Wind assisted ship propulsion: Exploring technologies, wing sail assessment procedure and VLGC case study
Bibliographic record
Abstract
A literature review of Wind Assisted Ship Propulsion methods is presented, along with an assessment procedure for wing sails and its thorough application in a case study. The assessment procedure conducted utilizes XFOIL to obtain a lift coefficient and drag coefficient, MARIN’s Blue Route application is used for obtaining wind data, finally MATLAB is utilized for calculations. The assessment procedure calculations focus on power, energy, fuel tonnage required, fuel tonnage saved, fuel volume required, and finical analysis including money saved, Net Present Value, and Simple Payback Period. These calculations are completed for a variety of fuels including combustion of High Sulphur Fuel Oil, Very Low Sulphur Fuel Oil, Liquified Natural Gas, Liquid Hydrogen, and Liquid Ammonia. In addition, the use of fuel cells is considered for Liquid Hydrogen, and Liquid Ammonia. The assessment procedure is deemed successful, the percent savings from the sail array mirrors that from literature very closely. The fuel savings results show 19% at 10 knots, 8% at 16 knots and 2% at 20 knots for the round trip from Hamburg, Germany to Walvis Bay, Namibia. During the literature review wing sails showed an average fuel oil consumption savings of 10%. More specifically up to 22% savings were observed at low speeds. On a more comparable journey from Cape Lopez, Gabon to Point Tucker Canada fuel oil consumption was reduced to 8.8% which is in line with 8.0% estimated during the case study on the round trip from Hamburg to Walvis Bay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".