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Record W4225003933 · doi:10.18280/mmep.090202

Effect of Room’s Temperature and Electrode Gap on Current of Negative Corona Discharge in Rod-Plane Electrode Configuration

2022· article· en· W4225003933 on OpenAlexvenueno aff
El Hanafi Ouatah, Soufiane Megherfi, Boukhalfa Bendahmane, Y. Zebboudj

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsElectrodeCorona dischargeMaterials scienceVoltageCurrent (fluid)Brush dischargeTownsend dischargePartial dischargePlane (geometry)Corona (planetary geology)MechanicsAnalytical Chemistry (journal)Electrical engineeringChemistryIonPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

The stable corona discharge is widely used in filtration and electrostatic separation in recent years, and several models have been used by researchers to analyze one of its most important properties which is the current-voltage characteristic. The aim of this paper is to investigate the influence of ambient temperature and electrodes’ gap on negative DC discharge using rod-plane geometry, and the Townsend formula was found to be most appropriate model (I=K.V.(V-V0)). The experimental results show that for the same voltage level applied to the high voltage electrode, the discharge current rises with increasing temperature and decreases as the electrodes’ gap increases. Using curve fitting, it was proven that the geometric factor K is proportional to temperature and to the power of the distance between electrodes independently, and the threshold voltage V0 is proportional to the product of the temperature reciprocal and the power of the inter-electrode spacing. From these results, a new modified Townsend formula by introducing the air temperature and the distance between electrodes is proposed to calculate the discharge current with an accuracy of ±10%.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.635

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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