Energy Conversion Efficacy of Neat Dimethyl Ether Combustion with Heat Release Characterization and Emission Analysis
Bibliographic record
Abstract
Dimethyl ether (DME) is widely regarded as a suitable energy source for compression ignition power systems because of its high reactivity. It has been widely reported that DME possesses a significantly low propensity to form soot, hindering the innate NOx-soot trade-off encountered with diesel fuel operation. Beyond the fuel-borne oxygen content of DME, its unique physical properties present a contrasting combustion behavior which may be advantageous to direct injection systems, especially concerning the mixing-controlled combustion mode. This work aims to detail the energy conversion efficacy of DME through heat release characterization and exhaust emission speciation. The tests were controlled within a single-cylinder research engine with an off-board high-pressure injection system to handle liquified DME up to 1000bar. To mitigate interference in fuel additives over the combustion behavior, the high-pressure fuel system specifically managed neat DME. The in-cylinder pressure was the indicator for combustion behavior, whereas exhaust emissions were sampled with infrared and mass spectroscopy for exhaust speciation. The in-cylinder combustion profile was aligned with the actual rate of injection to designate the characteristics between the injection and combustion events. Overall, the fuel-to-heat conversion efficiency is comparable, while the combustion efficiency is slightly greater owing to lower carbon monoxide emissions. Without injection-combustion overlap, i.e. low load and primarily premixed combustion, the heat release pattern of DME was like diesel. Under extended injection-combustion overlap, diesel finished injection up to CA50% of cumulative heat release whereas DME injection often finished up to ~CA80%. The end of the injection was followed by a fast and abrupt drop in heat release, e.g. the end of combustion. The application of exhaust gas dilution, however, altered the end of the combustion pattern and promote carbon monoxide emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".