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Record W3124739325 · doi:10.1115/1.4049822

Analysis of Random Mechanical Vibrations in Symmetrical Thin Plates Using Full-Field Vibration Measurements

2021· article· en· W3124739325 on OpenAlexaff
Alain Le Bot, Olivier Robin, Kévin Rouard, Alain Berry

Bibliographic record

VenueJournal of vibration and acoustics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsVibrationField (mathematics)Random vibrationCoherence (philosophical gambling strategy)Statistical energy analysisFrequency domainAcousticsEnergy (signal processing)PhysicsTime domainOpticsComputational physicsMathematical analysisComputer scienceMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract A successful application of statistical energy analysis for analyzing energy exchanges between weakly coupled subsystems theoretically requires a diffuse vibrational field in all subsystems. So as to verify the conditions of establishment of the diffuse field in practice, full-field vibration measurements were conducted with a high-speed camera on a simply supported rectangular plate excited by a wide band random force. The results constitute an experimental investigation of the diffuse field region in the frequency-structural damping domain and a validation of previously obtained numerical results. The domain of the diffuse field is confined to high frequencies and low damping, with limits than can be easily defined. However, it is shown that the vibrational field is not fully spatially homogeneous due to enhancement of response induced by the effect of coherence of rays. Theoretical values of the enhancement factor obtained using an image source analysis are confirmed by measurement results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.037
GPT teacher head0.264
Teacher spread0.227 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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