An Investigation Into OME3 on a High Compression Ratio Engine
Bibliographic record
Abstract
Abstract For decades, alternative fuels have been studied to further engine efficiency and lower combustion emissions. Of these fuels, biodiesel, alcohols, and ethers have shown advantageous benefits of improved mixing capability or reduced combustion emissions. Ether fuels consist of a range of C-O-C chain lengths that offer various noteworthy fuel properties such as fuel oxygen content and cetane number. In this work, oxymethylene dimethyl ether (OME3) and diesel are used as neat and blended fuels on a single-cylinder high compression ratio engine. Four test fuels are investigated in this work; baseline diesel, two diesel/OME3 blends, and neat OME3 fuel. Engine tests are conducted at an engine load of 6 bar and the intake oxygen concentration is modulated via EGR to realize the resulting engine performance, stability, and exhaust emissions among the test fuels. The results show that blending OME3 fuels with diesel is an effective technique to reduce soot emissions with minimal effect on NOx emissions. Moreover, neat OME3 was capable of emitting low NOx and soot emissions with a lower EGR amount than that of diesel-blends, mitigating negative combustion implication of EGR at high levels.
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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".