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Record W2558083176 · doi:10.1115/smasis2016-9059

Sound Transmission Loss of Adaptive Sandwich Panels Treated With MR Fluid Core Layer

2016· article· en· W2558083176 on OpenAlexaff
Masoud Hemmatian, Ramin Sedaghati

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceMagnetorheological fluidCore (optical fiber)Sound transmission classSandwich panelMagnetic fieldViscoelasticityAcousticsNatural frequencyComposite materialShear modulusLayer (electronics)Parametric statisticsTransmission lossStructural engineeringVibrationPhysicsEngineering

Abstract

fetched live from OpenAlex

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 the applied magnetic field on the natural frequencies and STL of a clamped circular plate. A multilayered uniform circular panel comprising two elastic face sheets and MR fluid core layer is fabricated. It is shown that as the applied magnetic field increases, the fundamental natural frequency of the MR sandwich panel increases. Moreover, the STL of the panel at the resonance frequency considerably increases under applied magnetic field. Furthermore, an analytical model for the STL of the finite multilayered panels with MR core layer is developed and compared with the experimental measurements. The MR core layer is treated as a viscoelastic material with complex shear modulus. It is shown that good agreement exists between the analytical and experimental results. Parametric study has also been conducted to investigate the effect of face sheets and core layers’ thickness.

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 score0.445
Threshold uncertainty score0.654

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 head0.020
GPT teacher head0.218
Teacher spread0.199 · 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
Published2016
Admission routes1
Has abstractyes

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