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Record W2901807812 · doi:10.25071/10315/35394

Modeling Of Magneto-Mechanical Response Of Magnetorhological Elastomers Having Different Arrangement Of Magnetic Particles

2018· article· en· W2901807812 on OpenAlexaff
Mostafa Asadi Khanouki, Ramin Sedaghati

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsMagnetoElastomerMaterials scienceBiomagnetismMagnetic nanoparticlesCondensed matter physicsMagnetic fieldComposite materialPhysicsMechanical engineeringMagnetEngineeringNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

A model describing the magneto-mechanical properties of magnetorheological elastomers (MREs) under an external magnetic field and a mechanical shear deformation is presented.The main purpose of the present study is to demonstrate the effect of particle distribution and applied magnetic field on the MRE mechanical properties.Four types of rectangular lattice models are considered as the representation of spatial distribution of magnetic particles in the matrix.Using the energy method, shear modulus is obtained and numerically calculated as function of the strength of the external magnetic field and the shear strain.The results show a high sensitivity of shear modulus on the spatial distribution of particles.Depending on the lattice type, shear modulus exhibits an increasing or decreasing behavior with the increase of magnetic field intensity.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.216
Teacher spread0.205 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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