Buckle Propagation in Pipelines Under Non-Uniform Pressure
Bibliographic record
Abstract
Preventing the occurrence of local buckling due to external pressure is one of the main concerns in design of offshore pipelines. However, when a pipeline is designed for deepwater, prevention of the propagation of local buckling along the pipeline has more importance. Therefore, the study of buckle propagation phenomenon and its prevention is a subject of many researches in the past 25 years. Great amount of these researches have focused on appropriate estimation of buckle propagation pressure. In this paper, details of 3-D finite element modeling for buckle propagation simulation are outlined. In order to verify the accuracy and validity of the finite element modeling, the numerical results, obtained from nonlinear finite element analysis have been compared with the results of the experimental study on full-scale models, undertaken by C-FER Technologies, Canada, which have been published by Toscano et al. (2002). Comparison shows that the finite element results have very close agreement with the experimental behaviour. Also, in the present paper, numerical results of Toscano et al. are discussed; and the study shows that the proposed method, outlined in this paper, gives more appropriate results than the proposed method by Toscano et al. In addition, the buckle propagation of pipeline under the uniform external pressure as well as non-uniform pressure, due to the presence of the internal flow (when the pipe is not full of fluid) is studied. The change of buckle propagation pressure is determined by the proposed method.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".