Pseudo-random artificial corrosion morphologies for ultimate strength analysis of corroded steel tubulars

The current paper presents a novel model which generates artificial corrosion morphologies for metallic tubulars. It computes pseudo-random 3D morphologies from the corrosion statistics, such as its mean depth, standard deviation, and correlation length. The non-repeating profiles are statistically comparable with each other and with the observation. The artificial morphologies can be used for numerical analysis and probabilistic assessment of the remaining strength of the corroded steel tubulars. The study results show that the axial load-bearing response of steel corroded tubes predicted by finite element (FE) models incorporating these artificial corrosion morphologies very closely follow those from the corresponding models with as-is corrosion morphologies. The latter morphologies were obtained from 3D high-resolution laser scanning of corroded specimens. The FE results of the axial compressive behaviour of corroded tubular specimens also show excellent agreements with experimental observations. The accuracy of the equivalent uniform thickness reduction approach, commonly used for quantifying the load-bearing capacity of corroded steel tubulars, is also evaluated. Models with a uniform wall-thinning are inclined to substantially overpredict the axial load-bearing performances of the tube relative to the model with the irregular corrosion morphology.

Automated summary

The study presents a novel model for generating artificial corrosion morphologies for metallic tubulars. The model computes pseudo-random 3D morphologies from corrosion statistics and shows statistically comparable results with observation. These artificial morphologies can be used for numerical analysis and probabilistic assessment of the remaining strength of corroded steel tubulars.