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Record W4389541074 · doi:10.17118/11143/20856

Aeroacoustic investigation of a ducted tail rotor using theLattice-Boltzmann Method

2023· article· en· W4389541074 on OpenAlexfundno aff
José Arredondo, Prateek Jaiswal, Marlène Sanjosé, Yann Pasco, Stéphane Moreau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsLattice Boltzmann methodsRotor (electric)PhysicsComputer scienceAcousticsAerospace engineeringMechanicsEngineering

Abstract

fetched live from OpenAlex

The present study focuses on the aeroacoustics aspects of a ducted rotor that could be used in helicopter electrically driven tail rotor systems.These systems are axial, low-solidity rotor-stator assemblies operating in low subsonic and transitional regimes at approach.Lattice Boltzmann (LBM) simulations are performed using a variable resolution (VR) mesh strategy that yields a proper convergence after ∼ 0.35 seconds.The flow behavior, far-field acoustic predictions, and directivity measurements are obtained with the LBM simulations and compared with experiments and RANS simulations done to the same tail rotor system.LBM gives closer predictions to experimentally aerodynamic properties than the RANS simulations.In addition, the direct far-field noise computations show a good overall match 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.266
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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