Dimethyl Ether to Power Next-Generation Road Transportation
Bibliographic record
Abstract
Review Dimethyl Ether to Power Next-Generation Road Transportation Simon LeBlanc , Xiao Yu , Linyan Wang , and Ming Zheng , * Department of Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Avenue, Windsor, N9B 3P4 Ontario, Canada * Correspondence: mzheng@uwindsor.ca Received: 20 March 2023 Accepted: 8 June 2023 Published: 19 June 2023 Abstract: The prevailing transportation uses internal combustion engines powered by fossil fuels that bear the reputation of carbon dioxide release among other harmful emissions. As an alternative, dimethyl ether (DME) has shown a high potential to mitigate emission challenges. The properties of DME present a highly reactive and volatile fuel suitable for clean combustion. However, the onboard handling of liquified DME is an ongoing challenge, especially for high-pressure direct injection applications. This paper aims to evaluate the sustainability, fuel handling, and combustion characteristics of DME as a clean and efficient fuel for sustainable on-road transportation. Strategies toward integrating DME fuel for automotive applications are emphasized. An overview of DME production is provided with relevance to current industry practices. Thereafter, the chemical and physical properties of DME are highlighted. The handling challenges of DME are accentuated, and accordingly, recommendations are made for setting up fuel management systems applicable to on-road engines and research laboratories. The DME fueling configurations, e.g., port injection and direct injection, are summarized. Empirical tests studied the engine and emission performance of DME combustion. Ultra-low NOx and smoke emissions, with high combustion efficiency, are achieved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".