Comparative Study on the Effects of Ethanol Proportion on the Particle Numbers Emissions in a Combined Injection Engine
Bibliographic record
Abstract
Ethanol has significant potential for the reduction of fuel consumption and the emissions of engines. In this paper, a dual-fuel combined engine test rig with ethanol injected in the intake port and gasoline injected directly into the cylinder are developed and the effects of ethanol/gasoline ratio (Re) on the combustion and emission of particle numbers are investigated experimentally. The results indicate that the peak in-cylinder temperature (Tmax) decreases continuously with the increase of the ethanol/gasoline ratio (Re). For particle emissions, ethanol can significantly reduce the accumulation mode particle number (APN) at low engine speed; and the lowest number of particulates are at G25 (the gasoline ratio is 25% of the fuel) at low load. And at high engine load, the total particle number (TPN) is insensitive to speed with large ethanol fraction and TPN is relatively small. With the decrease of Re (Re < 50%), TPN rises sharply. When the direct injection timing advances, TPN reduces continuously and the effects caused by speed can be neglected. On the contrary, the speed has significant effects on particle emissions at various ignition times. At low speed, increasing ignition advance can cause the increase of the TPN; which is contrary to the effects of particle emissions at medium engine speed. And the effect of ignition timing at high speed on particle number is not obvious. The ignition timing for which the lowest TPN is reached will increase with the direct injection timing advances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".