Effect of ethanol addition in gasoline on soot formation characteristics in diffusion flames
Bibliographic record
Abstract
A novel liquid fuel combustion apparatus that can produce stable laminar diffusion flames was designed and set up. Gasoline-ethanol blends of 0%, 20%, 50% and 85% of ethanol in volume were used as fuel. Soot concentration profiles under atmospheric pressure were measured with an optical diagnostic technique to examine how adding ethanol to gasoline affects soot formation. The test results show that these gasoline-ethanol blends can generate stable diffusion flames with visible heights remaining at(50± 1) mm with increased amount of ethanol in gasoline under the condition of a fixed carbon flow rate of 6.05 g/h, that soot concentrations at different heights are reduced to different degrees, and that the total soot decreases almost linearly. The totals of soot in the flames fuelled by blends of 20%, 50%, and 85% ethanol are reduced by 16.20%, 37.77% and 61.66% respectively against that in the gasoline flame.
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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 | 0.000 |
| 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) | 0.000 | 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".