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Record W2146401910 · doi:10.5539/jmsr.v2n4p64

Vibro-Acoustic Characterization and Optimization of Periodic Cellular Material Structures (PCMS) for NVH Applications

2013· article· en· W2146401910 on OpenAlexvenueno aff
Mohammad Al Zubi, Emmanuel Ayorinde, Mehmet Akif Dundar, Manjinder Warriach, Y. V. Murty

Bibliographic record

VenueJournal of Materials Science Research · 2013
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsnot available
Fundersnot available
KeywordsNoise, vibration, and harshnessHarshnessVibrationAcousticsMaterials scienceAutomotive industryNoise (video)Characterization (materials science)Structural engineeringComputer scienceMechanical engineeringAerospace engineeringEngineeringNanotechnologyPhysics

Abstract

fetched live from OpenAlex

NVH (Noise, vibration, and harshness) performance has been identified as having a significant influence in the purchase considerations of most automobile purchasers. Periodic cellular material structures (PCMS) are recently introduced multi-functional structures, commonly in sandwich form, that facilitate a wide variety of engineering purposes. Although literature concerning many PCMS properties is abundant, information about their vibration and acoustic responses is scanty, to the best knowledge of the present authors. This article documents a basic investigation of the vibration and acoustic behaviors of some PCMS through practical, analytical and numerical approaches, in order to evaluate the possibilities for minimizing, the transmission of noise and vibration. Some of these materials were made and investigated over frequency ranges which include several commonly-encountered vibration and acoustic frequencies of automotive and some other structures. Observations from this work are therefore expected to contribute towards design inputs to obtain better performances. A novel investigation of the effects upon vibration response of even slight inaccuracies of cutting out samples from larger blanks of such materials has also been made.

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)0.001
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 score0.119
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.278
Teacher spread0.263 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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