Hull Forms for Icebreaking Tankers
Bibliographic record
Abstract
The optimum design for an icebreaking tanker will depend on the trade route and the cargo delivery requirements. For example, the hull shape of a ship that spends almost all of its time operating in heavy ice can be optimized for low speed icebreaking conditions. In contrast, a ship that spends a small portion of its time in light ice that has been previously broken and the rest of its time in open water can be optimized for different requirements. The challenge for the designer is complicated by the observation that many ship design features that enhance powering performance in ice are detrimental to open water performance. This paper presents predictions of ship resistance in pack ice, level ice and open water for four tanker designs, which include a conventional hull with no modification for ice at all and three designs proposed for operation in Arctic ice conditions. The predictions of ship performance are based on model experiments carried out in Canada and Korea. The resistance of the four hulls in open water, two concentrations of pack ice and two level ice thicknesses are compared and discussed. Information of this sort is essential for developing the optimum ship design for a particular shipping route, given known profiles of open water, pack ice and level ice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".