Numerical implementation and benchmark of ice-hull interaction model for ship manoeuvring simulations
Bibliographic record
Abstract
The first question about the performance of a ship operating in ice is usually about the speedpower relationship moving ahead in the specified ice conditions. With advanced physical model test technology and the increasing scope and reliability of full-scale data on reference ships, this question may be answered with considerable confidence at the design stage. Attention has turned in recent years to assessment of the performance of ships undertaking turns and more complex maneuvers in ice. Ability to predict turning performance is important in ship navigation, and it is essential as the basis for numerical models in marine simulators used for operator training and operations planning. This paper reports on the development of a new physically based ice-hull interaction (IHI) model, developed at the NRC Institute for Ocean Technology. This model will serve as the key ice component for ship real-time simulators in ice. The model calculates forces generated by increments in an arbitrary prescribed ship motion. The model incorporates multi-failure ice modes and hydrodynamic effects, and tracks the development of the broken channel. The theoretical basis for the model has been described in a previous paper. This paper presents the model's numerical implementation and benchmarking based on ship model tests in ice.
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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) | 0.001 | 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 | 0.001 | 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".