Finite Element Investigation on the Tensile Armor Wire Response of Flexible Pipe for Axisymmetric Loading Conditions Using an Implicit Solver
Bibliographic record
Abstract
Composite flexible pipe is used in the offshore oil and gas industry for the transport of hydrocarbons, jumpers connecting subsea infrastructure, and risers with surface platforms and facilities. Although the material fabrication costs are high, there are technical advantages with respect to installation and performance envelope (e.g., fatigue). Flexible pipe has a complex, composite section with each layer addressing a specific function (e.g., pressure containment, and axial load). Continuum finite element modeling (FEM) procedures are developed to examine the mechanical response of an unbonded flexible pipe subject to axisymmetric loading conditions. A parameter study examined the effects of: (1) pure torsion, (2) interlayer friction factor, (3) axial tension, and (4) external and internal pressure on the pipe mechanical response. The results demonstrated a coupled global-local mechanism with a bifurcation path for positive angles of twist relative to the tensile armor wire pitch angle. These results indicated that idealized analytical- and structural-based numerical models may be incomplete or may provide an accurate prediction of the pipe mechanical response. The importance of using an implicit solver to predict the bifurcation response and simulate contact mechanics between layers was highlighted.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".