Numerical Investigation on NO to NO<sub>2</sub> Conversion in a Low-Temperature Combustion CI Engine
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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 NO<sub>2</sub> concentrations compared with traditional high temperature combustion conditions. Experimental investigations have been carried out to show the hydrocarbon impact on NOx emissions and NO-NO<sub>2</sub> 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 NO<sub>2</sub> 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 NO<sub>2</sub> 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-NO<sub>2</sub> conversion. The results showed that NO-NO<sub>2</sub> 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 NO<sub>2</sub> under compression alone without ignition. The numerical study shows increasing concentration of NO to NO<sub>2</sub> conversion until 1000K after which the NO<sub>2</sub> 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 NO<sub>2</sub>.</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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".