Development of CTOA Requirements for Ductile Fracture Arrest in Gas Pipelines: FE Model and Simulations
Bibliographic record
Abstract
Abstract In this paper, an engineering analysis approach is presented to predict fracture arrest CTOA requirements for gas pipelines using finite element (FE) analysis. A model with constant CTOA as fracture criterion was developed using the commercial FE code ABAQUS 2017x-explicit. Dynamic pipe fracture simulations were performed to determine fracture resistance curves (crack velocity as a function of pressure at constant CTOA) for an American Petroleum Institute (API) X80 steel pipe of diameter 914 mm to 1422 mm and thickness 13 mm to 26 mm. The model incorporated a user subroutine and shell elements. Backfill effects were modelled using smooth particle hydrodynamics and the flap pressure profile was modelled using experimental data from previous burst tests. Fracture resistance curves were compared with the gas decompression curve to determine the minimum arrest CTOA (point of tangency) for a given pipeline design (material, geometry, and gas). Results (arrest CTOA) of the given steel grade and design parameters (thickness, diameter, and backfill) are presented and compared with two previous arrest models that use the CTOA as fracture toughness parameter.
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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 | 0.001 | 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".