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Record W2145265059 · doi:10.1109/tdei.2009.5128496

Effect of Discharge Electrode Parameters on the Flow Velocity Profile of the Wire-rod Type Electrohydrodynamic Gas Pump Exit

2009· article· en· W2145265059 on OpenAlexaff
Nozomi Takeuchi, Koichi Yasuoka, Jen‐Shih Chang

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2009
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrohydrodynamicsCorona dischargeElectrodeMaterials scienceBrush dischargeCorona (planetary geology)VoltageVolumetric flow rateMechanicsIon windPartial dischargeFlow velocityAnalytical Chemistry (journal)Flow (mathematics)Electrical engineeringComposite materialChemistryPhysics

Abstract

fetched live from OpenAlex

Experimental investigation has been conducted to study the effects of corona wire diameter, pipe length, and corona polarity on outlet flow velocity distribution profile of a wire-rod type electrohydrodynamic (EHD) gas pump. Upon applying negative or positive dc high voltage between a wire electrode (outer diameter (o.d.) 60 μm, 200 μm, or 300 μm) and a rod electrode (o.d. 3 mm) in atmospheric air, corona discharge occurs and EHD gas flow is generated in the direction from the wire electrode to the rod electrode through a cylindrical pipe (inner diameter (i.d.) 20 mm). For both polarities, the discharge current and average flow velocity increase monotonically on increasing the applied voltage before the onset of spark discharge. Using wire electrodes with a smaller diameter, stable corona discharge between corona onset and spark onset is generated in a wider voltage range, and the discharge current becomes larger, resulting in a higher flow velocity. The maximum average flow velocity of 2.0 m/s, corresponding to a flow rate of 38 l/min, was achieved with a wire of diameter 60 μm by applying a voltage of -16 kV.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.221
Teacher spread0.212 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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