Experimental investigation on multi-cylinder SI engine fueled conventional gasoline, ethanol blends, and micro-emulsion as an alternative fuel
Bibliographic record
Abstract
In this contribution the two alternative fuels, firstly blended fuel of ethanol and gasoline (i.e. E15%+Gasoline 85%), and secondly, micro-emulsion (i.e. Gasoline 90%, E8% and H2O 2%) were compared with conventional 100% Gasoline fuel for checking the performance, combustion, and emission characteristics of 3-Cylinder SI engine experimentally. The blended fuel containing 15% ethanol with 85% gasoline and the micro-emulsion based fuel which contains H2O molecule in addition to simple blending were prepared in the lab, and both the fuels were tested experimentally on a 3-cylinder SI engine based test rig in order to find out which alternative fuel is more efficient in terms of performance and emission characteristics. The results depicted that although both the micro-emulsion based fuel and the blended fuel showed reduction in power as compared to conventional 100% Gasoline fuel, but the emissions were least for the micro-emulsion based fuel followed by the blended fuel. The CO, HC, and NOx emission decreased for both ethanol blended gasoline fuel and the micro-emulsion based fuel as compared to the 100% Gasoline. The injected water in the micro-emulsion fuel reduces the temperature inside the combustion chamber which is responsible for this decrease in NOx emission. For combustion analysis, pressure v/s the crank-angle showed slight decrease for the blended fuel (i.e. 15% ethanol with 85% Gasoline) and the micro-emulsion based fuel (i.e. Gasoline 90%, E8% and H2O 2%) as compared to the conventional 100% gasoline fuel. This can again be attributed to the fact that the micro-emulsion based fuel has a lower combustion chamber temperature compared to the conventional 100% Gasoline.
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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".