Impact of Fuelling Techniques on Neat n-Butanol Combustion and Emissions in a Compression Ignition Engine
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This study investigated neat n-butanol combustion, emissions and thermal efficiency characteristics in a compression ignition (CI) engine by using two fuelling techniques - port fuel injection (PFI) and direct injection (DI). Diesel fuel was used in this research for reference. The engine tests were conducted on a single-cylinder four-stroke DI diesel engine with a compression ratio of 18.2 : 1. An n-Butanol PFI system was installed to study the combustion characteristics of Homogeneous Charge Compression Ignition (HCCI). A common-rail fuel injection system was used to conduct the DI tests with n-butanol and diesel. 90 MPa injection pressure was used for the DI tests. The engine was run at 1500 rpm. The intake boost pressure, engine load, exhaust gas recirculation (EGR) ratio, and DI timing were independently controlled to investigate the engine performance.</div><div class="htmlview paragraph">The pressure rise rate of the DI could be lowered easily by retarding the injection timing without using EGR, whereas a very high EGR ratio was required for the PFI to reduce the pressure rise rate to the same level of the DI. Soot and nitrogen oxides (NOx) emissions for both fuelling techniques were at a very low level compared to those of base diesel, but the NOx for DI was slightly higher than that for PFI at 0% EGR. However, by using EGR, NOx was reduced to around 20 ppm, similar to the PFI level. Indicated thermal efficiency of the DI was greater than that of the PFI with the same NOx and cylinder pressure rise rate.</div></div>
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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 | 0.001 |
| 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) | 0.000 | 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 it