Influence of Ethanol Content in Gasoline on Speciated Emissions from a Direct Injection Stratified Charge SI Engine
Bibliographic record
Abstract
The influence of ethanol content in gasoline on speciated emissions from a direct injection stratified charge (DISC) SI engine is assessed. The engine tested is a commercial DISC one that has a wall guided combustion system. The emissions were analyzed using both Fourier transform infrared (FTIR) spectroscopy and conventional emission measurement equipment. Seven fuels were compared in the study. The first range of fuels was of alkylate type, designed to have 0, 5, 10 and 15 % ethanol in gasoline without changing the evaporation curve. European emissions certification fuel was tested, with and without 5 % ethanol, and finally a specially blended high volatility gasoline was also tested. The measurements were conducted at part-load, where the combustion is in stratified mode. The engine used a series engine control unit (ECU) that regulated the fuel injection, ignition and exhaust gas recirculation (EGR). A low concentration of ethanol in a commercial gasoline does not alter significantly the operational characteristics of the DISC engine tested. The engine-out hydrocarbon (HC) emissions are practically unchanged, while the carbon monoxide (CO) emissions decrease when 5 % ethanol is added. The tailpipe HC, CO and nitrogen oxides (NOx) emissions are virtually unchanged for the fuel with 5 % ethanol. However, for the alkylate fuels there was a tendency towards increased engine-out and tailpipe HC emissions with increased ethanol content. Aldehyde emissions, which could be measured only for the alkylate fuels, increased slightly when ethanol was added. Stratified charge aldehyde emissions were 2 - 3 times higher than for homogeneous charge operation.
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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".