Direct Design of Large Ice Class Ships With Emphasis on the Midbody Ice Belt
Bibliographic record
Abstract
The future development of oil and gas reserves in remote Polar Regions areas will require a new generation of highly ice-capable vessels. Many may need to be capable of operating at all times of the year. These ships will need to be able to travel faster in heavy ice than all but the largest icebreakers, which poses challenges for both hull and machinery design. The American Bureau of Shipping (ABS), BMT Fleet Technology Limited (BMT) and Hyundai Heavy Industries (HHI) are currently undertaking a joint project aimed at addressing these design challenges. Because of the unique and innovative aspects of large fast ships for Polar ice development, new methodologies for direct calculation of loads on all areas of the hull are needed. The project is also addressing the need for new techniques for the analysis of the outer hull, double hull and gas containment systems of these ships under design and accidental loads; areas in which ‘rule design’ can only provide a starting point. This paper focuses on the midbody ice loads that may results from both ice pressures and from glancing collisions in the midbody area. The paper highlights some of the challenges of direct design.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".