A Decision Making Process for the Selection of Better Ship Main Dimensions with the Fuel EEDI Requirements
Bibliographic record
Abstract
Initial ship design necessitates the evaluation of main ship parameters in order to obtain a feasible design solution satisfying the design objectives. Traditionally, relationships between the main dimensions and design parameters of proven designs provided the basis of a successful and safe solution. This approach restrains the designer from improving the design with respect to possible conflicting design criteria. Pareto frontier technique has widely been utilized in the design of ships, mainly at the advanced design stages for multi-objective optimization problems. As the principal dimensions of a ship, are vital on the performance of a vessel, major improvements in performance may be achieved by selecting “better” principal dimensions. This paper proposes to integrate in the Pareto technique studied earlier by the authors, the EEDI perspective at an early stage in the a decision-making process for the selection of better main dimensions with respect to multiple conflicting criteria. The previous work on the subject showed that rather promising results could be obtained for fishing boats, naval ships and planning hulls. In this case additional requirements are included in the selection using the expected fuel consumption into the owner requirements. The study is now limited to displacement type vessels and well known and tested resistance, seakeeping and propeller algorithms. A Pareto Front has been observed for all cases studied and is seen as a technological barrier for the ship performance with respect to main dimensions. We believe that the procedure developed reduces future conflicts in the design along the design spiral and satisfying the EEDI fuel reduction requirements. The improved performance of the design with respect to the conflicting design criteria at the initial stage of design also serves as a better basis for further optimization.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".