Clean combustion enabling with ethanol on a dual-fuel compression ignition engine
Bibliographic record
Abstract
In this work, ethanol is applied as the main energy source (up to 95%) on a high compression ratio (18.2:1) diesel engine for improvements in engine efficiency and exhaust emissions, especially at high engine loads. The intake port injection is applied for ethanol fuel delivery along with directly injected diesel pilots as the ignition source. In order to investigate the impact of ethanol on diesel engines operating in the dual-fuel mode, systematic engine experiments are carried out to study the combustion process, engine emissions, and fuel efficiency. The test results indicate that at medium engine loads (8–10 bar indicated mean effective pressure), the increasing use of ethanol offers substantially enhanced homogeneity of the cylinder charge and leads to a greater extent of premixed burning; as a result, the smoke emissions reduce drastically compared to those of the diesel baseline tests. However, the increasing use of ethanol generally results in higher incomplete combustion products. The near-top dead center injected diesel pilots are effective to control the ignition timing and combustion phasing, which provides desirable combustion controllability. At high engine loads, the clean combustion is enabled through the optimization of the engine intake pressure, exhaust gas recirculation, and the fuel ratio to achieve NOx emissions < 0.2 g/kW h and smoke emissions < 0.01 g/kW h. The load capability of the engine operating on ethanol as the primary energy source is demonstrated up to the engine full load (19.5 bar indicated mean effective pressure) with low NOx (0.2–0.7 g/kW h) and smoke ( <1 FSN) emissions.
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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 | 0.000 | 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) | 0.001 | 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".