Using renewable n-octanol in a non-road diesel engine with some modifications
Bibliographic record
Abstract
n-Octanol is a promising biofuel synthesized from biomass with several properties closer to diesel than the more popularly researched n-butanol. This study investigates the effects of injection timing (2°CA advance & retard), EGR (up to 30%) and Oct30 (30% by vol. of n-octanol in diesel) on combustion, performance, and emissions of a DI diesel engine. Results in comparison with diesel indicated Oct30 blend presented an enhanced premixed combustion phasing with higher peaks of pressure and HRR. BSFC was found to be slightly higher for Oct30 blend at all EGR rates. Further, when the injection timing is advanced, the blend produced better BSFC. Oct30 delivered better BTE at all injection timings. NOx and smoke emissions are lower for Oct30 at all conditions. Oct30 could overcome the trade-off between smoke and NOx emissions at a combination of certain EGR and injection timings. It was found that at advanced injection, the reduction in NOx and smoke density was 19.02% and 57.14%, respectively, while BTE increased by 4.6% and BSFC increased by 1.3%. At late injection, a reduction of 50.87% in NOx emissions and 15.87% in smoke density was achieved with a slight drop in BTE by 3.5% and an increase in BSFC by 9.7%.
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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".