Energy Efficiency Comparison between Butanol and Ethanol Combustion with Diesel Ignition
Bibliographic record
Abstract
The use of low temperature combustion (LTC) in diesel engines tends to suppress the NOx and dry soot emissions from diesel engines. However, due to the limitations of conventional diesel fuel properties, such as the high reactivity and low volatility, implementation of LTC is highly dependent on the application of exhaust gas recirculation (EGR). While the replacement of some of the fresh air intake with the burnt exhaust gas using EGR prevents premature combustion, it also results in a reduction in thermal efficiency. In this work, the use of two different alcohol fuels, ethanol and butanol, in a high compression ratio diesel engine has been investigated to examine their potential as substitutes for conventional diesel fuel when operating under low temperature combustion mode. The effect of diesel injection timing, alcohol fuel ratios, and EGR on engine emissions and efficiency were studied at indicated mean effective pressures in the range 0.8 to 1.2 MPa. From the data obtained it indicates that combustion with ultra-low smoke and nitrogen oxides emissions can be achieved with port injection of butanol at low to medium engine loads, and with port injection of ethanol at high engine loads. The major challenges encountered in these alternative fuel investigations were the control of the onset of combustion of butanol and the peak cylinder pressure of ethanol combustion. The peak pressure rise rate was also higher than diesel baseline for both butanol and ethanol combustion. To some extent these issues were overcome by a combination of the use of exhaust gas recirculation and changes to the diesel injection timing. However, while the use of these alcohol fuels has been shown to be promising, more work on their practical implementation with LTC mode operation is still required.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".