Development of a Pipeline Wrinkle Material Ultimate Limit State: Full Scale Modeling
Bibliographic record
Abstract
While the formation of a wrinkle in an onshore pipeline is an undesirable event, in many instances this event does not have immediate pipeline integrity implications. The magnitude or severity of a wrinkle formed due to displacement controlled loading processes (e.g. slope movement, fault displacement, frost heave and thaw settlement) may increase with time, eventually causing serviceability concerns (e.g. fluid flow or inspection restrictions). Pipe wall cracking and eventually a loss of containment involves contributions from the wrinkle formation process, as well as wrinkle deformations caused by in-service line pressure, temperature and seasonal soil displacements. The objective of this paper is to provide an overview of the ongoing research efforts, sponsored by TransCanada PipeLines Ltd, towards the development of a mechanics based wrinkle ultimate limit state that may be used in future to evaluate the long term integrity of wrinkled pipeline segments. The research efforts include testing and non-linear finite element modeling of a full scale wrinkled pipeline segment. This paper outlines the development of the full scale finite element model, including the detailed material model development, used to estimate the fatigue life of the experimental full scale fatigue test specimen. A comparison is then carried out between the experimental results and the results from the finite element analysis.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".