Deepwater Pipelines: Reliability of Finite Element Models in the Prediction of Collapse and Collapse Propagation Loads
Bibliographic record
Abstract
In the design of pipelines it is of utmost importance to use validated numerical tools, usually finite element models, to reliably determine the structural limit loads. Also for steel pipes manufacturers it is very important, for establishing the set-up of their production processes, to be able to analyze using validated finite element models the effect that different manufacturing imperfections have on the pipe limit loads (e.g. “ovalization” of the external diameter, eccentricity, residual stresses, etc.). For deepwater pipelines the most relevant limit states that need to be analyzed are the collapse and collapse propagation under different combinations of external pressure and bending. In the second section of this paper we discuss the finite element models that we developed to predict the collapse and collapse propagation of seamless steel pipes under external pressure and bending. The validation of these models was performed comparing the numerical results with experimental results obtained at CFER (Edmonton, Canada) [1] and at our lab for the pre-collapse and post-collapse regimes. In deepwater pipelines, in order to prevent the propagation of collapse failures through the pipeline length, buckle arrestors are used. In the third section of this paper we review the finite element models that we developed to predict buckle arrestors cross-over external pressures. The validation of these models was performed comparing the numerical results with experimental ones obtained at our lab for different ratios [arrestor thickness/pipe thickness] corresponding to either the flattening or flipping cross-over mechanisms [2]. Finally in the fourth section of this paper the validated finite element models are used to perform parametric analyses that provide useful data for pipeline engineers on the effect of different geometrical parameters on crossover pressure.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".