Energy Efficiency Comparison between Butanol and Ethanol Combustion with Diesel Ignition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div></div>
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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