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Record W3105466547 · doi:10.1063/5.0022618

Numerical simulation of the Trichel pulse characteristics in SF<sub>6</sub>/N<sub>2</sub> gas mixtures

2020· article· en· W3105466547 on OpenAlexafffund
Qingqing Gao, Xiaohua Wang, Kazimierz Adamiak, Aijun Yang, Dingxin Liu, Mingzhe Rong, Jiawei Zhang

Bibliographic record

VenuePhysics of Plasmas · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsElectric fieldIonizationAtomic physicsPhysicsBoltzmann equationDiffusionElectronIonPlasmaPoisson's equationElectric discharge in gasesPulse (music)VoltageElectrodeAnalytical Chemistry (journal)MechanicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

The paper presents the results of a numerical simulation of the effects of N2 on the characteristics of Trichel pulses in SF6 at 0.4 MPa. The simulation was performed in a 2D axisymmetric geometry assuming a three species discharge model for the applied voltage on the discharge electrode to be −25 kV. Three drift-diffusion equations coupled with Poisson's equation are considered to determine the dynamic behavior of Trichel pulses and the temporal and spatial distributions of charged species. The reduced ionization coefficient, the reduced attachment coefficient, the electron mobility, and the electron diffusion coefficient of SF6/N2 mixtures are obtained by solving the two-term Boltzmann equations. The content of N2 varies from 20% to 80%. The distribution of three charged species and the reduced electric field at the discharge tip are presented at different instants of time to explain the formation mechanism of the Trichel pulses in SF6/N2 mixtures. The time-dependent current, the total number of electrons, the positive ions, and the negative ions as well as the reduced electric field at the discharge tip under different SF6/N2 mixtures are analyzed to investigate the sensitivities of these properties to the N2 content.

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 score0.008
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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