Physical and Numerical Modeling of a Sub-Surface Caisson for Protection of Subsea Facilities against Scouring Icebergs
Bibliographic record
Abstract
Abstract A simple, rectangular sub-surface caisson was evaluated using physical andnumerical modeling to develop a method for protecting subsea installationsagainst scouring keels of icebergs on the Grand Banks. This protection strategywas developed to assess the feasibility of the structure as an alternative tocostly excavations used to protect subsea equipment on the Grand Backs. Thescenario considered was an ice keel (i.e., iceberg) scouring over a buriedcaisson, avoiding direct contact but with minimal clearance. A centrifugetesting program, consisting of five tests, formed the basis for calibration ofa finite element model. The first four centrifuge tests were carried out withwater as the pore fluid in order to simulate drained conditions during thekeel-soil-structure interaction. In the field, fully drained conditions may notexist; hence the fifth centrifuge test was conducted using a viscous pore fluidto simulate the partially drained conditions. The numerical analyses wereconducted using the commercially available finite element software packageABAQUS. This study demonstrated that the Coupled Eulerian Langrangian (CEL)technique is capable of modeling slow keel-soil-structure interaction events insand. The numerical model performed satisfactorily in simulating the centrifugetests. The results indicated that such a system can potentially provide theprotection needed for subsea installations in ice infested waters. Althoughthis work was based on the conditions encountered on the Grand Banks, theconcept would be applicable to other regions. Future work should involveevaluating various ice keel-soil-structure interaction scenarios and attackangles, development of Inspection, Repair and Maintenance (IRM) strategies aswell as a detailed assessment of construction and installation issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".