Effect of fuel injection strategies on the performance of the common rail diesel injection (CRDI) engine powered by biofuel
Bibliographic record
Abstract
The effects of fuel injection strategies on the characteristics of common rail diesel injection engine using mahua methyl ester (MME20) blend have been investigated. Fuel injection strategies such as fuel pressure and split injection have been implemented on a test engine. When MME20 was used as the fuel on mechanical injection at an injection pressure of 22 MPa, specific fuel consumption and NOx emission were found to be increased and brake thermal efficiency (BTE) decreased. In the first phase, in order to optimise the utilisation of the mahua methyl ester blend, fuel injection pressure was increased from 20 to 50 MPa with an increment of 10 MPa. The experimental observation reveals that high fuel injection pressure (50 MPa) exhibits higher BTE and better combustion characteristics when compared with decremented injection pressures. HC, CO and smoke level decreased with an increase in injection pressure due to better-atomised spray and mixture formation. In the second phase, implementation of spilt injection 5% MME20 as pilot injection at 5°, 10° and 15° CA before main injection was identified with the decrease of HC, CO, NOx and smoke emission with marginal sacrifice of BTE compared with diesel fuel.
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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".