Analyses of SCR with Pull-Tube Using ABAQUS and Flexcom
Bibliographic record
Abstract
Abstract Pipe-in-pipe analyses of in-place SCR (Steel Catenary Riser) within pull-tube (PT) or pull-in analyses of SCR through PT are computationally complicated problems in offshore development in the application of SCR hanging off through PT from a platform such as Spar. In the past, those analyses have been done primarily using ABAQUS, an advanced nonlinear FEA commercial tool. A benchmark study of pipe-in-pipe analysis, i.e. SCR through PT, was conducted to compare the performance of ABAQUS and Flexcom, a package popular in the offshore oil and gas industry dedicated to flexible structures like risers or mooring lines. Both static and dynamic loading conditions, as well as the pull-in process were included in the paper. Bending moment, tension, shear force, and von Mises stress of both SCR and PT were compared. The comparison shows that Flexcom pipe-in-pipe analysis of in-place SCR within PT or pull-in analysis of SCR through PT can obtain satisfactory results in bending moment and stress for both static and dynamic simulations with close agreement with ABAQUS. While the friction between SCR and PT is not accounted for in Flexcom, its effect is mainly on tension especially of PT, and insignificant for bending moment and bending stress, which governs the strength and fatigue performance of both PT and SCR at the upper hang-off region. This study indicates that it could be adequate to use Flexcom to conduct pipe-in-pipe analysis with much better efficiency for analysis of in-place SCR within PT or pull-in analysis of SCR through PT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".