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A Concept of a Miniaturized MR Clutch Utilizing MR Fluid in Squeeze Mode

2020· article· en· W3129468722 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsWestern University
Fundersnot available
KeywordsClutchArmature (electrical engineering)TorqueMultiphysicsTorque converterFinite element methodMagnetic gearMechanical engineeringComputer scienceMagnetorheological fluidEngineeringStructural engineeringMagnetPhysics

Abstract

fetched live from OpenAlex

This paper presents a novel design concept of a miniaturized Magneto-Rheological (MR) clutch. The design uses a set of spur gears as a means to control the torque. MR clutches with various configurations such as disk-, drum-, and armature-based have in the past been reported in the literature. However, to the best of our knowledge, the design of a clutch with spur gears to use MR fluid in squeeze mode is a novel concept that has never been reported previously.After a brief description of the MR clutch principles, the details of the mechanical design of the spur gear MR clutch are discussed. The distribution of the magnetic flux inside the MR clutch is studied using finite element analysis in COMSOL Multiphysics software. Preliminary experimental results using a prototype MR clutch that validates the new concept and the results therein will be presented next. To clearly show the performance of the proposed design, we compared the torque capacity of our MR clutch obtained experimentally with that of a simulated disk-type MR clutch of a similar size.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.583

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)0.0010.000

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 head0.018
GPT teacher head0.229
Teacher spread0.212 · 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

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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