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Record W2094274901 · doi:10.1115/icef2011-60135

An Analysis of the Production of Hydrogen and Hydrocarbon Species by Diesel Post Injection Combustion

2011· article· en· W2094274901 on OpenAlexaff
Marko Jeftić, Usman Asad, Xiaoye Han, Kelvin Y. Xie, Shui Yu, Meiping Wang, Ming Zheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNOxCombustionHydrocarbonDiesel fuelHydrogenCarbon monoxideHydrogen productionDiesel exhaustNitrogen oxideEnvironmental scienceChemistrySmokeWaste managementEnvironmental chemistryPulp and paper industryOrganic chemistryEngineeringCatalysis

Abstract

fetched live from OpenAlex

The effects of post injection on the combustion efficiency, exhaust emissions, and in-cylinder hydrogen generation were experimentally investigated in a modern heavy duty diesel engine. As the post injection was moved away from top dead center (TDC), the test results generally showed increasing carbon monoxide (CO) and total hydrocarbons (THC), fairly constant nitrogen oxide (NOx) emissions while the smoke emissions were more sensitive to the post injection timing. Hydrogen production was observed to be higher at later post injection timings. In a majority of instances, hydrogen production and carbon monoxide formation were very well correlated. Additional tests explored the effects of the overall air-to-fuel ratio on the in-cylinder hydrogen production and the experimental results indicated that a lower air-to-fuel ratio seemed to promote the in-cylinder generation of hydrogen. However, the increased hydrogen production was offset by less efficient power production from the post injection combustion. A Fourier transform infrared (FTIR) spectroscopy analysis of hydrocarbon emissions was carried out in an attempt to determine the effects of diesel post injection timing on individual light hydrocarbon species.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.210
Teacher spread0.198 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2011
Admission routes1
Has abstractyes

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