Hydrocarbon Species Impact on NO to NO2 Conversion in a Compression Ignition Engine Under Low Temperature Combustion Conditions
Bibliographic record
Abstract
Abstract Low temperature combustion has proved to be beneficial for low NOx and particulate matter emissions. Renewable fuels, such as biodiesel, alcohol fuels, and ether fuels can further decrease the carbon footprint of the engine. The NO to NO2 ratio in engine out NOx emissions has shown dependency on the concentration of hydrocarbon emissions. This relationship has a significant impact on the design of exhaust after-treatment systems. However, the effect of the renewable fuels on NO to NO2 conversion process is less understood. This paper investigates the impact of DME and propane on the in-cylinder conversion of NO to NO2 in a compression ignition engine. Firing test under low temperature combustion condition is first performed to demonstrate the impact of HC concentration on exhaust NO concentration and composition. Then, motoring tests are performed with a mixture of the HC and NO dosed into the engine intake manifold. The simplified testing scenario makes it easier to understand HC-NO interaction. To simplify the process of understanding the difference in fuel behavior a study of NO to NO2 conversion as a resolution of engine cycle is conducted using a Gas Sampling Valve which is capable of collecting in-cylinder gases at varying crank-angles. The FTIR data from these compression tests can help assist future mechanism studies to be performed. This study aims to describe the impact of the two fuels on the NO to NO2 conversion process and the boundary conditions at which these differences occur.
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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) | 0.002 | 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".