A novel double-rectangle simplification method for enhanced burst pressure prediction of natural gas pipelines with irregular corrosion defects
Bibliographic record
Abstract
Accurate burst pressure prediction for natural gas pipelines with irregular corrosion defects remains challenging due to their geometric complexity. This study proposes a novel double-rectangle simplification method that effectively characterizes irregular defects by decomposing them into large and small rectangular sections. Finite element analysis (FEA) validates the method's reliability, showing maximum relative errors below 4 % compared to experimental data. Furthermore, an equivalent rectangle method is developed to transform standard double-rectangle defects into single rectangular equivalents. By integrating this approach with the DNV RP-F101 formula, a modified prediction equation is derived. This equation demonstrates high accuracy (relative error <5 %) for pipelines with double-rectangle defects exhibiting corrosion ratios below 0.7. Parametric studies reveal that defect depth dominates burst pressure, while width and relative positioning have negligible effects. A generalized prediction formula is formulated, incorporating absolute corrosion depth and the depth ratio of the larger rectangle. This refined model provides a robust and practical tool for integrity assessment of pipelines with complex corrosion geometries.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".