Experimental Study of a Dual-Fuel Generator Set Operating on Diesel Fuel Direct Injected and Hydrous Ethanol Fumigation at Different Loads
Bibliographic record
Abstract
The sizable global use of fossil energy and the worries about harmful emissions to the environment and human health have led investigations focused on using renewable fuels. Ethanol seems to be a desirable renewable fuel due to availability and significant production. The ethanol fumigation in compression ignition engines has been explored as a possible solution to enhance efficiency and decrease harmful and pollutant gases. This study investigates the effects of a generator set running on directly injected diesel fuel containing 7% biodiesel in volume and port fuel injected hydrous ethanol in different loads. The experiments were carried out in a diesel engine with specific loads, and the ethanol substitution rate ranged from 9% to 52% by energy. Results showed a significant decrease up to 61% in smoke opacity. An improvement of about 9.7% at 7.0 kW in global thermal efficiency was observed. Nevertheless, the other loads' efficiency deteriorated, resulting in a maximum decrease of about 14.6% at 4.0 kW. The total specific fuel consumption increased when ethanol was used, whereas the exhaust gas temperature decreased. The lowest NOX emissions found was 290 ppm at 6.0 kW and an ethanol energy ratio of 21%, although an increase in CO emissions was observed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".