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Record W1997754469 · doi:10.1115/icef2005-1109

The Lean Mixture Operational Limits of a S.I. Engine When Operated on Fuel Mixtures

2005· article· en· W1997754469 on OpenAlexaff
Hailin Li, Ghazi A. Karim, A. Sohrabi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of CalgaryNational Research Council Canada
Fundersnot available
KeywordsGasolineOctane ratingCombustionAutomotive engineeringLimit (mathematics)NOxDrop (telecommunication)Process engineeringEnvironmental scienceFlammability limitMethaneEngine efficiencyHomogeneous charge compression ignitionCompression ratioComputer scienceInternal combustion engineCombustion chamberMechanical engineeringWaste managementEngineeringChemistryMathematics

Abstract

fetched live from OpenAlex

The operation of S.I. engines on lean mixtures is attractive in principle since it can provide improved fuel economy, reduced tendency to knock and extremely low NOx emissions. However, the associated flame propagation rates become degraded significantly and drop sharply as the operating mixture is made increasingly lean. Consequently, there exist distinct operational lean mixture limits beyond which satisfactory engine performance cannot be maintained due to the resulting prolonged and unstable combustion processes. The paper presents experimental data obtained in a single cylinder, variable compression ratio, S.I., CFR engine when operated in turn on CH4, H2, CO, gasoline, iso-octane and some of their binary mixtures. A quantitative approach for determining the operational limits of S.I. engines is suggested, compared and validated against corresponding experimental results of other traditional approaches. On this basis, the dependence of the values of the lean mixture operational limits on the composition of the fuel mixtures is investigated and discussed. The operational limit for throttled operation with methane as the fuel is also established.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.441

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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 designSimulation or modeling
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
Published2005
Admission routes1
Has abstractyes

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