The Optimal Design and Control of a Low-emission Methanol-Fueled Hybrid Electric Boat
Bibliographic record
Abstract
Combining clean alternative fuel, hybrid electric propulsion, and advanced real-time optimal control provides a very promising approach to clean energy transition and decarbonization. However, a hybrid electric ship's flexible electrified propulsion system is a complex mechatronic system that demands system optimization and real-time optimal control to reach its full potential. Accurate prediction of needed propulsion power is required to develop the integrated propulsion system design and control solutions. This study introduces an advanced propulsion power prediction method and an integrated model-based design and optimization approach for the design and control optimizations of the vessel propulsion system. After introducing the engine performance models of a methanol-fueled engine based on literature data, a case study of a representative tour boat is conducted to design and assess the balanced performance and emissions of the methanol-fueled hybrid electric marine vessel. Combining clean methanol fuel, hybrid electric propulsion, and optimal real-time control effectively reduces the overall carbon dioxide equivalent emissions from the vessel.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".