Assessment of Parameters Influencing Lateral Buckling of Deep Subsea Pipe-in-Pipe Pipeline System Using Finite Element Modeling
Bibliographic record
Abstract
The operational requirements for subsea pipeline systems have progressed towards higher design temperatures and pressures (HTHP). To address flow assurance requirements, pipe-in-pipe systems have been developed. For pipelines laid on the seabed, or with partial embedment, the potential for lateral buckling; in response to operational loads, external forces and boundary conditions, has become a major factor in engineering design. The effective axial force is a key factor governing the global lateral buckling response that is influenced by parameters such as internal and external pressure, and operating and ambient temperature. Other design parameters that influence lateral buckling include global imperfections or out-of-straightness, pipe/soil interaction characteristics and installation conditions. Global buckling reduces the axial load capacity of the pipeline that may impair operations and exceed serviceability limit states. Results from a numerical parameter study on lateral buckling response of a subsea pipe-in-pipe (PIP) pipeline are presented. The parameters examined include pipe embedment, pipe out-of-straightness (OOS), soil shear strength, soil peak and residual forces and displacements, variation in soil properties distributed along the pipeline route, and external pressure associated with the installation depth. The observed pipe response was a complex relationship with these parameters and kinematic boundary conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".