Predicting the performance of a tug and tanker during escort operations using computer simulations and model tests
Bibliographic record
Abstract
Optimizing the performance of an escort tug is an important part of the design process, but the impact of tug performance on total system performance is the critical issue. Whilst tug performance can be predicted with model experiments, this is an impractical method for evaluating the combined tug-towrope-tanker system. Numerical simulation of the total system is a much more practical option, since numerical maneuvering models for ships are well established and can easily include the predicted trajectory of the tanker under the action of a force generated by the tug. The challenging part of the simulation is providing a realistic estimate of the tug performance. IMD has developed a hybrid modeling process that combines captive model experiments for obtaining hydrodynamic coefficients of the tug with numerical simulation of the complete system. This paper describes the development of the method and presents some results to demonstrate trends in system performance with design variables, such as location of the towing point. It also discusses some operating practices and the associated safety considerations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".