JOURNAL ARTICLE

A Method on Estimates of Stress Intensity Factors of Outer Circle and Inner Ellipse Submarine Pipeline Under Bending Moment.

  • Published In: International Journal of Computational Methods, 2023, v. 20, n. 9. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Li, Long; Deng, Yousheng 3 of 3

Abstract

The slender circle submarine pipeline possesses both shell and beam characteristics, which are widely used in practical engineering. Unfortunately, for some reason, there will be some geometric defects in the cross-section of the pipeline (such as machining errors and seawater corrosion, etc.), resulting in the stiffness of the circular submarine pipeline being different. The cracked variable stiffness pipeline is actually a typical three-dimensional complex crack problem. In this paper, taking the outer circle and inner ellipse submarine pipeline as an example, a method based on elementary mechanics and the concept of the conservation law is proposed to determine the stress intensity factors (SIFs) of variable stiffness pipeline, and a series of closed-form expressions of SIF are derived for the cracked pipeline. The results from the present method are shown to agree well with available solutions and FEM. In particular, the present method can also be easily applied to other similar variable stiffness submarine pipelines. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Computational Methods. 2023/11, Vol. 20, Issue 9, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2023
  • ISSN:02198762
  • DOI:10.1142/S0219876223500093
  • Accession Number:174116073
  • Copyright Statement:Copyright of International Journal of Computational Methods is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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