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Record W2563072797 · doi:10.1088/1361-665x/aa5209

Effect of applied magnetic field on sound transmission loss of MR-based sandwich panels

2016· article· en· W2563072797 on OpenAlexaff
Masoud Hemmatian, Ramin Sedaghati

Bibliographic record

VenueSmart Materials and Structures · 2016
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsSound transmission classMagnetorheological fluidSandwich-structured compositeAcousticsSandwich panelNatural frequencyMagnetic fieldMaterials scienceSound powerParametric statisticsRayleigh–Ritz methodCore (optical fiber)Structural engineeringVibrationEngineeringPhysicsComposite materialMathematicsSound (geography)

Abstract

fetched live from OpenAlex

Abstract This study aims to investigate the sound transmission loss (STL) capability of sandwich panels treated with Magnetorheological (MR) fluids at low frequencies. An experimental setup has been designed to investigate the effect of the intensity of applied magnetic field on the natural frequencies and STL of a clamped circular panel. It is shown that the fundamental natural frequency of the MR sandwich panel increases in proportion to the applied magnetic field. In addition, the STL of the panel at the resonance frequency increases as the magnetic field is amplified. Furthermore, the classical plate theory and Ritz method have been utilized to develop the governing equations of motion of the finite multilayered circular panels comprising two elastic face sheets and MR fluid core layer. The radiated sound power from the panel is derived using Rayleigh integral as a function of the transverse velocity of the panel which is subsequently used to evaluate the STL. The theoretical study is validated comparing the simulation results with the experimental measurements. Experimental and analytical parametric study have also been conducted to study the effect of the core layers’ thickness on the natural frequency and the STL of sandwich panel.

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 score0.023
Threshold uncertainty score0.722

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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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