Numerical Analysis of API 5 L X42 and X52 Vintage Pipes With Cracks in Corrosion Defects Using Extended Finite Element Method
Bibliographic record
Abstract
Abstract Cracks and corrosion in pipelines can occur simultaneously, representing a hybrid defect known as cracks in corrosion (CIC), which is often difficult to model using the available assessment codes or methods. As a result, detailed modeling of CIC has not been studied extensively. In this study, the extended finite element method (XFEM) has been applied to predict the failure pressures of CIC defects in API 5 L Grade X42 and X52 pipes. The pipes were only subjected to internal pressure and the XFEM models were validated using full-scale burst tests available in the literature. Several CIC models with constant total defect depths (55% and 60% of wall thickness) were constructed to investigate the effect of the initial crack depth on the failure pressure. The failure criterion was defined when wall penetration occurred due to crack growth, i.e., the instance the crack reached the innermost element of the pipe wall mesh. It was observed that for shorter cracks, the failure pressure decreased with the increase of the initial crack depth. The results indicated that the CIC defect could be treated as crack-only defects when the initial crack depth exceeded 50% of the total defect depth. However, for longer cracks, the initial crack depth was found to have a negligible effect on the failure pressure, implying that the CIC defect could be treated as either a crack or corrosion utilizing the available assessment methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".