An experimental study of engine characteristics and tailpipe emissions from modern DI diesel engine fuelled with methanol/diesel blends
Bibliographic record
Abstract
Herein, we investigated and analysed the performance and characteristics of a compression ignition engine on methanol/diesel blends and the impact of engine loads on tailpipe emissions and engine performance. Four combinations of blended methanol were tested and compared with pure diesel. Engine characteristics, such as: brake thermal efficiency, brake specific fuel consumption and brake power were analysed. HCs and CO emissions followed similar trends. The highest HCs concentration reached 40 ppm at lower load (L1) for pure diesel and 5 ppm for blended fuel at a higher load (L5, B40). NOx emissions showed a different trend, showing a higher concentration of 640 ppm for higher load (L5, B40) and the lowest concentration appeared at lower load (L1) for pure diesel. For each individual load, with blended methanol at different percentages, NOx has increased with higher methanol blends. Whilst with HCs, CO emissions were reduced with an increased percentage of methanol addition. Additionally, smoke emissions were significantly reduced by increasing the methanol percentage in methanol/diesel blends. Pure diesel at all engine loads showed higher brake thermal efficiency and lower fuel consumption. The highest brake specific fuel consumption was found at lower load (L1, B40) and reached up to 0.75 g/kWh.
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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".