Numerical Simulations of Ice Forces on Moored and Thruster-Assisted Drillships
Bibliographic record
Abstract
Abstract The present work examines the loads on moored/DP-assisted drillships and their responses in managed pack ice. Numerical simulations are used to determine deformations and stresses within the ice cover and the movements of the vessel. The results give ice forces and moments on the vessel, as well as its surge, sway and yaw responses. The drillship assumed in this simulation work has a length of 230 m, a beam of 42 m, and a mass of 100,000 Mt. Simulations examine a case representing a moored vessel in a managed ice field consisting of 50 m floes that are 1 m in thickness, moving at a constant speed of 0.3 m/s. For ice moving along the surge direction, the peak surge force was approximately 2 MN. The corresponding maximum surge was 1.5 m. A parametric study further examines several aspects of ice interaction with the vessel. The results quantify various effects of ice management (the role of floe size), ice thickness and drift speed, the direction of ice movement, and stiffness of the mooring system. A case of combined mooring and thruster-assisted mooring is also examined. The additional contribution of the thrusters (or control of the DP system) is shown to somewhat reduce the offsets of the vessel. The work presents a methodology for evaluating the stationkeeping performance of vessels in managed pack ice and quantifying the role of the mooring/DP system characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".