Simultaneous and Global Optimizations of LNG-Fueled Hybrid Electric Ship for Substantial Fuel Cost, CO<sub>2</sub>, and Methane Emission Reduction
Bibliographic record
Abstract
Natural gas (NG) is a promising low-carbon fuel to replace diesel for heavy-duty marine propulsion with prominent fuel cost and carbon dioxide equivalent (CDE) emissions reduction potential. However, using NG in compression ignition (CI) engines presents several inherent drawbacks. This research addresses the central issue of an NG-diesel CI engine with increased methane emissions, resulting in minor CDE emission reduction. The hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) emissions are minimized in addition to fuel consumption and carbon dioxide emission reduction as in the current practice. The simultaneous optimizations of powertrain component sizes and the NG-engine hybrid electric propulsion control are introduced to minimize NG fuel consumption and CDE emissions globally. The top-level powertrain component sizing and bottom-level optimal power control and energy management are conducted simultaneously to achieve global optimization with balanced fuel cost and CDE emissions reductions. The new method reduced the fuel cost by about 75 percent and well-to-wake CDE emissions by about 40 percent over the traditional diesel-mechanical propulsion for the test ferry running under an actual operation profile. The research opened a new path to the global design and control optimization of the NG-diesel CI engine-powered hybrid electric marine propulsion system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".