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Record W1991660108 · doi:10.1299/jsmeb.48.265

A Study on the Effect of Stratified Mixture Formation on Combustion Characteristics in a Constant Volume Combustion Chamber

2005· article· en· W1991660108 on OpenAlexfundno aff
Kihyung Lee, Changhee Lee, Hae-Young Jeoung

Bibliographic record

VenueJSME International Journal Series B · 2005
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsCombustionCombustion chamberSchlieren photographyVolume (thermodynamics)MechanicsSootTurbulenceSchlierenLean burnMaterials scienceNOxThermodynamicsChemistryFlow (mathematics)PhysicsFlow visualization

Abstract

fetched live from OpenAlex

It is well known that a lean burn engine caused by stratified mixture formation has many kinds of advantages to combustion characteristics, such as higher thermal efficiency and lower CO, NOx levels than conventional homogeneous mixture combustion. Although this combustion can achieve low fuel consumption, it produces much unburned hydrocarbon and soot because of inhomogeneity of the charge mixture in the combustion chamber. Therefore, it is necessary to investigate the effect of mixture formation on combustion characteristics in order to obtain the stable lean combustion. In this paper, fundamental studies for stratified combustion were carried out using a constant volume combustion chamber. The effect of mixture formation on the combustion characteristics in the chamber was examined experimentally. In addition, the effect of turbulence on stratified charge combustion process was observed by schlieren photography. From this study, as the swirl intensity increases, (Sv)max is rapidly enhanced and the period of combustion is shortened. We also find that the stratification degree can be quantified by using burning velocity and it was controlled by induced air pressure and turbulent intensity.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.493

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 head0.235
Teacher spread0.227 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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