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Record W1587498870 · doi:10.1115/gt2015-43737

Aeroacoustic Analysis of a Low-Subsonic Axial Fan

2015· article· en· W1587498870 on OpenAlexaff
Marlène Sanjosé, Dominic Lallier-Daniels, Stéphane Moreau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsWakeAcousticsNoise (video)Rotor (electric)Sound pressureAeroacousticsPhysicsMechanicsConvergence (economics)Computer science

Abstract

fetched live from OpenAlex

A mesh convergence study of direct aero-acoustic simulations of a typical automotive engine cooling rotor is performed. The simulations are performed using the Lattice Boltzmann Method (LBM) that has been extensively used in the recent years for low speed fan applications. The influence of the mesh refinement on the global performances, the pressure distribution over the blade and in the wake and tip gap zones is investigated using the large experimental database available. The direct acoustic predictions for the different numerical setups is compared with acoustic measurements and a Ffowcs Williams and Hawking’s analogy is applied to identify the noise source contributions from the rotor parts. With the three setups investigated the convergence is achieved on the global performances and the sound power spectra. Still some discrepancies between the setups appear in the unsteady pressure loading of the blades and the wake flow. The blades are seen as the main noise contributors particularly the cusp region near the hub and the tip region with the tip flow recirculation.

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 score0.278
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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