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Record W2014628803 · doi:10.1063/1.3362416

DEVELOPMENT OF A FIELD CONCENTRATOR COIL BY FINITE ELEMENT MODELING FOR POWER EFFICIENCY OPTIMIZATION IN EDDY CURRENT THERMOGRAPHY INSPECTION

2010· article· en· W2014628803 on OpenAlexafffund
M. Grenier, Clemente Ibarra‐Castanedo, F. Luneau, Hakim Bendada, Xavier Maldague, Donald O. Thompson, Dale E. Chimenti

Bibliographic record

VenueAIP conference proceedings · 2010
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEddy currentElectromagnetic coilFinite element methodThermographyConcentratorAcousticsMechanical engineeringExcitationElectrical conductorEddy-current testingNondestructive testingMaterials scienceElectrical engineeringOpticsEngineeringPhysicsStructural engineeringInfrared

Abstract

fetched live from OpenAlex

Eddy current thermography is a relatively new inspection technique that takes advantage of the electromagnetic induction phenomenon to generate heat in electro conductive materials during inspection. An interesting advantage of eddy current heating compared to classical optical or ultrasonic heating is that the excitation source is smaller and can be conveniently shaped in order to provide energy efficient localized heating. Such excitation source is more suitable for the development of portable instruments and to perform field inspections. In this paper, finite element modeling (FEM) is used to optimize the eddy current coil configuration in terms of heating power efficiency. The performances of air‐core coils, normally used in eddy current thermography, and a new field concentrator coil are compared and discussed. FEM results demonstrate that the proposed field concentrator coil improves the magnetic coupling with the inspected material and requires less energy than air‐core coils to generate the same temperature variation.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.633

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 integrity0.0000.000
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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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