Fuel economy assessment tool for auxiliary kite propulsion of merchant ship
Bibliographic record
Abstract
A tool dedicated to assess fuel economy induced by kite propulsion has been developed. To produce reliable results, computations must be performed on a period over several years, for several routes and for several ships. In order to accurately represent the impact of meteorological trends variations on the exploitability of the kite towing concept, a time domain approach of the problem has been used. This tool is based on the weather database provided by the ECMWF. Two sailing strategies can be selected for assessing the performance of the kite system. For a given kite area, the simulation can be run either at constant speed or at constant engine power. A validation has been made, showing that predicted consumption is close from in-situ measurement. It shows an underestimation of 11.9% of the mean fuel consumption mainly due to auxiliary consumption and added resistance in waves that were not taken into account. To conclude, a case study is performed on a 2200 TEU container ship equipped with an 800m² kite on a transatlantic route between Halifax and Le Havre. Round trip simulations, performed over 5 years of navigation, show that the total economy predicted is of around 12% at a speed of 16 knots and around 6.5% at a speed of 19 knots.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".