MétaCan
Menu
Back to cohort
Record W2024264144 · doi:10.1121/1.4803850

Unsteady numerical simulation of a round jet with impinging microjets for noise suppression

2013· article· en· W2024264144 on OpenAlexafffund
Phoi-Tack Lew, Alireza Najafi­-Yazdi, Luc Mongeau

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersU.S. Public Health ServiceNational Institutes of HealthCompute CanadaPratt and Whitney Canada
KeywordsJet (fluid)Mach numberTurbulence kinetic energyLarge eddy simulationSound pressureAeroacousticsAcousticsTurbulenceLattice Boltzmann methodsJet noiseMechanicsSound powerPhysicsNoise (video)Computational fluid dynamicsComputer simulationComputational physicsSound (geography)Computer science

Abstract

fetched live from OpenAlex

The objective of this study was to determine the feasibility of a lattice-Boltzmann method (LBM)-Large Eddy Simulation methodology for the prediction of sound radiation from a round jet-microjet combination. The distinct advantage of LBM over traditional computational fluid dynamics methods is its ease of handling problems with complex geometries. Numerical simulations of an isothermal Mach 0.5, Re(D) = 1 × 10(5) circular jet (D(j) = 0.0508 m) with and without the presence of 18 microjets (D(mj) = 1 mm) were performed. The presence of microjets resulted in a decrease in the axial turbulence intensity and turbulent kinetic energy. The associated decrease in radiated sound pressure level was around 1 dB. The far-field sound was computed using the porous Ffowcs Williams-Hawkings surface integral acoustic method. The trend obtained is in qualitative agreement with experimental observations. The results of this study support the accuracy of LBM based numerical simulations for predictions of the effects of noise suppression devices on the radiated sound power.

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: none
Teacher disagreement score0.791
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207