Vessel Performance Analysis and Fuel Management
Bibliographic record
Abstract
A prototype Vessel Performance Monitoring and Analysis System (VPMAS) was deployed on a ferry to acquire needed performance data, to help improve vessel performance and reduce fuel consumption. A paper published in 2014 described preliminary data trends observed, key performance indicators computed, data products explored and exploratory tools developed for crews to gain insight into their vessel operation. The current paper describes further analysis of the operational data for speed optimization in calm sea states and the preliminary development of trim optimization software. It was found that trip durations around 7 hours (13.3 knots) use the least amount of fuel. The least amount of fuel is used when the excess distance travelled is minimized and the voyage time is optimized. There is a lot of leeway in terms of voyage time and excess distance travel by the ship before there is a heavy penalty on fuel consumption. Considering only a mean draft of 6 m and an average speed of 14 knots in the current paper, the optimal trim condition for the ferry is around −0.6 m (bow down), which reduces the resistance by 15% compared to the even keel condition. Positive trim causes the considerable increase of the total resistance consistently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".