Analysis of a diesel engine fueled with ternary fuel blends and alumina nano-additives at various combustion chamber geometries
Bibliographic record
Abstract
The present study investigates the impact of various combustion chamber geometries in a direct injection engine fueled with diesel–biodiesel–ethanol blends mixed with alumina nano-additives, named as high-performance fuel (HPF). The HPF was subjected to various combustion bowl geometries including standard hemispherical chamber geometry (SG), shallow depth reentrant bowl geometry (CG1), toroidal reentrant chamber geometry (CG2), and toroidal chamber geometry (CG3). Performance results reveal that in comparison with the SG-HPF arrangement, brake thermal efficiency increased by 11.51% and brake-specific energy consumption decreased by 10.37% when using the CG2-HPF arrangement. For emmisions, CG2-HPF reduced carbon monoxide, hydrocarbon, and smoke emissions by 33.53%, 18.35%, and 14.37%, respectively, in comparison with SG-HPF. Regarding combustion, CG2-HPF resulted in a high heat release rate owing to the reentrant chamber profile of CG2 which improves the air–fuel mixture rate, atomization, and evaporation rate, resulting in more efficient combustion, increased cylinder pressure, and increased heat release rate. Thanks to the geometry of the reentrant profile, the turbulent kinetic energy of the fuel mixture is maintained and returned to the combustion zone. Thus, the stagnation of rich mixtures within the combustion zone tend to decrease. Overall, the CG2 geometry was found to be the optimum geometry profile for HPF, based on improved performance and combustion characteristics, as well as reduced exhaust emissions.
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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 | 0.001 |
| 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".