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Record W2062190837 · doi:10.1063/1.4831666

Modeling a Galfenol based stress sensor capable of sensing up to three axial stresses

2013· article· en· W2062190837 on OpenAlexafffund
P. Weetman, George Akhras

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsRoyal Military College of Canada
FundersMinistère de la Défense Nationale
KeywordsMagnetizationMaterials scienceInverse magnetostrictive effectStress (linguistics)MagnetostrictionShear stressCondensed matter physicsMechanicsMagnetic fieldPhysicsComposite material

Abstract

fetched live from OpenAlex

A three dimensional rate equation model can be used to calculate the magnetization response in a Galfenol sample under the application of any or all components of stress (axial and shear) [P. Weetman and G. Akhras, J. Appl. Phys. 109, 043902 (2011)]. For a Galfenol based stress sensor, one is essentially interested in the inverse of that calculation: from magnetization measurements, determine which stresses are acting on the system. A conceptual design of a Galfenol based three dimensional dynamical sensor is presented. One assumes the time-varying magnetization and its time derivative in all three directions can be measured for different external magnetic bias fields at different points in time. It is shown that the rate equation model can be used to calculate all the stresses acting on the system from knowledge of the magnetization and the time derivative of magnetization. The necessary calculations are presented and then applied to a sample set of magnetization values, which were generated from a benchmarked sensing model that used up to three axial stresses.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.393

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 head0.025
GPT teacher head0.231
Teacher spread0.206 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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