MétaCan
Menu
Back to cohort
Record W2168740485 · doi:10.1177/1045389x15586449

The effect of magneto-rheological fluid on vibration suppression capability of adaptive sandwich plates: Experimental and finite element analysis

2015· article· en· W2168740485 on OpenAlexaff
Mehdi Eshaghi, Ramin Sedaghati, Subhash Rakheja

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2015
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceRheologyComposite materialMagnetoVibrationFinite element methodPolyethylene terephthalateCore (optical fiber)CantileverStructural engineeringAcousticsMagnetMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This article presents an experimental and theoretical study of vibration analysis of a magneto-rheological fluid-based sandwich plate. Two sandwich plates consisting of polyethylene terephthalate face layers were fabricated with two different magneto-rheological fluids (MRF-132DG and MRF-122EG) as the core layer and silicon rubber spacer as the sealant. The dynamic responses of the cantilever sandwich plate were experimentally characterized. A finite element model based on the Classical Plate Theory was formulated to obtain governing equations of motion of the multi-layer magneto-rheological plate. The complex shear modulus of each magneto-rheological fluid in the pre-yield region was described by a phenomenological model as a function of the magnetic flux density and excitation frequency. The results clearly showed enhanced vibration suppression properties of the magneto-rheological sandwich plate over a broad frequency range through variations in both the stiffness and damping.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

Explore more

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