Combustion Management of Neat Dimethyl Ether Combustion for Enabling High Efficiency and Low NOx Production
Bibliographic record
Abstract
Article Combustion Management of Neat Dimethyl Ether Combustion for Enabling High Efficiency and Low NOx Production Simon LeBlanc, Binghao Cong, Navjot Sandhu, Long Jin, Xiao Yu and Ming Zheng * Department of Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada * Correspondence: mzheng@uwindsor.ca Received: 30 June 2024; Revised: 30 September 2024; Accepted: 10 October 2024; Published: 23 October 2024 Abstract: Modern compression ignition engines heavily rely on exhaust gas recirculation to reduce NOx emissions. Despite this, complex and expensive after-treatment systems are still necessary to comply with stringent emission regulations. Conventional diesel combustion operates on a robust and readily controllable mode through which the high-pressure fuel injection and combustion processes are intimately coupled. The heterogeneous nature of direct injection systems is liable to the NOx-soot trade-off inherent to diesel-fueled engines. Dimethyl ether (DME) presents a unique fuel that is reactive, volatile, and oxygenated, offering significant potential to address emission challenges with reduced reliance on aftertreatment systems. In this research, the combustion management of neat DME fuel was investigated using a high-pressure direct injection system. Principally, the suitability of single-shot fuel scheduling as a combustion management technique for DME under low NOx production was explored. The transient high-pressure injection behaviour of DME was characterized with an offline test bench. A single-cylinder research engine platform was employed to study DME combustion characteristics. A wide range of engine conditions was investigated, including injection pressures from 200 bar up to 880 bar and engine loads from 1 bar up to 17 bar indicated mean effective pressure (IMEP). The combustion management of DME as it relates to fuel injection and operating boundary conditions was emphasized throughout the work. To accomplish this, tests were conducted at direct comparison conditions to diesel operation. Most notably, the DME combustion process finished in a shorter period than diesel, albeit with a significantly longer injection duration. At most operating conditions, the soot emissions were below that of upcoming emission regulations without particulate filter exhaust treatment. Even under high engine load operation—17 bar IMEP—of neat DME, the NOx emissions could be readily contained via EGR management to 51 ppm engine-out NOx during which soot reached a maximum of 1.0 FSN. Such operating circumstances of high engine load and low oxygen availability (overall lambda of 1.2) exhibited a deterministic combustion timing control via injection timing while performing with low combustion noise (4.8 bar/°CA) and high burning efficiency (98.5%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".