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Record W4206587681 · doi:10.18280/i2m.200604

Anhysteretic Magnetization Effect on the Centered and Non-Centered Minor Hysteresis Loops in Jiles-Atherton Model

2021· article· en· W4206587681 on OpenAlexvenueno aff
Bachir Selmoune, M. Hamimid

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMinor (academic)MagnetizationHysteresisMagnetic hysteresisCondensed matter physicsExcitationPhysicsSaturation (graph theory)Magnetic fieldNuclear magnetic resonanceMathematicsQuantum mechanicsLaw

Abstract

fetched live from OpenAlex

In this paper, an accurate evaluation of minor hysteresis loops using the modified Jiles-Atherton model is presented. This model is based on the anhysteretic magnetization, which is given in most cases by the Langevin equation. The anhysteretic magnetization is characterized by three parameters, the mean field parameter α, the shape parameter of anhysteretic magnetization curve a and the saturation magnetization Ms. The parameters influencing the minor hysteresis loops are a and α. These parameters are expressed usually in the form of simple power laws and they connect the minor loop parameters to the major ones. These expressions are applicable in both centered and non-centered minor loops cases. In the centered minor loops, the parameter k is introduced in order to adjust the width of the minor loops regarding the level of the magnetic excitation. The coefficients of the proposed expressions (γ, β and σ) are obtained by optimization procedure. The proposed approach is validated using measured minor loops in both cases. A close agreement is obtained between modeled and measured ones.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.263
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

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 designSimulation or modeling
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

Citations1
Published2021
Admission routes1
Has abstractyes

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