Ethanol–diesel premixed charge compression ignition to achieve clean combustion under high loads
Bibliographic record
Abstract
Ethanol–diesel dual-fuel premixed charge compression ignition combustion was investigated in a diesel engine with a compression ratio of 18.2:1, where the ethanol was delivered via port fuel injection and the diesel was injected through common-rail high-pressure direct injection. The paper presents the test results highlighting the effect of the fuel ratio, the exhaust gas recirculation and the diesel start of injection on the dual-fuel combustion and the emissions from medium to high loads. The results showed that the usage of ethanol can effectively suppress the nitrogen oxide emissions and the soot emissions with less aggressive exhaust gas recirculation levels than with diesel-only low-temperature combustion. The combustion phasing was responsive to the in-cylinder diesel injection over a wide range of timings. The dual-fuel operation load was increased to an indicated mean effective pressure of 18 bar (nearly the full load of the test engine), with a boost intake pressure of up to 2.5 bar and the ethanol ratio increased to about 0.9. The high-load premixed charge compression ignition was realized through adequate control of the exhaust gas recirculation level, the intake boost pressure, the injection pressure, the injection scheduling and the fuel ratio. The challenges and the approaches to suppress the nitrogen oxide emissions and the soot emissions simultaneously at a high load were demonstrated. The nitrogen oxide level was constrained to within 0.2 g/kW h and the soot level was simultaneously below 0.01g/kW h at a stable operation load with an indicated mean effective pressure of 16 bar and a moderate pressure rise rate.
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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".