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Data-driven semianalytical method for predicting equivalent thickness of flexible pipeline carcass.

Authors :
Li, Wenbo
Lu, Hailong
Sævik, Svein
Yan, Jun
Source :
Applied Ocean Research. Feb2024, Vol. 143, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

The carcass layer inside a flexible marine pipeline is a large-angle spiral structure with a complex cross-section. A carcass resists radial loads and prevents flexible pipelines from collapsing under extreme hydrostatic pressures. The accurate evaluation of the critical collapse pressure of a carcass under external pressure is crucial for the safe design of flexible pipelines. Because of the complex cross-section of the carcass, the internal surface of the carcass slides and contacts adjacent layers under external loads, which complicates the analysis. Therefore, the complex cross-section of the carcass is often simplified into a uniform section with a fixed thickness during analysis. In this study, parametric models of the carcass with different cross-sectional parameters were established, and the radial stiffness of the carcass under symmetric radial compression was calculated numerically. Next, the calculated equivalent radial stiffness was introduced into the analytical model to obtain geometric coefficients with different cross-sectional parameters. Finally, symbolic regression was used to derive a mathematical equation relating the equivalent thickness and critical cross-sectional parameters of the carcass. The results showed that the Minimum error in the buckling eigenvalue between the equivalent models and full carcass layer model was 6.2 %, the error in the critical collapse pressure was 14.5 %. Meanwhile, the influence of different parameters on the critical collapse pressure of the carcass was discussed. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01411187
Volume :
143
Database :
Academic Search Index
Journal :
Applied Ocean Research
Publication Type :
Academic Journal
Accession number :
174815923
Full Text :
https://doi.org/10.1016/j.apor.2023.103862