SIIBED: Numerical Modeling of Subsea Pipelines and Cables in Ice Prone Region
Bibliographic record
Abstract
Abstract The objective of this paper is to incorporate the outcomes of laboratory and physical testing carried out under the SIIBED program in order to develop, calibrate and validate a design tool for assessment of risk to subsea infrastructure due to ice keel interaction with pipelines, flexible flowlines, and electrical cables. This tool could then also be used to investigate load transfer to other subsea structures and facilities. A numerical modeling procedure is developed using the finite element analysis software Abaqus where the large deformation process of iceberg-pipe-soil interaction can be accommodated using the Coupled Eulerian Lagrangian (CEL) technique. The complexity of the ice-pipe-soil interaction is captured by appropriate and varied contact strategies in different areas of the model. Details of the model are discussed, including advancements of soil behavior and flexible flowline mechanical response, where it is desirable for the design tool to be optimized for computational efficiency while retaining reliable predictions of response. Case studies are presented for thick walled pipeline, flexible flowlines and electrical cables. Typically, the ice is modeled as a rigid body with unlimited strength. Limiting ice interaction forces, via pressure, is shown to have an effect on the displaced shape of the pipeline and flexible. Including radial compliance of the electrical cable is also shown to have an effect. With mesh refinement and retaining sufficient complexity in key areas, the complex model can be analyzed with a reasonable amount of computational cost. Advancements in the modeling of the ice feature strength limits are highlighted as well as application of large deformation modeling of electrical cables, which is atypical.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".