An Investigation of OME<sub>3</sub>-Diesel Fuel Blend on a Multi-Cylinder Compression Ignition Engine
Bibliographic record
Abstract
Oxygenated, low energy-density fuels have the potential to decouple the NOx-soot emissions trade-off in compression-ignition engines. Additionally, synthetic fuels can provide a pathway to reach carbon-neutral utilization of hydrocarbon-based fuels in IC engines. Oxymethylene Dimethyl Ether (OME) is one such synthetic, low energy-density fuel, derived from sustainable sources that in combination with conventional fossil fuels with higher energy content, has the potential to reduce CO2 emissions below the US and EU VI legislative limits, while maintaining ultra-low soot emissions. The objective of this work is to investigate and compare the performance, emissions and efficiency of a modern multi-cylinder diesel engine under conventional high temperature combustion (HTC) with two different fuels; 1) OME310 - a blend of 10% OME3 by volume, with conventional Ultra-Low Sulphur Diesel (ULSD), and 2) D100 - conventional ULSD in North America. EGR sweep tests at three speed-load points (with highest time residency on the US EPA Emission Test Cycles) have been carried out on a multi-cylinder compression ignition engine to allow a detailed comparison of the two fuels. The engine employs production-level hardware with no modifications, allowing for turbocharger-EGR trade-off and accessories friction loads to be accounted for. All results are reported on a brake-specific basis. The tests results indicate that OME310 is effective in reducing soot and HC emissions under low load operation by up to 50% while no discernible improvement is seen at higher loads. The lower soot can permit higher intake dilution levels to be applied with Exhaust Gas Recirculation (EGR) to reduce NOx emissions. The burning characteristics such as the combustion phasing and duration, with OME310 are similar or improved compared to that of D100 and the CO2 emissions are reduced by up to 4% with improved brake thermal efficiency under all the tested speed/load conditions.
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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".