Local Design Pressures During Ship Ram Events Modeling the Occurrence and Intensity of High Pressure Zones
Bibliographic record
Abstract
During ship ice interaction events (i.e. rams with multi-year ice), the occurrence of local pressures vary in time and space. A link between local pressures and global forces is the sum of the local forces from n High Pressure Zones (HPZs) across the interaction face equals the total force transmitted into the structure. In this paper, a model for HPZ density, and force during ship ram events is presented. The occurrence and intensity of HPZs on panel areas were simulated using a Poisson process and an exponential distribution for HPZ force. The model is extended to consider HPZ occurrence in time through a ramming event, modeling HPZ rate. Such a model allows the designer to determine baseline ‘parent’ local pressure design parameters based on vessel size and expected operational speed. The faster a ship operates through an ice regime, the greater the HPZ rate. Larger and faster ships will penetrate further, having longer interaction durations and hence a greater number of HPZs forming (unless, for example, the ship passes through a ridge). Rates too will vary along the vessel being greater on the bow and least from mid-body to stern. For design, we are interested in the maximum local pressure on a single panel area through the ram duration. The results are compared with previous local pressure models for 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".