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Record W2052624286 · doi:10.1115/icef2005-1245

Effect of Internal Exhaust Gas Recycling on n-Heptane HCCI Combustion

2005· article· en· W2052624286 on OpenAlexaff
Usman Asad, Ming Zheng, David S.‐K. Ting, Graham T. Reader

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHomogeneous charge compression ignitionCombustionCHEMKINExhaust gas recirculationIgnition systemMaterials scienceSootInternal combustion engineAutomotive engineeringThermodynamicsChemistryCombustion chamberEngineeringPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Homogeneous Charge Compression Ignition (HCCI) combustion is currently limited in application due to several key issues such as a narrow operating range, high hydrocarbon and carbon monoxide emissions and lack of control over the onset of combustion. Exhaust gas recycling (EGR) has the potential to 1) provide reasonable control over the HCCI combustion process and 2) extend its operating range. In this paper, the effect of hot internal EGR on ignition timing and heat release rate of HCCI combustion fuelled with n-heptane has been investigated using the CHEMKIN 0-D closed combustion engine simulation package. An attempt has been made to study the effect of individual components of EGR on cylinder-temperature history and the rate of chemical reaction. The results indicate that combustion initiation is directly linked to the thermal energy contained in the hot EGR but the particular chemical species contained in the EGR have different influences towards ignition timing and heat release rate. This theoretical study would be substantiated by experimental work in the near future.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.261
Teacher spread0.253 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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