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Record W3046558244 · doi:10.1109/tmag.2020.3013147

Optimization of Magnetoelastic Torquemeter Designs

2020· article· en· W3046558244 on OpenAlexafffund
Xavier Tousignant, David Ménard

Bibliographic record

VenueIEEE Transactions on Magnetics · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsPolytechnique Montréal
FundersMitacs
KeywordsMagnetometerOffset (computer science)PhysicsMagnetic fieldMagnetizationMagnetostrictionIsotropyFerromagnetismWaveformNuclear magnetic resonanceSIGNAL (programming language)RADIUSElectromagnetMagnetCondensed matter physicsOpticsComputer scienceVoltage

Abstract

fetched live from OpenAlex

The performance of magnetoelastic torquemeters based on the changes in a measurable magnetic field is obtained with a simplified numerical model, assuming isotropic magnetoelastic properties throughout the ferromagnetic shaft and independent magnetic cells. The output gain of two torquemeter designs is obtained as a function of the distance of the magnetometers and width of the magnetic bands. Results show that a radial sensor configuration gives a gain 1.76 times larger in average than an axial sensor configuration. It is also shown that the radial configuration is more sensitive to magnetometer position errors and requires a tighter control over the device parameters to maintain a constant gain. Furthermore, computations reveal that fluctuations in the magnetization of the shaft decrease the gain and increase the signal offset and that the shaft radius can alter this dependence due to an enhanced magnetoelastic response.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.993

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)0.0080.000

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 head0.230
Teacher spread0.189 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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

Citations8
Published2020
Admission routes2
Has abstractyes

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