Study of Low Temperature Combustion with Neat n-Butanol on a Common-rail Diesel Engine
Bibliographic record
Abstract
This study investigates neat n-butanol, as a cleaner power source, to directly replace conventional diesel fuels for enabling low temperature combustion on a modern common-rail diesel engine. Engine tests are performed at medium engine loads (6∼8 bar IMEP) with the single-shot injection strategy for both n-butanol and diesel fuels. As indicated by the experimental results, the combustion of neat n-butanol offers comparable engine efficiency to that of diesel while producing substantially lower NOx emissions even without the use of exhaust gas recirculation. The greater resistance to auto-ignition allows n-butanol to undergo a prolonged ignition delay for air-fuel mixing; the high volatility helps to enhance the cylinder charge homogeneity; the fuel-borne oxygen contributes to smoke reduction and, as a result, the smoke emissions of n-butanol combustion are generally at a near-zero level under the tested engine operating conditions. However, the n-butanol combustion process typically features rapid heat release within a short combustion duration, which leads to high pressure rise rates. The use of exhaust gas recirculation is effective to postpone the ignition and slow down the combustion rates, but deteriorated HC and CO emissions and, in certain cases, misfire incidences are observed with high rates of exhaust gas recirculation. When low temperature combustion is enabled under the tested conditions, the application of n-butanol replacing diesel offers improved engine efficiency and requires less use of exhaust gas recirculation.
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 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".