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Record W2044607362 · doi:10.1121/1.4786618

Benchmarking the lattice Boltzmann method for the determination of acoustic impedances of axisymmetric waveguides

2006· article· en· W2044607362 on OpenAlexaff
Andrey R. da Silva, Philippe Depalle, Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsRotational symmetryMach numberLattice Boltzmann methodsAcoustic radiationAcoustic impedanceFinite element methodBenchmarkingAcousticsElectrical impedanceRadiation impedancePhysicsMechanicsComputational physicsComputer scienceMathematical analysisMathematicsRadiationOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

A numerical technique for determining the acoustic impedance of axisymmetric waveguides based on the lattice Boltzmann method (LBM) is proposed here. This approach presents some desirable characteristics, namely, the possibility of explicitly considering the propagation of waves at low Mach number conditions with a relative low computational cost when compared to traditional continuum methods. The validation of the method is achieved by simulating the radiation of an open unflanged pipe using LBM and comparing the results with those obtained through a finite-element model and through the analytic solution derived by Levine and Schwinger. Finally, the results and the discussion of the method in terms of its computational efficiency and applicability to the simulation of wind instruments at more realistic conditions are presented. [The first author was supported by CAPES (Brazil).]

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.291
Teacher spread0.274 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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