Determination of the Collapse and Propagation Pressure of Ultra-Deepwater Pipelines
Bibliographic record
Abstract
In the design of ultra-deepwater steel pipelines, it is important to be able to determine the pipe behaviour while subjected to external pressure and bending. In many cases, the ultra-deepwater lay process, where these high loads exist, governs the structural design of the pipeline. Much work has been performed in this area, and it is generally recognized that there is a lack of test data on full-scale samples of line pipe from which analyses can be accurately benchmarked. This paper presents the results of a nil-scale test program and finite element analyses performed on seamless steel line pipe samples intended for ultra-deepwater applications. The work involved obtaining full-scale test data and further enhancing existing finite element analysis models to accurately predict the collapse and post-collapse response of ultra-deepwater pipelines. The work and results represent a continuing effort aimed at understanding the behaviour of pipes subjected to external pressure and bending, accounting for the numerous variables influencing pipeline collapse, and predicting collapse and post-collapse behaviour with increasing confidence. The test program was performed at C-FER Technologies (C-FER), Canada, with the analyses undertaken by the Center for Industrial Research (CINI), Argentina. The results of this work have demonstrated very good agreement between the finite element predictions and the laboratory observations. This allows increased confidence in using the finite element models to predict collapse and post-collapse behaviour of pipelines subject to external pressure and bending.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".