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Record W1997606224 · doi:10.1115/imece2014-36785

A Hybrid Model for Characterizing Pre-Yield Properties of MR Fluids

2014· article· en· W1997606224 on OpenAlexaff
Mehdi Eshaghi, Ramin Sedaghati, S. Rakheja

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsMagnetorheological fluidMagnetic fieldFinite element methodMaterials scienceExcitationMagnetShear modulusVibrationMechanicsStiffnessBeam (structure)Timoshenko beam theoryAcousticsPhysicsComposite materialMechanical engineeringStructural engineeringEngineeringOptics

Abstract

fetched live from OpenAlex

This study aims to present a field dependent phenomenological model to characterize the Magneto-Rheological (MR) fluid in the pre-yield region under varying frequency and applied magnetic field. Systematic analytical and experimental studies are proposed to formulate a hybrid model for representing complex shear modulus of a typical MR fluid (MR 122EG from Lord Corporation) as a function of both applied magnetic field and frequency. Two fully treated MR based sandwich beams with aluminum and copper face layers and MR fluid as the core layer are designed and fabricated. Uniform magnetic flux across the sandwich beam is provided using two permanent magnets. The fabricated MR based sandwich beams are then tested on an electrodynamic shaker under sweep sine excitation and different applied magnetic field to realize the effect of external excitation frequency and applied magnetic field on the stiffness and damping properties of the structure. The finite element model based on classical plate theory is also developed to analyze vibration response of the designed MR based sandwich beams incorporated with MR fluid. Then, by correlating the finite element results with those of the experiment, the frequency and field dependent complex shear modulus of the MR fluid is identified.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.220

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 head0.024
GPT teacher head0.199
Teacher spread0.175 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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