Influence of Fuel Injection Rate on the Performance, Emission and Combustion Characteristics of DI Diesel Engine Running on Calophyllum Inophyllum Linn Oil (Honne Oil) / Diesel Fuel Blend
Bibliographic record
Abstract
The present work examines the use of a non-edible vegetable oil namely honne oil, a new possible alternative fuel for diesel engine. High viscosity of honne was reduced by blending it with diesel fuel. A direct injection (DI) diesel engine typically used in agricultural sector was operated on neat diesel (ND) and a blend of 50% honne oil with 50% diesel fuel (H50). Rate of injection of fuel was changed (by changing plunger diameter) to study the performance, emission and combustion characteristics. For the blend (H50), increasing the plunger diameter (PD) from the manufacturer specified plunger diameter (8 mm) increased the brake thermal efficiency and reduced CO, HC, smoke opacity and NOx emissions. However, brake thermal efficiency, CO and HC emissions at 10 mm PD were higher with H50 compared to ND (8 mm PD). Smoke opacity and NOx emissions at 10 mm PD were marginally lower for H50 compared to ND. The ignition delay with H50 was decreased as PD was increased. Improved premixed heat release rate was observed with H50 when the PD was increased. The best PD was found to be 10 mm for H50 based on brake thermal efficiency.
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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".