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Record W2063636742 · doi:10.2514/6.2013-2027

Experimental reproduction of random pressure fields for vibroacoustic testing of plane panels

2013· article· en· W2063636742 on OpenAlexaff
Olivier Robin, Alain Berry, Stéphane Moreau, Simon Campeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFuselageAcousticsSupersonic speedVibrationComputer sciencePhysicsMechanicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The feasability of the experimental reproduction of random pressure fields on a plane panel and corresponding induced vibrations is studied. The random pressure fields to be reproduced, a Diffuse Acoustic Field (DAF) and a Turbulent Boundary Layer (TBL), are described using their Cross-Spectral Densities (CSD). We propose an open-loop reproduction strategy that uses the synthetic array concept, for which a small array element is moved to create a large array by post-processing. Three possible approaches are suggested to define the complex amplitudes to be imposed at the post-processing step to all the virtual reproduction sources distributed on a virtual plane, the synthetic array, facing the panel to be tested. With a setup using a single acoustic monopole, a scanning laser vibrometer and a baffled simply supported aluminum panel, we obtain experimental vibroacoustic indicators such as the Transmission Loss (TL) for DAF, subsonic and supersonic TBL excitations. The assets or weaknesses inherent to each method are discussed, in terms of their aptitudes for reproducing the target pressure field for a given array geometry. The experimental TL results are compared to simulation results obtained using a commercial software. Most of the comparisons show that the three approaches are suitable for DAF and TBL wall pressure fluctuations reproduction, and thus should open perspectives for the experimental vibroacoustic testing of fuselage panels.

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 score0.281
Threshold uncertainty score0.373

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)0.0000.000

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 head0.031
GPT teacher head0.256
Teacher spread0.224 · 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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