Probable Ice Impact Locations and Magnitudes on a Naval Hull Form in Forward Transit Through Marginal Ice Zones
Bibliographic record
Abstract
Abstract Operational capability assessment of ships in the Arctic have traditionally focused on icebreaking hull forms. The classification of these capabilities resulted in the development of the International Association of Classification Societies (IACS) Unified Requirements for Polar Ships, which requires the hull structure to withstand a predetermined glancing bow impact. The assumptions made when developing the Polar Class Rules, such as the collision with a thick and semi-infinite multiyear ice floe, slow speeds, and a glancing bow impact on an icebreaking bow shape, are not always valid when assessing the operational capabilities of non-icebreaking hull forms. The ex-HMCS IROQUOIS was simulated in forward transit through marginal ice zones using the computer program “GEM” to study the location and magnitude of probable ice loads. The collision model was validated using the DDePS case 2a collision scenario (glancing collision on the bow). The model was simulated transiting forward at different speeds through ice regimes with varying identified parameters of interest, namely concentration, ice floe size distribution, and ice floe thickness. The observed results show most of the high magnitude ice impacts for this naval hull form occurred at or near the stem, and only a few high magnitude impacts on the bow shoulder. This contrasts to an earlier study using GEM where an icebreaking hull form in forward transit through a marginal ice zone experienced a plurality of high magnitude impacts along the bow shoulder. The main reason for the difference is the shape of the bow, with the naval hull form being markedly slender with much lower normal frame angles. These results suggest that both stem and bow shoulder impacts are of importance when assessing the operational capability of naval hull forms in marginal ice zones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".