Assessment of Numerical Modeling Tools for the Prediction of Ship Performance in Ice
Bibliographic record
Abstract
Abstract This paper provides a general review of available models used in the NRC-OCRE (National Research Council – Ocean, Coastal and River Engineering) that could be used to support the assessment of a ship’s performance in ice conditions. The models were separated into three main categories: empirical, numerical and real-time, and reviewed in terms of key strengths and weaknesses. A general overview of the modelling categories and specific models within each category is given. Within each modelling category, specific models were compared to outline the key features of both the independent models and the modelling category itself. A representative model within each category and sub-category was selected and used to present the output for a given scenario. This enabled a demonstration of output capabilities for each modelling category. It also provides the reader with additional information pertaining to the input requirements and validation for the selected models. A discussion of the integration of an ice loading model into an existing ship simulation framework is included. A specific case was reviewed in which a successful integration had occurred and was documented. This allowed for demonstration of a process that could be followed for updating one’s numerical modelling capabilities. Based on this review, guidance was provided in terms of selecting a numerical tool to extend current ship performance modelling capabilities to consider ice operations. Each modelling category and sub-category has a unique set of advantages and disadvantages. These should be considered in detail to ensure that the numerical model(s) selected are optimal in terms of their ability to assess desired scenarios and interface with existing software.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".