Experimental study on effect of use of oxyhydrogen blends with traditional fuels in spark engines for better performance
Bibliographic record
Abstract
The utilization of oxyhydrogen as a supplementary fuel in internal combustion engines has garnered significant attention due to its potential to enhance engine performance and reduce emissions. This study investigates the effects of oxyhydrogen addition on the performance, emissions, and thermodynamic efficiencies of a spark-ignition engine fueled by gasoline, propane, and methane. The engine drives a commercial stationary power generator. Results demonstrate that oxyhydrogen addition leads to a consistent reduction in CO 2 and HC emissions across all fuel types, with the most substantial reduction observed in gasoline, decreasing from 2,413 g/kg to 1,709 g/kg (a 29% reduction) with 20% oxyhydrogen addition. However, NO x emissions tend to increase, particularly pronounced in gasoline, where they escalate from 9.5 g/kg fuel with no oxyhydrogen to 24.6 g/kg fuel for the same case (a 159% increase). The power output exhibits varying degrees of improvement depending on the fuel type and oxyhydrogen concentration. Oxyhydrogen has compensated for the power reduction when propane and methane are used as the base fuel. The energy and exergy efficiencies consistently improve with increasing oxyhydrogen addition, with the most significant improvement observed in propane, where the energy efficiency increased from 30.0% to 37.3% (a 24.3% improvement) with 20% oxyhydrogen addition. • Gasoline, methane and propane are blended with oxyhydrogen up to 20%vol. • A dual-fuel carburettor is included in the engine of a commercial power generator. • Methane/oxyhydrogen blends show better performance in terms of emissions. • A 20% oxyhydrogen compensates the power reduction with methane.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".