Experimental investigation of effects of magnetic field on performance, combustion, and emission characteristics of a spark ignition engine
Bibliographic record
Abstract
Abstract This article presents a comprehensive experimental work for the application of permanent magnets with the intensity of 9,000 G to a four‐stroke four cylinders gasoline engine on fuel lines at a location near to the combustion chamber. By using the permanent magnet, the liquid fuel disintegrates into small diameter, and de‐clustering of the fuel molecules of hydrocarbon has been proved to provide better atomization of the fuel, which makes sure that the fuel strenuously combines with oxygen and results in complete and more efficient burning process inside the combustion chamber. The experimental analysis revealed that magnetic treatment has improved performance and emission characteristics. Analysis over the engine test results with magnetic fuel conditioning showed that the reduction of 4–12% in fuel consumption and reduction in 11, 10, 18, and 10% for CO, CO2, HC, and NOx emissions, respectively, compared to gasoline fuel without magnetic condition. Further, we experimentally investigated the performance of petrol engine parameters such as in‐cylinder temperature and pressure, cylinder, and head cylinder temperature, the temperature of different parts of the piston. As a whole, magnetic fuel treatment has improved combustion and reduced the harmful pollutants of the compression ignition engine.
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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) | 0.002 | 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".