Hydrocarbon impact on NO to NO <sub>2</sub> conversion in a compression ignition engine under low-temperature combustion
Bibliographic record
Abstract
Compression ignition engines can employ high rates of exhaust gas recirculation to realize low-temperature combustion in order to reduce the NO x emissions. However, a substantial increase in NO 2 contribution to the NO x emissions is also observed. The relationship between this NO to NO 2 conversion is also affected by the hydrocarbons originating mainly from the fuel. This can have important consequences for the design of the exhaust after-treatment system. Therefore, this article presents an empirical investigation of the impact of hydrocarbon emissions on the in-cylinder NO–NO 2 conversion process. First, engine motoring tests are performed with propane and NO gases dosed into the engine intake manifold. Engines with different compression ratios are employed to study the effect of in-cylinder temperature and intake HC–NO ratio on the NO–NO 2 conversion process. Next, the hydrocarbon impact on the NO x survivability at different engine combustion modes is investigated using a common-rail diesel engine test platform with independent control of exhaust gas recirculation, intake boost, and exhaust back pressure. Results show that the existence of hydrocarbon has a strong promotion effect of converting NO to NO 2 . During compression test, NO–NO 2 conversion rate can reach 95% under certain intake HC–NO concentration ratio, and the minimum HC–NO concentration ratio to sustain a high NO–NO 2 conversion rate is sensitive to peak in-cylinder temperature; engine combustion results also show that hydrocarbon not only can promote the in-cylinder NO–NO 2 conversion process, but also has the potential of decreasing the total NO x emissions under low-temperature combustion mode.
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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) | 0.001 | 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".