Heat Release Analysis of Clean Combustion with Ethanol Ignited by Diesel in a High Compression Ratio Engine
Bibliographic record
Abstract
The control of nitrogen oxide and smoke emissions in diesel engines has been one of the key researches in both the academia and industry. Nitrogen oxides can be effectively suppressed by the use of exhaust gas recirculation (EGR). However, the introduction of inert exhaust gas into the engine intake is often associated with high smoke emissions. To overcome these issues there have been a number of proposed strategies, one of the more promising being the use of low temperature combustion enabled with heavy EGR. This has the potential to achieve simultaneously low emissions of nitrogen oxide and smoke. However, a quantitative way to identify the transition zone between high temperature combustion and low temperature combustion has still not been fully explored. The combustion becomes even more complicated when ethanol fuel is used as a partial substitution for diesel fuel. In the work reported here, the heat release rate (HRR) of diesel low temperature combustion at different engine loads and various intake oxygen levels was analyzed. The high temperature combustion zone, the transition zone and the low temperature combustion zone were initially defined from the smoke emissions and then identified on the corresponding HRR characteristics. The HRR-shape changes were observed for different combustion zones. A new parameter named combustion acceleration was proposed which was the first derivative of HRR to capture the HRR-shape change. It was observed that the peak of combustion acceleration can potentially be used as an indication of the transition zone. The same analytical procedure was applied to the ethanol combustion initialized by diesel direct injection. The corresponding results were compared with the diesel single-shot low temperature combustion.
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 | 0.001 | 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) | 0.001 | 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".