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Research Papers: Structures and Safety Reliability

Extreme Response Prediction of Steel Risers Using a Four Parameter Distribution

[+] Author and Article Information
Miguel Alfonso Calderon Ibarra

Laboratory of Analysis and Reliability
of Offshore Structures—LACEO,
Civil Engineering Program—COPPE,
Federal University of Rio de Janeiro,
Centro de Tecnologia, Ilha do Fundão,
Rio de Janeiro 21945-970, Brazil
e-mail: miguel@laceo.coppe.ufrj.br

Fernando Jorge Mendes de Sousa

Laboratory of Analysis and Reliability
of Offshore Structures—LACEO,
Civil Engineering Program—COPPE,
Federal University of Rio de Janeiro,
Centro de Tecnologia, Ilha do Fundão,
Rio de Janeiro 21945-970, Brazil
e-mail: fjmsousa@laceo.coppe.ufrj.br

Luís Volnei Sudati Sagrilo

Laboratory of Analysis and Reliability
of Offshore Structures—LACEO,
Civil Engineering Program—COPPE,
Federal University of Rio de Janeiro,
Centro de Tecnologia, Ilha do Fundão,
Rio de Janeiro 21945-970, Brazil
e-mail: sagrilo@coc.ufrj.br

Ying Min Low

Centre for Offshore Research and Engineering,
Department of Civil and
Environmental Engineering,
National University of Singapore,
Engineering Drive 2, E1A 07-03,
Singapore 117576
e-mail: ceelowym@nus.edu.sg

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received September 30, 2017; final manuscript received October 19, 2018; published online January 17, 2019. Assoc. Editor: Carlos Guedes Soares.

J. Offshore Mech. Arct. Eng 141(4), 041601 (Jan 17, 2019) (9 pages) Paper No: OMAE-17-1178; doi: 10.1115/1.4041893 History: Received September 30, 2017; Revised October 19, 2018

Short-term extreme response estimates are required in many areas of ocean and offshore engineering, such as steel risers design. As in many cases, the response in non-Gaussian, a theoretical solution, is usually not readily available for this purpose. Hermite transformation and Weibull-based models, among others, are some alternatives that have been used in connection with sampled response time series. In this work, a new approach is investigated. Recently, a four-parameter distribution known as the shifted generalized lognormal distribution (SGLD) has been presented in the literature. One of its main advantages is that it covers regions of skewness–kurtosis not covered by other distributions of common use in engineering. In this paper, the performance of this distribution is evaluated in the extreme values' estimation of the utilization ratios of steel riser sections. Three alternatives for using SGLD are investigated in two case studies of different dynamic behavior. The first one is a steel-lazy wave riser (SLWR) connected to a turret-moored FPSO (floating, production, storage and offloading unit) in 914 m water depth, and the second is a SLWR connected to a spread-mooring FPSO in a water depth of 1400 m. The results obtained by the SGLD-based analysis, which considered several simulation lengths, are compared to those obtained by means of an extreme value distribution fitted to episodical extremes obtained from many distinct realizations. The results of a traditional Weibull-fitting approach to the response peaks and those obtained with a Hermite transformation-based model are also presented for comparison.

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Figures

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Fig. 1

Typical time series of the riser cross section utilization ratio

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Fig. 6

Bias estimates for the utilization ratio of the cross section at top connection for the SLWR connected to an FPSO in 1400 m water depth

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Fig. 7

Bias estimates for the utilization ratio of the critical cross section at floaters zone for the SLWR connected to an FPSO in 1400 m water depth

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Fig. 8

CoVs of the 3 h most probable values estimates for the utilization ratio of the cross section at top connection for the SLWR connected to an FPSO in 1400 m water depth

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Fig. 9

CoVs of the 3 h most probable values estimates for the utilization ratio of the critical cross section at floaters zone for the SLWR connected to an FPSO in 1400 m water depth

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Fig. 10

Validity region delimited by the coefficients of skewness γs and kurtosis κs covered (above the traces) by the Hermite and SGLD models

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Fig. 2

8” SLWR connected to an FPSO in 914 m water depth

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Fig. 3

Type I distribution fitting to the episodical extremes observed for the utilization ratio of the cross section point investigated for the SLWR connected to an FPSO in 914 m water depth

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Fig. 4

Bias estimates for the utilization ratio of the cross section point investigated for the SLWR connected to an FPSO in 914 m water depth

Grahic Jump Location
Fig. 5

CoVs of the 3 h most probable values estimates for the utilization ratio of the cross section point investigated for the SLWR connected to an FPSO in 914 m water depth

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