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Record W1983093259 · doi:10.1121/1.428267

Hybrid tool for quickly estimating the radiated acoustic power from a vibrating structure in a multiple-source environment

2000· article· en· W1983093259 on OpenAlexafffund
Olivier Beslin, Olivier Foin, Jean Nicolas

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2000
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsAcousticsSound powerComputer scienceCurvaturePower (physics)VibrationField (mathematics)Effective radiated powerSound pressurePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper presents a new hybrid method for predicting overestimating and underestimating indicators of the acoustic power radiated by a vibrating surface even in the presence of other surrounding acoustic sources. This method is applicable to plates or low curvature surfaces radiating in open acoustic fields. The method is hybrid in the sense that the vibration field is measured and the parietal pressure field is predicted considering two extreme academic cases "baffled" and "unbaffled." Many simplifications are made and justified in order to save running time. The method is successfully validated in comparison with experimental results on both laboratory and real life structures. This method has led to a quick tool, allowing one to obtain a good approximation of the radiated power in a few minutes. It provides a natural extension of a classical analyzer for vibroacoustics engineering.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.506
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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 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

Citations0
Published2000
Admission routes2
Has abstractyes

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