Maneuvering and Simulation of a Ship Entering Into the Vancouver Harbor With an Escort Tug
Bibliographic record
Abstract
Ship maneuverability and its prediction in the early design stage become possible and important during last 40 years as a result of some marine accidents involving large ships. Maneuverability standards were developed and proposed by International Maritime Organization (IMO) which provides the performance criteria. Ship simulation technology in particular simulation of ship maneuvering advanced well in recent years. With the availability of numerical or experimental hydrodynamics coefficients, maneuverability of different ships can now be simulated with the help of computer programs. Relatively good agreement was reported by various researchers between simulated results and those obtained from real ship trials. It seems that simulation can now identify acceptable ship maneuvering performance in calm seas. However the effects of the wind and the currents are not that well studied and reported while they are always important factors for ship maneuvering especially in restricted waters. In this study “good” ships are identified by a numerical simulation and then their course keeping in restricted area is studied in calm seas and under wind and current conditions. The simulation work is on ESSO OSAKA 278,000DWT tanker, a well tested ship for regular maneuvering test and for entrance in the Vancouver Harbor under wind and current conditions. The effect of escort tugs on such an operation is also quantified. The range of current and wind speeds for “successful” operation is then established. The detailed analysis and comparison with available experimental results are provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".