MétaCan
Menu
Back to cohort
Record W2744248972 · doi:10.1177/1045389x17721058

Frequency response analysis of magneto-rheological clutch designs

2017· article· en· W2744248972 on OpenAlexaff
Peyman Yadmellat, Rajni V. Patel

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2017
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsWestern University
Fundersnot available
KeywordsClutchFinite element methodElectromagnetic coilEddy currentBandwidth (computing)TorqueFrequency responseSkin effectRheologyAcousticsEngineeringMaterials scienceMechanical engineeringMechanicsPhysicsStructural engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This article presents the frequency response analysis performed on two different geometrical configurations of magneto-rheological clutches including disk-based and drum-based configurations. For each configuration, two sub-configurations with inner coil and outer coil designs are studied. The configurations are thoroughly studied with respect to the effect of design parameters on the bandwidth of magneto-rheological clutches using finite element analysis and by considering the effect of eddy currents and the skin effect in the magnetic circuit. The N-way analysis of variance is used to study the effect of the main parameters as well as interaction between the parameters on the bandwidth. Furthermore, trade-offs between the effect of the main parameters and the torque capacity are discussed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.261
Teacher spread0.235 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intelligent Material Systems and StructuresSame topicVibration Control and Rheological FluidsFrench-language works237,207