Investigation of Fuel Injection Strategies for Direct Injection of Neat n-Butanol in a Compression Ignition Engine
Bibliographic record
Abstract
In this study, impacts of neat n-butanol fuel injection parameters on direct injection (DI) compression ignition (CI) engine performance were investigated to gain knowledge for understanding the fuel injection strategies for n-butanol. The engine tests were conducted on a four-stroke single-cylinder DI CI engine with a compression ratio of 18.2:1. The effects of fuel injection pressure (40, 60 and 90 MPa) and injection timing in a single injection strategy were investigated. The results showed that an increase in injection pressure significantly reduced nitrogen oxides (NOx) emissions which is the opposite trend seen in conventional diesel combustion. The parallel use of a higher injection pressure and retarded injection timing was a proposed method to reduce NOx and cylinder pressure rise rate simultaneously. NOx was further reduced by using exhaust gas recirculation (EGR) while keeping near zero soot emissions. However, the high load limit was around 6.5 bar indicated mean effective pressure (IMEP) due to high cylinder pressure rise rate. Next, the effect of a split injection, in which the fuel delivery is divided into two injections (first and second injection ratio: 50:50) near top dead center, on the engine performance was investigated to reduce the cylinder pressure rise rate. The split injection could significantly reduce the cylinder pressure rise rate without compromising the indicated thermal efficiency, NOx and soot emissions. It was also demonstrated that the high load limit could be extended to 11.5 bar IMEP by modifying the split injection ratio while maintaining high thermal efficiency and clean emissions.
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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 | 0.001 |
| 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".