Impact of Fuel Injection Pressure on Spray and Combustion Characteristics of OME and Diesel Blends
Bibliographic record
Abstract
Abstract Emission regulations focus on the simultaneous reduction of NOx and particulate matter emissions, especially for heavy-duty engines. Oxygenated fuels offer significant advantages in reducing particulate emissions while having little effect on NOx emissions. In addition, renewable fuels present a GHG emission advantage to meet the zero-emission requirements of future hydrocarbon fuels. Among the leading contenders, oxymethylene dimethyl ether (OME) fuels have the potential to be used for direct injection applications. OME as a blend with diesel fuel offers a direct means of improving the emissions of current on-road diesel engines without modification. In this paper, an empirical investigation into spray behavior and engine performance of diesel/OME fuel at 10% by mass has been performed under various fuel injection pressures. Neat diesel fuel was tested as a baseline case. The results are compared to tests under matching conditions using a diesel and OME fuel blend with a focus on spray characteristics, combustion behavior, and engine-out emissions. The physical properties of OME improve the volatility of diesel fuel and can tolerate shorter mixing times without promoting PM production. The PM emissions were found to be reduced by up to 50% and the combustion efficiency was improved at matching NOx levels with OME blending.
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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 | 0.001 |
| 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) | 0.002 | 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".