Combustion and Exhaust Gas Speciation Analysis of Diesel and Butanol Post Injection
Bibliographic record
Abstract
Experimental testing was done with a modern compression ignition engine to study the effect of the engine load and the effect of different fuels on the post injection characteristics. Two different fuels were utilized; ultra-low sulphur diesel and n-butanol. The results showed that a post injection can be an effective method for increasing the operating range of the engine load. Engine operation at high load can be limited by the peak cylinder pressure but the test results showed that an early post injection can increase the engine load without increasing the peak in-cylinder pressure. Neat butanol combustion may have a very high peak in-cylinder pressure and a very high peak pressure rise rate even at low load conditions. The test results showed that a butanol post injection can contribute to engine power without significantly affecting the peak pressure rise rate and the peak in-cylinder pressure. The test results indicated that high load conditions were more favourable for the combustion of delayed post injections. For diesel fuel post injection with exhaust gas recirculation, the earliest post injection timing was limited by high smoke emissions but this was not the case for butanol. However, the delayed post injections with butanol fuel were limited by the loss of ignition of the butanol post injection. An early post injection reduced the exhaust carbon monoxide emissions for the neat butanol tests but not for the diesel fuel tests. The hydrocarbon speciation analysis indicated that the butanol post injection tended to produce mostly formaldehyde, ethane, and unburned butanol hydrocarbons and that there were no detectable quantities of hydrogen, methane, ethylene, and propylene at these test conditions. It was found that the formation of these latter species was sensitive to the post injection timing when diesel fuel was used with reduced intake oxygen.
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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 | 0.001 | 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) | 0.003 | 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".