Evaluation of Existing Fracture Mechanics Models for Burst Pressure Predictions, Theoretical and Experimental Aspects
Bibliographic record
Abstract
The predictions of burst pressure and leak/rupture have significant impact on the pipeline integrity assessment results, and subsequently on the extent of the required mitigation and re-inspection interval. There are models available for burst pressure and leak/rupture prediction in the literature, namely API-579, CorLas®, and NG-18 (Modified Ln-Sec). In this paper, evaluation of existing fracture mechanics models for burst pressure and leak/rupture prediction for external crack and crack like features including stress corrosion cracks (SCC) are performed. Both theoretical and practical aspects of each model are discussed in detail. An experiment is set up to conduct fifteen full scale burst tests of 20-inch, 34-inch and 36-inch diameter joints removed from the pipeline field. Among them, seven pipe joints are with base material SCC, four joints with seam weld cracks, three joints with linearly aligned crack-like features surrounded with shallow base material SCC, and one joint with crack-like feature in the Weld area. A high speed camera is utilized to capture rupture events. The rupture events can be stable tearing, crack coalescence and unstable propagation of a critical crack or multiple closely aligned cracks in a crack colony which are responsible for the rupture. Detailed profile measurements of the critical crack/s for burst pressure predictions are done from the fracture surfaces. Relevant material properties are measured for each joint in base material or seam weld location depending on the crack location. Burst pressure predictions are performed with models available in API-579, CorLas®, and Modified NG-18. An elastic-plastic finite element model is generated to quantify the effective stress intensity factor with and without the end cap effects during the burst testing. The accuracy and conservatism of the models evaluated are analyzed. Implications of the findings are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".