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Record W1984897529 · doi:10.1115/icef2011-60132

A Preliminary Study of the Spark Characteristics for Unconventional Cylinder Charge With Strong Air Movement

2011· article· en· W1984897529 on OpenAlexafffund
Shui Yu, Kelvin Y. Xie, Xiaoye Han, Marko Jeftić, Tongyang Gao, Ming Zheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsMechanicsSpark gapIgnition systemPlasmaSPARK (programming language)Materials scienceElectrodeVoltageFlow velocityCylinderDissipationFlow (mathematics)Electrical engineeringPhysicsThermodynamicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Detailed fundamental understanding of spark discharge under strong air movement condition is crucial to optimize the ignition systems for stratified charge engines. In this paper, extensive bench tests of spark discharge under strong air movement condition are conducted by means of both optical and electrical diagnosis. Strong correlations between the physical structures of spark plasma channel and the gas velocity are found in this paper. The spark heat dissipation distance, the plasma stretched distance and the plasma area under various flow velocities are analyzed. The resistance between the electrode gaps is increased with the enhancement of flow velocity. As a result, the discharge voltage is enhanced, while the discharge duration is shortened. When the flow velocity is enhanced substantially, restrikes of spark discharge are observed. The increasing rate of the discharge voltage before the first restrike is found to be a 2-order polynomial relation to the gas velocity. With the enhancement of flow velocity, the delivered discharge energy increases linearly at the velocity below 25m/s, while it tends to be maintained at the higher flow velocities. Both the increase of the electrode gap size and the flow velocity shorten the spark discharge duration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.416

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 head0.028
GPT teacher head0.219
Teacher spread0.191 · 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 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

Citations12
Published2011
Admission routes2
Has abstractyes

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