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Record W2112685620 · doi:10.1109/tia.2010.2045324

Dynamic Induction Charging of Particles With Finite Conductivity

2010· article· en· W2112685620 on OpenAlexaff
Deying Yu, G.S.P. Castle, Kazimierz Adamiak

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsWestern University
Fundersnot available
KeywordsConstant (computer programming)PermittivityConductivityFinite element methodTime constantThermal conductionMechanicsRelaxation (psychology)Materials scienceElectrodeTransient (computer programming)Particle (ecology)DielectricPhysicsElectrical engineeringComposite materialEngineeringComputer scienceThermodynamicsOptoelectronics

Abstract

fetched live from OpenAlex

Despite the common belief that, in the process of induction charging, the actual charging time constant and the material-based relaxation time constant are equal, there is no proof that these parameters are related at all. The purpose of this paper is to determine the actual induction charging time constant for practical types of particles having both finite conductivity and permittivity. All investigations were based on numerical simulations done for idealized geometric models of the problem. The model was developed for the transient case and solved by using the COMSOL commercial software, which is based on the finite-element method. Spherical and rough particles with various contact areas with the ground electrode were considered. The surface conduction of the particle was neglected. The effect of the particle contact area, conductivity, and permittivity on the actual charging time constant was investigated, and the results were compared with that predicted by the classic relaxation time constant, as defined by the material properties. It was found that the actual time constant is not equal to the relaxation time constant but is directly affected by the value of the contact area between the particle and ground electrode.

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 categoriesInsufficient payload (model declined to judge)
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 score0.158
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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.

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
Published2010
Admission routes1
Has abstractyes

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