Advancing sustainability in the maritime sector: energy design and optimization of large ships through information modelling and dynamic simulation
Bibliographic record
Abstract
This paper introduces a new design methodology for the naval sector to face the challenges of reducing GHG emissions and the related environmental impact. The proposed methodology is based on the application of the novel Building Information Modeling (BIM) to Building Energy Modeling (BEM) technique to design large ships. To do so, the information model of a modern cruise ship is developed with the aim to automatically create a 3D physics-based model and simulate the energy performance of the ships under real and dynamic conditions. Compared to other approaches adopted in literature to model the ships energy performance, the one proposed in this paper is less complex because of the advanced tools of BIM technique in handling a high number of thermal zones and related surfaces. Furthermore, since BIM improves the project management process, the methodology can streamline the design-to-delivery procedure by supporting the design of HVAC and waste heat recovery systems. To show the effectiveness of the proposed approach in improving the sustainability of modern ships, and more in general of the shipping industry, the energy performance of the Allure of the Seas, a 6000-passenger cruise ship operating in the Caribbean Sea, is analysed by means of the developed simulation tool. The accuracy of the developed model is verified by comparing results respect to actual measured data achieving overall electricity consumption differences around 1%. Two different commonly used heat recovery strategies are assessed to show the flexibility of the tool. The simulation results highlight that lower primary energy consumptions can be obtained by optimizing the waste heat recovery energy management system (up to 600 MWh per trip), proving the importance to exploit new advanced modelling tools in the early energy design of ships when a high level of energy efficiency is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".