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Record W2147239442 · doi:10.2514/6.2011-2848

Statistical Properties of Pressure Loadings and Vibroacoustic Response of a Simplified Side Glass Induced by the Flow over Generic Flow-deflector and Side-mirror

2011· article· en· W2147239442 on OpenAlexaff
Stéphane Moreau, Venkata Phani Kiran Vadavalli, Noureddine Atalla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
FundersPurdue University
KeywordsFlow (mathematics)Materials scienceAcousticsMechanicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

The statistical description of the wall pressure fluctuations in a non-equilibrium turbulent boundary layer (TBL) on a plate with two typical bluff bodies a side mirror and a flat fence is presented. To achieve statistically converged turbulent flow, a long time history computational fluid dynamics (CFD) simulation was conducted with the Lattice-Boltzmann solver PowerFLOW. It provided the unsteady wall pressure on the plate. Mean flow, wall pressure spectra and two point correlations of the wall-pressure results are validated for both cases with experiments run in the anechoic wind tunnel at Purdue University. From a spatial temporal analysis of the turbulent flow the convection and acoustic zones are captured correctly, which are major sources of excitation and transmission respectively. Using the CFD wall-pressure coherence on the plate, zonal Corcos’ model parameters like convection velocity and decay coefficients have been determined by least-square curve fitting. The identified empirical model parameters are validated with experimental values and later used to predict the vibroacoustic indicators for the plate. Vibroacoustic indicators for a side mirror case are calculated using both CFD-derived parameters and finite element method. These results are validated with experiments on a plate-cavity system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.590

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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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