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Record W4229454438 · doi:10.3390/fluids7050161

Advances in the Prediction of the Statistical Properties of Wall-Pressure Fluctuations under Turbulent Boundary Layers

2022· article· en· W4229454438 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

VenueFluids · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
FundersCentre Lyonnais d'Acoustique, Université de LyonAgence Nationale de la Recherche
KeywordsBoundary layerTurbulenceMechanicsMach numberCompressibilityAirfoilPhysicsBoundary layer thicknessSpectral densityCompressible flowStatistical physicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Analytical or empirical models of the wall-pressure power spectral density under a turbulent boundary layer are often validated on test cases in an incompressible flow regime. In this work, an analytical model based on the compressible Poisson equation for the unsteady pressure in a turbulent boundary layer is developed. The Large Eddy Simulation of the flow over a controlled-diffusion airfoil at Mach 0.5 is used to validate the assumptions made on the statistical properties of the boundary layer turbulence and to validate the prediction of the statistics of the wall-pressure fluctuations. The predicted wall-pressure spectrum also compares favorably with experimental data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.139

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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