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Record W2971896079 · doi:10.3397/1/376722

Vibration of and radiated acoustic power from a simply-supported panel excited by a turbulent boundary layer excitation at low Mach number

2019· article· en· W2971896079 on OpenAlexaff
Mansour Jenzri, Olivier Robin, Noureddine Atalia

Bibliographic record

VenueNoise Control Engineering Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMach numberAcousticsBoundary layerSound powerAnechoic chamberVibrationWind tunnelExcitationPhysicsMechanicsSound (geography)

Abstract

fetched live from OpenAlex

The general context of this study is to perform cross-validation of analytical and numerical calculations of vibroacoustic response of panels under a turbulent boundary layer excitation. This article focuses on the specific case of a rectangular aluminum panel with controlled simply-supported boundary conditions, and tested in a lowspeed anechoic wind tunnel (Mach number ≤ 0.12). An underlying goal is to setup a microphone array for directly estimating the radiated sound power from the panel. The vibration of and radiated sound power from the panel are first estimated under a shaker mechanical excitation so as to verify agreement with theoretical calculations and the relevance of the proposed shoebox-shaped microphone array. Similar measurements are then conducted under a turbulent boundary layer excitation. Finally, estimated radiation efficiencies under point mechanical and turbulent excitations are compared. © 2019 Institute of Noise Control 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.206
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 source (direct Gemma or distilled Codex), 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

Citations6
Published2019
Admission routes1
Has abstractyes

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