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Record W2076670417 · doi:10.1109/tia.2012.2209849

Design of a Novel Test Fixture to Measure Rotational Core Losses in Machine Laminations

2012· article· en· W2076670417 on OpenAlexaff
Natheer Alatawneh, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2012
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsFixtureTest fixtureMagnetic circuitCore (optical fiber)Magnetic fieldMechanical engineeringGauge (firearms)LaminationAcousticsRotational speedMagnetic fluxElectrical engineeringMeasure (data warehouse)EngineeringElectronic engineeringComputer scienceMagnetPhysicsMaterials scienceOpticsComposite material

Abstract

fetched live from OpenAlex

The need for testing magnetic materials used in electric machine laminations and measurements under a rotating field is of importance in machine design. In order to obtain satisfactory experimental data, the design of the measurement apparatus deserves particular attention. The purpose of this paper is to propose a novel design of a magnetic circuit based on the Halbach array, which generates a uniform flux density inside the test specimen for the measurement of rotational core loss. The proposed design is simulated and prototyped, and experimental tests are performed on two different samples of M19 gauge-24 and M36 gauge-29 silicon steel at three different frequencies (60 Hz, 400 Hz, and 1 kHz). The field-metric method is used in this work to evaluate the rotational core losses.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.066
GPT teacher head0.284
Teacher spread0.218 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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