Simulations of the Effect of the Rate of Change of Ice Direction on Stationkeeping of Drillships
Bibliographic record
Abstract
Abstract This paper examines the response of a drillship to the action of an ice cover undergoing a gradual change of direction of motion. For effective stationkeeping, the drillship must continuously change its heading to face the oncoming ice. The simulations consider a vessel that maintains position using Thruster-Assisted Mooring (TAM). A turret mooring system resists the surge and sway movements, while the thrusters act to correct the heading. The simulations of ice deformation and drift solve equations that describe conservation of mass and momentum, and a failure criterion. The drillship is treated as a three-degrees-of-freedom (surge, sway and yaw) rigid body. The results give distributions of ice drift, deformation and stresses around the vessel as well as the forces and offsets of the drillship. The resulting peak ice forces and moments on the drillship show clear dependence on the rate of change of ice drift direction. As may be expected, ice forces and moments increase for high rates. They also increase for higher ice velocities. Analysis of the results shows that the ratio of the length of the drillship to the radius of curvature of ice drift trajectories can be used to estimate ice forces and moments in an environment of changing ice drift direction. The present work additionally included a cursory examination of the effects of ice cover conditions; namely ice thickness and the existence of ridge fragments and icebreaking tracks, which are often formed as part of ice management operations.
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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 | 0.003 |
| 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 | 0.001 | 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".