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Record W1539387857 · doi:10.4271/2007-01-1913

Pre-ignition characterization of partially-stratified natural gas injection

2007· article· en· W1539387857 on OpenAlexaff
Edward C. Chan, R. L. Evans, Martin Davy, Stefano Cordiner

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIgnition systemNatural gasCharacterization (materials science)Materials scienceEnvironmental scienceWaste managementEngineeringAerospace engineeringNanotechnology

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">The partially stratified charge (PSC) injection ignition strategy aims to improve thermal efficiency and reduce emissions of lean-burn spark-ignited internal combustion engines by extending the lean limit of operation. The object of the current study is to characterize the pre-ignition formation of the fuel plume in the vicinity of the PSC spark plug, using a complementary experimental and numerical approach. Visualization of the PSC plume was conducted using Schlieren motion photography in a constant volume pressure chamber at various injection pressures and signal durations. Inlet conditions were determined with 1-D and 2-D numerical models derived from experimental conditions. Subsequently, the 3-D numerical study was carried out with FLUENT using the SST <i>k</i>-ω turbulence model. It was found that the simulation results were adequate in representing the experimental data in terms of injection angle and jet penetration. As local fuel concentration of the spark region is vital to the stability of the combustion event, the result of this study will be instrumental in predicting the combustion quality under the PSC regime, leading to an optimized PSC system designs.</div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.225
Teacher spread0.217 · 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.

Study designObservational
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

Citations11
Published2007
Admission routes1
Has abstractyes

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