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Effects of Material Properties for Non-Equilibrium Conditions in Induction Heating Process

2013· article· en· W2034109451 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

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Rimouski
Fundersnot available
KeywordsMaterial propertiesThermal conductivityMaterials scienceThermodynamic equilibriumInduction heatingThermodynamicsRotational symmetryPermeability (electromagnetism)ThermalThermodynamic processConductivityMeasure (data warehouse)MechanicsComposite materialChemistryPhysicsComputer scienceElectromagnetic coil

Abstract

fetched live from OpenAlex

As the induction heating is very fast, it is reasonable to assume that the material properties are different from those measured under thermodynamic equilibrium conditions. For this reason, this study attempts to measure the effect of material properties variations on the surface temperature using the 2D axisymmetric model. The results show that the relative magnetic permeability is the property that most significantly influences surface temperatures and the hardness profile. The effects of specific heat and electrical conductivity are rather low, while the thermal conductivity has a negligible effect on the model developed. Moreover, the variation ofaustenitizingtemperature of margins has limited effects on the developed model. Therefore, the use of material properties at thermodynamic equilibrium was sufficient to establish models able to predict trends.

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 score0.002
Threshold uncertainty score0.386

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 head0.029
GPT teacher head0.318
Teacher spread0.289 · 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