Numerical simulation of the crack propagation in a pipeline subjected to third-party damage
Bibliographic record
Abstract
With over 830,000 km of operating pipeline in Canada alone, their safe and continued functioning underpins much of daily life. A key type of risk associated with pipelines is third-party damage, damage caused by actions not associated with the pipelines normal operation. The question of whether the pressurized structure like pipeline or pressure vessel would undergo “unzipping” due to the third-party impact is crucial for the safety of pipelines or pressure vessels in service needs to be answered. Thus, we endeavour to develop a methodology for assessment of design solutions effectiveness to prevent a pipeline or pressure vessel failure in an abrupt explosion-like fashion due to third-party damage. Model of crack propagation determining whether the “unzipping” rupture will occur is viewed as a key element in the safety-driven design procedure providing significant effect on the safety of operation. The crack propagation modeling is achieved through the use of nonlinear fracture mechanics technique. The method of singular integral equations is used to calculate the critical stress required for the catastrophic failure of pipeline or pressure vessel damaged due to third-party interference. The model was implemented as a FORTRAN program. Testing of the developed numerical tool was performed using experimental data available in the literature, with the results showing promising agreement.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".