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Record W3216092786 · doi:10.1121/10.0008150

Hybrid lattice-Boltzmann digital-waveguide simulation of wind instruments

2021· article· en· W3216092786 on OpenAlexaff
Song Wang, Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsLattice Boltzmann methodsWaveguideSolverAcousticsPhysicsBoundary value problemHelmholtz resonatorAeroacousticsNonlinear systemResonatorComputational physicsMechanicsOpticsMathematicsMathematical optimizationQuantum mechanics

Abstract

fetched live from OpenAlex

A hybrid lattice-Boltzmann digital-waveguide model is proposed to improve the efficiency of the computational aeroacoustics modeling of wind instruments. The nonlinear sound generator and the assumed linear resonator of a wind instrument are modeled separately using the lattice Boltzmann (LB) method and digital waveguide (DWG) method, correspondingly. Their coupling is achieved through the characteristic boundary condition, which breaks down the lattice Boltzmann solutions at the junction into separated traveling waves along different characteristic lines. The lattice Boltzmann solver then sends the outgoing acoustic wave to the digital waveguide, and receives the incoming acoustic wave to complete the boundary condition. Two examples are tested to demonstrate the validity of the proposed method, including a hybrid cylindrical pipe composed of two coupled cylindrical segments, one represented by LB and the other by DWG, and a hybrid single-reed instrument that comprises an LB mouthpiece-reed system and a DWG pipe.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.255
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

Citations1
Published2021
Admission routes1
Has abstractyes

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