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Record W2994556161

Experimental Validation Of An Acceleration Power Spectral Density Aircraft Panel Model Given Different Excitations

2018· article· en· W2994556161 on OpenAlexaff
Steven Albert James Sonnenberg, Joana Rocha, Malte Misol, Michael Rose

Bibliographic record

Venueelib (German Aerospace Center) · 2018
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsCarleton University
FundersDeutsches Zentrum für Luft- und Raumfahrt
KeywordsFuselageSpectral densityAcousticsNoise (video)AccelerationStatistical energy analysisVibrationExcitationBoundary layerAeroacousticsPhysicsEngineeringMechanicsStructural engineeringComputer scienceSound pressureClassical mechanicsElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The noise and vibration in an aircraft cabin during cruise conditions is primarily caused by external flow excitations from the turbulent boundary layer (TBL). The TBL causes the fuselage panels on the aircraft to vibrate. These vibrations radiate sound energy in the form of noise. It is of interest to be able to predict the response of these panels to different excitations using an analytical model, so that expensive wind tunnel and flight tests can be minimized when doing noise research. Two existing analytical models were modified to account for different excitations: one with simply supported boundary conditions and the other with arbitrary boundary conditions. These models were programmed, using Matlab, to calculate the acceleration power spectral density (PSD), and radiated sound power (RSP). Models were validated against experimental data, obtained by the authors, for a thin rectangular panel with boundary conditions between simply supported and clamped conditions. It is shown that the modified analytical models accurately predict the panel response for a point force excitation, for a TBL excitation, and for an oscillating piezoelectric patch excitation.

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 score0.337
Threshold uncertainty score0.892

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 head0.033
GPT teacher head0.290
Teacher spread0.258 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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