate the Fatigue Life of Flexible Risers Attached to Floating Platforms for Deepwater Offshore Oil Production

Alves,

Corrêa,

de Sousa

et al. 2024

Mathematics

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This study introduces a Finite Element (FE) hybrid methodology for analyzing deepwater offshore oil and gas floating production systems. In these systems, flexible risers convey the production and are connected to a balcony on one side of the platform. The proposed methodology couples, in a cost-effective manner, the hydrodynamic model of the platform with the FE model that represents the risers and the mooring lines, considering all nonlinear dynamic interactions. The results obtained and the associated computational performance are then compared with those from traditional uncoupled analyses, which may present inaccurate results for deepwater scenarios, and from fully coupled analyses that may demand high computational costs. Moreover, particular attention is dedicated to integrating global and local stress analyses to calculate the fatigue resistance of the flexible riser. The results demonstrate that the coupled global analyses adequately capture the asymmetric behavior due to all risers being connected to one of the sides of the platform, thus resulting in a more accurate distribution of fatigue damage when compared to the uncoupled methodology. Also, fatigue life is significantly affected by adequately considering the coupling effects.

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Section: Computational Performancementioning

confidence: 99%

A Coupled, Global/Local Finite Element Methodology to Evaluate the Fatigue Life of Flexible Risers Attached to Floating Platforms for Deepwater Offshore Oil Production

Alves,

Corrêa,

de Sousa

et al. 2024

Mathematics

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“…BNs are currently one of the most effective theoretical models in the field of uncertain knowledge representation and inference. DBNs have been used for many years in the field of fault diagnosis and the lifetime prediction of structural systems [14]. Arzaghi et al [15] proposed a probabilistic approach based on DBNs to construct an integrated model of the fatigue degradation of subsea pipelines caused by pitting and corrosion, and applied the method to estimate the RUL of high-strength steel pipelines.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method for RUL of Underwater Self-Enhancement Structure: Subsea Christmas Tree High-Pressure Valve Actuator as a Case Study

Liu,

Dai,

Zhao

et al. 2023

JMSE

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Underwater pressure-bearing structures are produced in practice by means of pressure self-enhancement methods in order to improve the stress distribution and enhance the pressure-bearing performance. On the other hand, the pairs equation shows that stress is an important factor influencing the degradation of the structure. In fact, improving the stress distribution will not only improve the pressure-bearing performance, but will have an impact on the life degradation trend. Thus, pressure self-enhancement affects the structural life by changing the stress distribution. With this in mind, this paper considers the effect of pressure self-enhancement on the service time of subsea structures, and a Bayesian network (BN)-based method that can be used to predict the remaining useful life (RUL) of underwater self-enhanced structures is proposed. The method also takes into account the influence of multiple sources of structural factors in order to predict the RUL of the structure more accurately. The life degradation process of an all-electric Christmas tree valve actuator is used as a case study. The prediction results are compared with data in the literature to verify the validity of the method. The results have implications for guidance on the O&M assurance of underwater production systems.

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“…Xu et al [1] propose an improved fatigue reliability analysis method for deepwater risers using data-driven models based on response surface methods to replace physicalbased models, improving computational efficiency. An annual crack growth model based on fracture mechanics considers crack inspection data, while a crack growth dynamic Bayesian network evaluates and updates the fatigue reliability of the riser.…”

mentioning

confidence: 99%