How to Optimize the Design of Mechanical Crack Arrestors
Bibliographic record
Abstract
Ductile fractures in natural gas and other high-energy pipelines could be arrested by either toughness in the pipe body or by a crack arrestor device. Although numerous crack arrestor devices have been proposed and patented, the mechanical crack arrestor that surrounds the outside of the pipe (external sleeve type) is the most common type of arrestor. This paper presents an empirically based criteria developed for the optimization of the design of mechanical crack arrestors. The initial development was based on a significant number of steel sleeve crack arrestors with different radial spacings (with and without grouting) and axial lengths that had the same thickness and strength as the main-line pipe. That work was extended to circular cross-section (toroidal) arrestors with different mechanical connectors to eliminate the need for welding. These crack arrestor tests were on 152 and 304 mm (6 and 12-inch) diameter pipes pressurized with nitrogen, rich gas, and liquid carbon dioxide that produce radically different crack-driving forces. It will be shown that the arrestor size is related to the velocity of the ductile fracture as it enters the arrestor, i.e., the fracture velocity is a measure of the instability that needs to be overcome for arrest. A limited number of results from full-scale tests are also presented to validate the design guidelines from this project. Finally, it will be shown how the results could be expanded for composite arrestors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".