Gasoline−Di-methyl Ether Homogeneous Charge Compression Ignition Engine
Bibliographic record
Abstract
The combustion and knock characteristics in an engine were investigated under the homogeneous charge compression ignition (HCCI) operation condition fueled with and gasoline and di-methyl ether (DME) with regard to the valve timing, in order to identify an operating limit and correlation between knock and operating conditions. Commercial gasoline was used as fuel and injected at the intake port using port fuel injection equipment. Di-methyl ether (DME) was used as an ignition promoter and was injected directly into the cylinder during the intake stroke. Different intake valve timings and fuel amounts were tested to verify the knock characteristics of the HCCI engine. In order to characterize the high load operation limit, the peak combustion pressure, rate of combustion pressure rise, and indicated mean effective pressure were correlated with the knocking probability and intensity. The start of combustion was independent from the intake valve open (IVO) timing. The burn duration and the rate of the combustion pressure rise depended on the fuel quantity and the IVO timing. The knocking probability and intensity increased with the peak combustion pressure and the combustion pressure rise. According to the indicated mean effective pressure decrease when the total air excess ratio was lower than 2.4, the high load operating range of the test engine was limited to under 0.5 MW/m 2 . The knocking probability can be predicted by two derived equations. The hydrocarbon emission increased at the retarded intake valve open timing and the lower total air excess ratio. The carbon dioxide emission was influenced mainly by the total air excess ratio.
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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".