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Record W2598558464 · doi:10.1115/1.4036293

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

2017· article· en· W2598558464 on OpenAlexafffund
Kang Pan, James S. Wallace

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpark plugIgnition systemShieldCombustionMaterials scienceShielded cableEngine knockingMechanicsNuclear engineeringIgnition timingHomogeneous charge compression ignitionNatural gasMechanical engineeringGeologyEngineeringChemistryCombustion chamberPhysicsWaste 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 0.1–0.2 ms 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 engine 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.292
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2017
Admission routes2
Has abstractyes

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