Machine Learning Techniques for Ship Performance Predictions in Open Water and Ice
Bibliographic record
Abstract
The primary purpose of the work is to explore the practicality of using Artificial Intelligence (AI); specifically, Machine Learning (ML) and Deep Learning (DL), to predict ship performance characteristics based on time-averaged and time-dependent data. Three application cases are studied. The first modelling case is a time-averaged ship propulsor performance dataset, the second and third modelling cases are a time-averaged and time series prediction of forces on a dynamic positioning ship operating in a broken ice-field. An ML-based model was developed to predict various propulsor coefficients of a podded propulsor, given the advance coefficient, cavitation condition, hub geometric variations, pod configurations and the azimuthing angle. The second modelling case involved developing an ML algorithm to predict time-averaged ice forces on DP-controlled ships at the given ranges of ice concentration, floe size, ice thickness, strength, density, drift speeds and direction. The third modelling case involved predicting the time-dependent forces on a DP-controlled ship at specific operating conditions and ice-field parameters. The AI-ML-based predictive models showed reasonable accuracy compared to the corresponding measurements and performed better than conventional regression-based models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".