A novel performance representation method for icebreakers in ice using the CCGS Henry Larsen
Bibliographic record
Abstract
This paper presents a novel approach to representing the performance of icebreakers in both modelscale and full-scale scenarios, as well as their correlation. Traditionally, icebreaker correlation studies have provided limited insights, often focusing on a few performance metrics under selected ice thickness and/or flexural strength in level ice. In some cases, pack ice conditions were used for model-scale testing, but these were rarely compared with full-scale results due to the variability in ice piece size and other environmental factors, such as waves. Generally, icebreakers encounter a wide range of ice conditions, from loose pack ice to thick pressure ridges. However, these diverse conditions have not been comprehensively captured or analyzed in traditional ship performance assessments. To address this gap, a new method employing a non-dimensional performance curve using thrust/torque and overload coefficients was developed and applied to both model-scale and full-scale ship performance data. Additionally, the correlation between model-scale and full-scale performance was systematically evaluated and discussed. The proposed method encompasses all potential scenarios an icebreaker may encounter, enabling the estimation of external forces, such as ice resistance, using the non-dimensional performance curve. This approach effectively transforms the ship into a sensor under various ice conditions. By utilizing performance data such as ship speed and RPM values, the method allows for the derivation of external forces, which may include a combination of ice, wave, current, and wind loads.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".