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Record W1966364820 · doi:10.1109/tia.2015.2417834

Design Considerations of 2-D Magnetizers for High Flux Density Measurements

2015· article· en· W1966364820 on OpenAlexafffund
John Wanjiku, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic fluxMagnetizationFlux (metallurgy)Magnetic flux leakageEddy currentMagnetYoke (aeronautics)Electromagnetic coilMaterials scienceMechanicsRange (aeronautics)Magnetic fieldElectrical engineeringMechanical engineeringPhysicsEngineeringComputational physicsComposite material

Abstract

fetched live from OpenAlex

A 2-D magnetizer introduces variation in the flux density (B) across the sample under rotational magnetization. This variation requires additional energy requirements that limits the attainable aspect ratios, flux density levels and stresses the supply. A numerical methodology that accounts for the flux leakage and the eddy currents is proposed to analyze uniformity and variation in B in four 2-D magnetizers. The analysis was done beyond the knee of the magnetization curve. Numerical results will show that round magnetizers can mitigate the variation of the square magnetizer by over 92%, by making the MMF more sinusoidal and equalizing the reluctance along the airgap. In addition, deeper yokes minimized the variation by about 50% in the square and the Halbach testers. The results of this analysis was a design that mitigates the variation in B by a combination of sinusoidally distributed windings, and a deep yoke. The proposed magnetizer achieved very high flux densities over a relatively wide frequency range, which were 2.04 and 1.69 T at 60 Hz and 1 kHz, respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.134
GPT teacher head0.286
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2015
Admission routes2
Has abstractyes

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