LNG systems for natural gas propelled ships
Bibliographic record
Abstract
In order to reduce the atmospheric pollution generated by ships, the International Marine Organization has established Emission Controlled Areas. In these areas, nitrogen oxides, sulphur oxides and particulates emission is strongly controlled. From the beginning of 2015, the ECA covers waters 200 nautical miles from the coast of the US and Canada, the US Caribbean Sea area, the Baltic Sea, the North Sea and the English Channel. From the beginning of 2020, strong emission restrictions will also be in force outside the ECA. This requires newly constructed ships to be either equipped with exhaust gas cleaning devices or propelled with emission free fuels. In comparison to low sulphur Marine Diesel and Marine Gas Oil, LNG is a competitive fuel, both from a technical and economical point of view. LNG can be stored in vacuum insulated tanks fulfilling the difficult requirements of marine regulations. LNG must be vaporized and pressurized to the pressure which is compatible with the engine requirements (usually a few bar). The boil-off must be controlled to avoid the occasional gas release to the atmosphere. This paper presents an LNG system designed and commissioned for a Baltic Sea ferry. The specific technical features and exploitation parameters of the system will be presented. The impact of strict marine regulations on the system's thermo-mechanical construction and its performance will be discussed. The review of possible flow-schemes of LNG marine systems will be presented with respect to the system's cost, maintenance, and reliability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".