An Enabling Study of Low Temperature Combustion With Ethanol in a Diesel Engine
Bibliographic record
Abstract
Previous research indicates that the low temperature combustion (LTC) is capable of producing ultra-low nitrogen oxides (NOx) and soot emissions. The LTC in diesel engines can be enabled by the use of heavy exhaust gas recirculation (EGR) at moderate engine loads. However, when operating at higher engine loads, elevated demands of both intake boost and EGR levels to ensure ultra-low emissions make engine controllability a challenging task. In this work, a multifuel combustion strategy is implemented to improve the emission performance and engine controllability at higher engine loads. The port fueling of ethanol is ignited by the direct injection of diesel fuel. The ethanol impacts on the engine emissions, ignition delay, heat-release shaping, and cylinder-charge cooling have been empirically analyzed with the sweeps of different ethanol-to-diesel ratios. Zero-dimensional phenomenological engine cycle simulations have been conducted to supplement the empirical work. The multifuel combustion of ethanol and diesel produces lower emissions of NOx and soot while maintaining the engine efficiency. The experimental setup and study cases are described, and the potential for the application of an ethanol-to-diesel multifuel system at higher loads has been proposed and discussed.
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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) | 0.000 | 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 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".