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Record W4379981934 · doi:10.1088/1361-6463/acdd0f

Numerical approaches in simulating Trichel pulse characteristics in point-plane configuration

2023· article· en· W4379981934 on OpenAlexafffund
Afshin Shaygani, Kazimierz Adamiak

Bibliographic record

VenueJournal of Physics D Applied Physics · 2023
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric fieldDiscretizationMechanicsCorona dischargePlane (geometry)Poisson's equationBoltzmann equationPlasmaPulse (music)ElectronIon windPhysicsComputational physicsThermodynamicsMathematicsGeometryMathematical analysisElectrodeVoltageQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract In this work, a detailed comparison is made of a few different approaches to numerical modeling of non-equilibrium gas discharge plasmas in dry ambient air at atmospheric conditions, leading to Trichel pulse discharge. Simulation models are based on a two-dimensional axisymmetric finite element discretization of point-plane geometry. The negative corona discharge and the hydrodynamic approximation for generic ionic species (electrons, positive and negative ions) are used. The models account for the drift, diffusion, and reactions of the species. They comprise continuity equations coupled to Poisson’s equation for the electric field. Three different formulations were used to specify the ionic reaction rate coefficients. In the first one, the reaction coefficients are approximated by the analytical expressions as a function of the electric field intensity. Two others extract the reaction coefficients from the solution of the Boltzmann equation as a function of the reduced electric field or the electron energy. The effect of gas flow and heating on the pulse characteristics is also investigated. The accuracy of the models has been validated by comparing them with the experimental data.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.051
GPT teacher head0.286
Teacher spread0.236 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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