Modelling of a DP Drillship Advancing in Managed Ice Fields: Comparison Between Numerical Simulations and Ice Basin Tests
Bibliographic record
Abstract
The present paper reports on comparisons between results from ice basin tests and numerical simulations. The tests were conducted in the model ice basin of the National Research Council Canada in St. John’s, Newfoundland. Those tests examined the performance of a vessel controlled by a dynamic positioning (DP) system in managed ice conditions at a scale of 1:40. A numerical ice dynamics model was used to simulate ice basin test conditions. The results indicate that surge direction thrust and ice force are in good agreement. Ice basin measurements, however, produced higher sway-direction ice forces and yaw-direction moments. It appears that the treatment of sidewall boundaries and the resulting confinement of the ice cover may have contributed to that discrepancy. An additional contribution may be due to differences between the DP algorithms, which were used in the numerical simulations and ice basin tests. Numerical simulations also examined the role of floe shapes. The results indicate that floe shapes obtained from field observations reduce sway-direction ice forces and yaw-direction moments below values obtained when using near-square floe geometries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".