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Record W3122264284 · doi:10.4271/2021-01-0506

Numerical Investigation on NO to NO<sub>2</sub> Conversion in a Low-Temperature Combustion CI Engine

2021· article· en· W3122264284 on OpenAlexaff
Nupur Gupta, Xiao Yu, Nick A. Eaves, Meiping Wang, Ming Zheng

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCombustionAutomotive engineeringHomogeneous charge compression ignitionMaterials scienceComputer scienceEnvironmental scienceCombustion chamberChemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

<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>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.008
GPT teacher head0.217
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSAE technical papers on CD-ROM/SAE technical paper seriesSame topicAdvanced Combustion Engine TechnologiesFrench-language works237,207