A Study of Combustion Inefficiencies in SI Engines Powered by Alcohol and Ether Fuels Using Detailed Emission Speciation
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Advanced combustion engines, as power sources, dominate all aspects of the transportation sector. Stringent emission and fuel efficiency standards have promoted the research interest in advanced combustion strategies and alternative fuels. Owing to the comparable energy density to the existing fossil fuels and renewable production, alcohol and ether fuels may be a suitable replacement, or an additive to the gasoline/diesel fuels to meet the future emission standards with minimal modification to current engine geometry. Furthermore, lean and diluted combustion are well-researched pathways for efficiency improvement and reduction of engine-out emissions of modern engines. However, lean-burn or EGR dilution can introduce combustion inefficiencies in the form of excessive hydrocarbon, carbonyl species and carbon monoxide emissions. In this study, the total energy loss to the exhaust in the form of emission species due to incomplete/inefficient combustion of alcohol (butanol and ethanol) and ether (dimethyl ether) fuels in SI engine has been investigated. The impact of both dilution strategies, fresh air (lean) and EGR dilution on the exhaust gas components for different alternative fuels has been studied. A detailed analysis of the combustion products has been conducted to determine the contribution of different exhaust components to the fuel energy loss to the exhaust species. Additionally, the results have also been compared to gasoline combustion as a baseline. Preliminary test results indicate that as the charge is diluted (either lean or EGR dilution), the contribution of longer chain hydrocarbon species to the exhaust energy increases. DME, primarily following HCCI combustion, exhibits distinct emission speciation.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".