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Record W4386902724 · doi:10.1109/tmag.2023.3317769

Experimental Validation of a New Power-Equivalent Magnetic Permeability Model for Induction Heating Applications

2023· article· en· W4386902724 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Magnetics · 2023
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec
KeywordsInduction heatingMaterials scienceAlgorithmPhysicsComputer scienceAnalytical Chemistry (journal)Mechanical engineeringQuantum mechanicsChemistryEngineeringElectromagnetic coilOrganic chemistry

Abstract

fetched live from OpenAlex

This article discusses a recently published methodology to determine the time-harmonic (TH) effective magnetic permeability used to simulate induction heating problems comprising non-linear and hysteretic materials. The model and its implementation in a whole multiphysics numerical scheme are first shown and calibrated with temperature-dependent magnetic measurements for simulating the specific behavior of an AISI 4340 working steel piece during the induction heating process. Then, the experimental setup designed to send <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sim }4$ </tex-math></inline-formula> kW of heating power in steel disks is explained. The setup allows the disks to reach the austenitization and Curie temperature of steel in 30 s. The temperature distributions were measured with an infrared (IR) thermal camera and compared with the simulation both in space and time during the heating. In addition to being numerically inexpensive, the computational model successfully predicts the temperature within 10% of accuracy between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$200 ^{\circ} \text{C}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$800 ^{\circ} \text{C}$ </tex-math></inline-formula>.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.704

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 head0.041
GPT teacher head0.275
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