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Record W2046643828 · doi:10.1121/1.3588676

Prediction of sound absorption characteristics of orifice plates with mean flow using the lattice Boltzmann method.

2011· article· en· W2046643828 on OpenAlexaff
Kaveh Habibi, Phoi-Tack Lew, Luc Mongeau

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBody orificeLattice Boltzmann methodsMechanicsMean flowTurbulenceMaterials scienceAcoustic impedanceDuct (anatomy)Computational fluid dynamicsPorous mediumAcousticsPhysicsPorosityMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the sound absorption characteristics of orifice plates in turbulent mean flows using numerical simulations. The production of vorticity at the orifice edge governs the sound absorption phenomena. The LBM-LES methodology was used to perform detailed numerical simulations of the unsteady turbulent field. The lattice Boltzmann method has several advantages over continuum based CFD methods for situations where the configurations of the duct, the orifice, and the flow structures through or in contact with porous/perforated media are geometrically complex. The absorption coefficient and the impedance of the perforated plate were obtained in a virtual three dimensional (3-D) impedance tube apparatus. Both the mean flow velocity profiles and the acoustic characteristics of a simple circular orifice were found to be in good agreement with available experimental data. The model was then exercised to investigate the acoustic properties of orifices with complex shapes over a range of mean flow velocities.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.262
Teacher spread0.221 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicLattice Boltzmann Simulation StudiesFrench-language works237,207