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Record W1726392308 · doi:10.1115/icef2003-0768

Prospect of Reduced CO and NOx Emissions in Diesel Dual Fuel Engines

2003· article· en· W1726392308 on OpenAlexaff
Mwila C. Mulenga, Graham T. Reader, David S.‐K. Ting, Ming Zheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNOxDiesel fuelHomogeneous charge compression ignitionCombustionIgnition systemHeptaneDiesel engineExhaust gas recirculationCHEMKINWaste managementMaterials scienceChemistryEnvironmental scienceAutomotive engineeringCombustion chamberThermodynamicsOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

A numerical analysis of homogeneous natural gas/diesel/air mixture, that could be formed when small amounts of diesel pilot are used in a diesel dual fuel engine or when using natural gas/diesel dual fuel homogeneous charge compression ignition combustion, was studied to provide insight on future experimental investigations to be conducted by the institution. Use was made of Curran’s normal-heptane mechanism in CHEMKIN to deduce its effects on lean fuel/air mixtures with respect to ignition timing and carbon monoxide (CO) and oxides of nitrogen (NOx) emissions. For the cases considered, increase in the amount of n-heptane addition resulted in advanced ignition closer to TDC thereby increasing peak cylinder pressure and gross IMEP. Increase in the amount of n-heptane addition also resulted in early decomposition of the fuel consequently advancing the production of OH radicals that oxidised the CO. For the same fuel/air mixture strength, increase in the amount of n-heptane addition increased NOx production due to increased peak temperature, residence time and availability of O2. When using leaner mixtures and small amounts of n-heptane to promote/advance ignition, however, peak cylinder temperatures were reduced and hence comparatively reducing engine-out NOx emissions.

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)0.001
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 score0.124
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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