Combustion and Emission Characteristics of SI and HCCI Combustion Fueled with DME and OME
Bibliographic record
Abstract
DME has been considered an alternative fuel to diesel fuel with promising benefits because of its high reactivity and volatility. Research shows that an engine fueled with DME will produce zero smoke emissions. However, the storage and the handling of the fuel are underlying difficulties owing to its high vapour pressure (530 kPa @ 20 °C). In lieu, OME1 fuel, a derivate of DME, offers advantages exhibited with DME fuel, all the while being a liquid fuel for engine application. In this work, engine tests are performed to realize the combustion behaviour of DME and OME1 fuel on a single-cylinder research engine with a compression ratio of 9.2:1. The dilution ratio of the mixture is progressively increased in two manners, allowing more air in the cylinder and applying exhaust gas recirculation (EGR). The high reactivity of DME suits the capability to be used in compression ignition combustion whereas OME1 must be supplied with a supplemental spark to initiate the combustion. The results indicate that a low-temperature heat release (LTHR) is present during the combustion of DME fuel. Increasing the excess-air ratio is observed to have little effect on the LTHR phasing while increasing the heat released during combustion. Increasing the EGR levels significantly suppress the in-cylinder pressure rise rates of both fuels, and retard the combustion phasing.
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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 | 0.001 | 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) | 0.002 | 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".