MétaCan
Menu
Back to cohort
Record W2558592748 · doi:10.1115/icef2016-9445

Numerical Studies of Glow Plug Shield on Natural Gas Ignition Characteristics in a CI Engine

2016· article· en· W2558592748 on OpenAlexafffund
Kang Pan, James S. Wallace

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpark plugShieldIgnition systemCombustionShielded cableMechanicsEngine knockingMaterials scienceNuclear engineeringNatural gasIgnition timingInternal combustion engineHomogeneous charge compression ignitionMechanical engineeringGeologyEngineeringCombustion chamberChemistryPhysicsWaste managementElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents a numerical study on fuel injection, ignition and combustion in a direct-injection natural gas (DING) engine with ignition assisted by a shielded glow plug (GP). The shield geometry is investigated by employing different sizes of elliptical shield opening and changing the position of the shield opening. The results simulated by KIVA-3V indicated that fuel ignition and combustion is very sensitive to the relative angle between the fuel injection and the shield opening, and the use of an elliptical opening for the glow plug shield can reduce ignition delay by 0.1∼0.2ms for several specific combinations of the injection angle and shield opening size, compared to a circular shield opening. In addition, the numerical results also revealed that the natural gas ignition and flame propagation will be delayed by lowering a circular shield opening from the fuel jet center plane, due to the blocking effect of the shield to the fuel mixture, and hence it will reduce the DING performance by causing a longer ignition delay.

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 score0.335
Threshold uncertainty score0.406

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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
Published2016
Admission routes2
Has abstractyes

Explore more

Same topicAdvanced Combustion Engine TechnologiesFrench-language works237,207