Design and retrofit towards zero-emission ships: Decarbonization solutions for sustainable shipping
Bibliographic record
Abstract
The maritime transport sector is undergoing significant development to meet the stringent targets set by the International Maritime Organization and national regulations. An informed decision-making process is essential during the design of new ships or the refurbishment of existing ones, especially when selecting new technologies or implementing alternative fuels. This paper presents an approach based on dynamic simulation and multi-objective optimization aimed at identifying the technologies and strategies to be implemented on board. The methodology combines physics-based and data-driven modelling to enhance the performance and consumption assessment of ships under real-world conditions, using tools such as Autodesk Revit for 3D modelling, MatLab and Python for weather data customization, and TRNSYS for simulating the onboard energy system. Specifically, the analysis focuses on optimizing an existing cruise ship and integrating cutting-edge technologies, using measured ship operational data through a calibrated and validated model. Technologies such as single and double absorption chiller, wet steam screw expander and fuel cell are investigated to define a roadmap to their implementation towards energy and emission efficiency. The proposed methodology shows that significant reductions in pollutant emissions can be achieved by their optimization and implementation. Indeed, the use of bio-liquified natural gas to power fuel cells can lead to non-renewable primary energy savings of up to 16.8 %. The study also highlights the importance of future incentive policies for the development of cost-effective green fuels. This research underscores the necessity of innovative solutions to be properly optimized and designed to achieve substantial reductions in greenhouse gas emissions in the shipping sector. • Model based approach for the sustainable design and retrofit of ships • Multi-objective optimization for a proper design of existing or new ships • Identification of the mix of cutting-edge technologies and alternative fuels • Evaluation of target prices of alternative fuels to achieve maritime sustainability • Proposed methodology aids transition to environmentally-friendly shipping practices
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".