Effect of Dent Depth on the Burst Pressure of NPS30 X70 Pipes With Dent-Crack Defect
Bibliographic record
Abstract
Pipeline is the common mode for transporting oil, gas, and various petroleum products. Buried linepipe can be exposed to various external interferences and corrosive environment and as a result, damage in the form of dent or corrosion or crack or gouge or combination of any of these damages can form in the pipe wall. Such damage or combined damages can reduce the pressure capacity of the pipeline. A defect combining dent and crack, often known as dent-crack defect, can develop in the wall of a buried oil and gas linepipe. This combined defect may lead to a leak or a rupture in the pipe wall and hence, the pipeline operator becomes concerned about the performance and safety of the pipeline when a dent-crack defect is detected in the field pipeline. A long-term research program is currently underway at the Centre for Engineering Research in Pipelines, University of Windsor to study the influence of various parameters such as dent depth and operating line pressure on the pressure capacity or burst strength of 30 inch diameter and X70 grade pipes with D/t of about 90. From the study completed so far, it has been found that the dent depth of 8% with crack depth of 4 mm or more can reduce the pressure capacity by 32%. This paper discusses the test specimens, test setup, test procedure, test results, and data obtained from finite element analyses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".