MétaCan
Menu
Back to cohort
Record W2290610200 · doi:10.1109/tmag.2015.2481924

New Approach for Accurate Prediction of Eddy Current Losses in Laminated Material in the Presence of Skin Effect With 2-D FEA

2015· article· en· W2290610200 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEddy currentSkin effectFinite element methodElectrical conductorCurrent (fluid)Materials scienceMagnetMechanicsEddy-current testingMechanical engineeringStructural engineeringPhysicsComposite materialEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Two-dimensional finite element (FE) models normally assume the laminated core as a non-conductive material, and therefore, the damping effect of the eddy currents is ignored in the field solution. Besides, the use of integrated iron loss models in FE software could result in overestimated eddy current losses at high frequencies if the skin effect is ignored. This paper presents a simple way to accurately predict eddy current losses in laminations with FE analysis. An experimental setup along with the simulation models is used to demonstrate the validity of the method. The utility of this method in loss separation and identification of loss coefficients in the presence of skin effect is also illustrated. This method is also applied to a synchronous permanent magnet (PM) machine with sinusoidal current.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.315

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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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