A Large-Scale Optimization Framework for Simultaneous Design and Routing of Wind-Assisted Ships
Bibliographic record
Abstract
Abstract This paper presents a comprehensive approach to optimizing the performance of wind-assisted ships. The proposed optimization framework simultaneously addresses ship routing and design, considering the complex interactions between design parameters and route optimization across multiple weather scenarios. Optimal control methods, including the direct multiple shooting technique and automatic differentiation, are employed to efficiently optimize both routing and design parameters, resulting in a large-scale optimization problem. The methodology is demonstrated through a case study of a 2200-TEU container ship equipped with Flettner rotors, operating on a transatlantic route between Brittany and Halifax. The results provide a quantitative, model-based estimate of fuel savings, highlighting the importance of integrated optimization for wind-assisted propulsion systems, while maintaining computational efficiency suitable for iterative design processes. Compared to traditional design and routing methods, based on static polar diagrams and sequential design loops, this approach drastically reduces optimization times, enabling rapid design iterations and informed decision-making. Furthermore, the study offers insights into the sensitivity of fuel consumption to key design parameters, such as the number and placement of Flettner rotors, providing a more accurate and comprehensive estimate of potential energy savings compared to conventional methods. By overcoming the limitations of traditional polar-diagram-based methods, this framework provides a more accurate and dynamic assessment of wind-assisted propulsion systems. These findings emphasize the necessity of integrated optimization for maximizing performance and sustainability in maritime transport. Keywords Wind-assisted propulsion; Design optimization; Ship routing; Large-scale 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".