Effect of Some Oxygenates on the Opacity Level of a DI Diesel Engine with and without DPF
Bibliographic record
Abstract
Toyota car fitted with smoke meter to measure the opacity in the exhaust was used. Five different types of oxygenates were used with the concentration of each one varied between 5 to 20% by volume at an increment of 5%. The results show a significant reduction in the opacity of the exhaust products. A maximum of 70% reduction was achieved when 15% ethanol was added at 3000 RPM, and 62% reduction when 20% methanol was added at same speed. As for Dimethoxy Ethane (DMET), a maximum reduction of 30% was achieved at 3000 RPM and that of Tri-propylene glycol methyl ether (TPGME) was 27.3% at same speed. Diethylene glycol monoethyl ether (DGME) did not show encouraging results as a maximum reduction of 10.3% was achieved at 2000 RPM with 5% of DGME. Further, it was found that the reduction in the opacity level was less significant when the filter was used. This, perhaps, is due to the nature of the DPF used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".