Effects of Corrosion Pit Idealization Shape on the Susceptibility of Crack Initiation in the Analysis of Pipeline Dents Interacting With Corrosion
Bibliographic record
Abstract
Abstract External corrosion pitting is a common material degradation mechanism for steel pipelines resulting in a reduction of the pipe wall thickness (WT) within a localized, affected area, hereafter referred to as metal loss (ML). In many cases, corrosion pits interact with other major pipeline defects, such as dents; such interacting features can potentially increase the susceptibility to failure of in-service pipelines since both defects are stress and strain concentrators. There has been a lack of clarity and consensus on the generally accepted geometrical shape that should be used to model ML in pipelines for engineering analyses. As a result, ML in pipelines is being idealized with various geometrical shapes such as sharp-edge rectangular shapes, curved-edge rectangular shapes, and semi-ellipsoidal shapes. The effect of these variations in idealization shapes of ML features, especially while interacting with pipeline dents, needs to be investigated to provide clarity and suggest the best approach to simulate ML features in pipelines with a higher margin of safety. In this study, pressurized steel pipe with an outside ML defect was modelled using a curved-edge rectangular shape, a sharp-edged rectangular shape and a semi-ellipsoidal shape in ABAQUS™. A comparison of both hoop and von Mises stresses, and the equivalent plastic strain distributions of the pressurized pipe with different ML shapes was investigated. The ML shapes were used to conduct dent-metal loss interaction analysis to investigate the effect of varying dent depth on the pipeline’s susceptibility to crack initiation. Specifically, this study examined different dent depths interacting with an ML feature that was used to evaluate the susceptibility to crack initiation using dent formation strain limit and the ductile failure damage indicator (DFDI). It was found that different shapes show different stress concentration distributions. The results show significant variations in the susceptibility to cracking in pipelines with varying dent depths interacting with ML. This outcome may have a significant impact on pipeline integrity assessment and management planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".