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Record W4235135930 · doi:10.1155/2012/153204

Prediction of Turbulent Boundary Layer Induced Noise in the Cabin of a BWB Aircraft

2012· article· en· W4235135930 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueShock and Vibration · 2012
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Victoria
FundersFundação para a Ciência e a Tecnologia
KeywordsBoundary layerTurbulenceNoise (video)Range (aeronautics)AcousticsAircraft noiseSound pressureVolume (thermodynamics)Flow (mathematics)Boundary (topology)Aerospace engineeringEngineeringMechanicsPhysicsComputer scienceMathematicsNoise reduction

Abstract

fetched live from OpenAlex

This paper discusses the development of analytical models for the prediction of aircraft cabin noise induced by the external turbulent boundary layer (TBL). While, in previous works, the contribution of an individual panel to the cabin interior noise was considered, here, the simultaneous contribution of multiple flow-excited panels is analyzed. Analytical predictions are presented for the interior sound pressure level (SPL) at different locations inside the cabin of a Blended Wing Body (BWB) aircraft, for the frequency range 0–1000 Hz. The results show that the number of vibrating panels significantly affects the interior noise levels. It is shown that the average SPL, over the cabin volume, increases with the number of vibrating panels. Additionally, the model is able to predict local SPL values, at specific locations in the cabin, which are also affected with by number of vibrating panels, and are different from the average values.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.153

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 head0.018
GPT teacher head0.221
Teacher spread0.203 · 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