Empirical Study of Simultaneously Low NOx and Soot Combustion With Diesel and Ethanol Fuels in Diesel Engine
Bibliographic record
Abstract
Diesel low temperature combustion (LTC) is capable of producing diesel-like efficiency while emitting ultra-low nitrogen oxides (NOx) and soot emissions. Previous work indicates that well-controlled single-shot injection with exhaust gas recirculation (EGR) is an operative way of achieving diesel LTC from low to mid engine loads. However, as the engine load is increased, demanding intake boost and injection pressure are necessary to suppress high soot emissions during the transition to LTC. The use of volatile fuels such as ethanol is deemed capable of promoting the cylinder charge homogeneity, which helps to overcome the high soot challenge and, thus, potentially expand the engine LTC load range. In this work, LTC investigations were carried out on a high compression ratio (18.2:1) engine. Engine tests were first conducted with diesel and LTC operation at 8 bar indicated mean effective pressure (IMEP) was enabled by sophisticated control of the injection pressure, injection timing, intake boost, and EGR application. The engine performance was characterized as the baseline, and the challenges were identified. Further tests were aimed to improve the engine performance against these baseline results. Experiments were, hence, conducted on the same engine with secondary ethanol port fuelling (PF). Single-shot diesel direct injection (DI) was applied close to top dead center (TDC) to ignite the ethanol and control the combustion phasing. The control sensitivity was studied through injection timing sweeps and EGR sweeps. Additional tests were performed to investigate the ethanol-to-diesel ratio effects on the mixture reactivity and the engine emissions. Engine load was also raised to 16.4 bar IMEP while keeping the simultaneously low NOx and soot emissions. Significant improvement of engine control and emissions was achieved by the DI+PF strategy.
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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) | 0.001 | 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".