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Record W4399271208 · doi:10.2514/6.2024-3094

Large-Eddy Simulation and Broadband Acoustic Prediction of a Helicopter Rotor in Forward Flight

2024· article· en· W4399271208 on OpenAlexaff
Stéphane Moreau, Marlène Sanjosé, Régis Koch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsÉcole de Technologie SupérieureUniversité de Sherbrooke
Fundersnot available
KeywordsBroadbandAcousticsAerospace engineeringRotor (electric)Large eddy simulationAerodynamicsComputer sciencePhysicsEngineeringMeteorologyTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

A compressible large-eddy simulation (LES) is performed on an helicopter rotor in forward flight in order to compare with wind-tunnel measurements and previous similar simulations in hover. The rotor is the 1/4th scale UH-1 helicopter rotor composed of two blades with a Reynolds number based on chord and tangential tip velocity of 2.27 × 106 and a tip tangential mach number of 0.73. At the inlet, both an advancing and descending velocities are imposed to mimic approach. The resulting flow field is first obtained by an unsteady Reynolds-Averaged Navier-Stokes simulation that provides an initialization to the LES. Both simulation results are then coupled with a FfowcsWilliams & Hawkings analogy to compute the noise in the far field. Combined approaches show a very good agreement with experiment over the full audible frequency range. The low to mid-frequency range is dominated by the Blade-Vortex Interaction that is much more parallel in the present case. The high frequency range is mostly caused by broadband turbulence-interaction noise.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.298

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

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