Crack Shape Development for Leak-Before-Break Analysis in Pipelines
Bibliographic record
Abstract
Surface cracks in pipelines under certain service conditions may grow due to fatigue, which is caused by pressure (cycles). The leak-before-break (LBB) assessment method is employed to avoid any catastrophic failure prior to a detectable leakage. In the LBB analysis, crack critical length is an essential element for determining the pipeline leak or rupture. The common approach regarding the evaluation of LBB is to calculate the critical crack length and through-wall length under iven pressure cycling conditions. If the critical crack length is less than the through-wall length, LBB conditions could occur and be detected if leak detection capability is high. This involves complex calculations in crack fatigue growth and could result in extensive analysis if thepipeline has a large crack population. This paper presents a simplified approach for assessing the leak-before-break of the flawed pipelines. This approach is based on industrial code API 579-1/ASME FFS-1 Fitness-For-Service. Through the investigation of effects for different parameters on crack growth, including crack initial geometry, pipeline materials, loading conditions, pipeline diameter and wall thickness, it was determined that the crack initial aspect ratio is a major factor influencing crack growth and geometry evolution. Based on these parameters, a crack fatigue growth map was developed. By comparing the behaviors of different cases, it was confirmed that the proposed method is a valid approach for the pipeline LBB analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".