Numerical Investigation on NO to NO<sub>2</sub> Conversion in a Low-Temperature Combustion CI Engine
Bibliographic record
Abstract
Low temperature combustion (LTC) has been proved to overcome the trade-off between NOx and soot emissions in direct injection compression ignition engines. However, the lowered NOx emissions are accompanied by high hydrocarbon and CO emissions. Moreover, the NOx emissions under LTC has much higher NO2 concentrations compared with traditional high temperature combustion conditions. Experimental investigations have been carried out to show the hydrocarbon impact on NOx emissions and NO-NO2 conversion under various engine operation conditions, but the mechanism is less understood. The article includes numerical studies of the impact of hydrocarbons in the in-cylinder conversion of NO to NO2 during low temperature conditions in a compression ignition engine. In the present work, a stochastic reactor model with detailed chemical kinetics is utilized to investigate the reaction pathways during the NOx reduction and NO2 conversion processes. The test conditions are simulated for a compression ratio of 13.1:1 to match the existing experimental data. A mixture of propane at 1000 ppm is dosed with NO compositions varying between 40 ppm to 1000 ppm. The reaction pathways depict a dependence on temperature during the NO-NO2 conversion. The results showed that NO-NO2 conversion rates initially increase until NO concentration of 350 ppm and then start to rapidly decrease as the concentration is further increased. The investigation using chemical pathways is conducted to understand the conversion of NO to NO2 under compression alone without ignition. The numerical study shows increasing concentration of NO to NO2 conversion until 1000K after which the NO2 concentration declines for initial NO dosing of 350 ppm. Furthermore, the study presents the other species responsible for the destruction of NO and has not been identified in earlier literature. This paper aims to understand the role of the dominant species and the process of conversion of NO to NO2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".