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Record W3046673906 · doi:10.1109/tps.2020.3010561

A Novel Method for Circuits of Perfect Electric Conductors in Unstructured Particle-in-Cell Plasma–Object Interaction Simulations

2020· article· en· W3046673906 on OpenAlexafffund
Sigvald Marholm, Diako Darian, Mikael Mortensen, R. Marchand, Wojciech J. Miloch

Bibliographic record

VenueIEEE Transactions on Plasma Science · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNorges Forskningsråd
KeywordsDiscretizationComputer sciencePoisson's equationPlasmaFinite element methodTopology (electrical circuits)Convergence (economics)Particle-in-cellPhysicsElectric potentialComputational scienceVoltageMathematical analysisElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

A novel numerical method has been developed that incorporates electrically conducting objects into particle-in-cell simulations of electrostatic plasma. The method allows multiple objects connected by voltage and current sources in an arbitrary circuit topology. Moreover, by means of an unstructured mesh, the objects can have arbitrary shapes. The electric potential of the objects is solved self-consistently by incorporating charge constraints into the finite element discretization of the Poisson equation. This method has been implemented in a new code, Particles-in-Unstructured-Cells (PUNC), suitable for rapid prototyping. The flexibility of this code has proven convenient to survey various methods, and an issue of reduced convergence rate of today's unstructured plasma-object interaction codes is highlighted. The results for a conducting sphere immersed in the Maxwellian plasma are in good agreement with previous studies.

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 score0.350
Threshold uncertainty score0.698

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 integrity0.0000.000
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 head0.276
Teacher spread0.247 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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