A Study of Spark Ignition Engine Fueled with Methanol and Ethanol Fuel Blends with Iso-Octane
Bibliographic record
Abstract
Alternative fuels are derived from resources other than petroleum. The benefit of these fuels is that they emit less air pollutant compare to gasoline and most of them are more economically beneficial compared to oil and they are renewable. In addition, ethanol has higher evaporation heat, octane number and flammability temperature therefore it has positive influence on engine performance and reduces exhaust emissions. In this study, the effects of unleaded iso-octane, unleaded isooctane–ethanol blend (E5) and iso-octane-methanol (M5) blends on engine performance are investigated experimentally in a single cylinder fourstroke spark-ignition engine at a constant 8 Kg load. The engine speed was changed from 1100 to 1800 rpm. The results of the engine test showed that ethanol addition to unleaded iso-octane increases the value of IP, FP and IMEP with E5 fuel. The results also showed that the indicated power, brake power, friction power, indicated mean effective pressure, torque, exhaust temperature, and thermal efficiency increases with the increase in engine speed at a constant load of 8 Kg for E5, M5 and isooctane fuels. Thermal efficiency was maximum for E5 fuel (38.13%) at a speed of 1750 rpm.
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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".