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Experimental Investigation of Squeezed MRF Film Stopper and Its Effect on Vibrating Bimorph for Frequency Tuning of an Energy Generator

2019· article· en· W3010858257 on OpenAlexaff
Sylvester Sedem Djokoto, Martin Agelin‐Chaab, Vytautas Jūrėnas, Egidijus Dragašius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBimorphCantileverMagnetorheological fluidMaterials scienceAcousticsMagnetVibrationMagnetic fieldStiffnessGenerator (circuit theory)Power (physics)PiezoelectricityElectrical engineeringComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper proposed a frequency tuning concept using magnetorheological fluids (MRF) in squeeze mode as a stopper. The MRF is a smart fluid that changes from a liquid state into a semi-solid state within milliseconds when a magnetic field is applied. The change from liquid state to semisolid state demonstrated in this study is achieved by varying the distance between two magnets and hence the magnetic field. The experimental results show that the frequency of the vibrational system was increased by 16% when the MRF was activated at 0.1T. The damping was also increased by 48.8% when a magnetic field of 0.3T was added to MRF. The change in damping also affected the stiffness of the piezoelectric cantilever beam and hence the power generated from the piezoelectric bimorph cantilever. The generated power from the cantilever without MRF damping was calculated to be 1.1 μW at a resistance of 330 kΩ but increased to 9 μW when the damping of 0.1T was applied.

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 score0.345
Threshold uncertainty score0.333

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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