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Record W2331235100 · doi:10.2514/6.2014-3313

Aeroacoustic study of Internal Mixing Nozzles with Forced Lobed Mixers using a High-Mach Subsonic Lattice Boltzmann Scheme

2014· article· en· W2331235100 on OpenAlexaff
Kaveh Habibi, Luc Mongeau, Damiano Casalino, Phoi-Tack Lew

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsMach numberLattice Boltzmann methodsNozzleMixing (physics)MechanicsPhysicsAcousticsAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Numerical simulation of the transient compressible flow through lobed mixers inside turbofan engine nozzles was performed. A novel entropic Lattice Boltzmann (LB) scheme was used for the computational studies. The very large eddy simulation scheme (LBMVLES) was used to capture sub-grid scale flow structures. The sound generation process was investigated using the porous Ffwocs William-Hawkings (FW-H) surface integral method. A contoured dual-stream nozzle with a standard 12-lobed mixer (12CL) was selected as a test case to validate the LBM simulation. A detailed model of the mixer and the nozzle was concurrently created to simulate both the internal flow through the mixer and the external jet plume. All operating boundary conditions were imposed based on available measured data for the same mixer geometry. The transient and mean flow characteristics of the flow field were obtained both inside the nozzle and within the jet plume. The transient behavior of the streamwise vortices at the nozzle exit was visualized and quantified. The Reynolds number based on nozzle exit diameter was 2.02×10 6 and the acoustic Mach number reached 0.94. Both near field and far field results and trends were in good agreement with experimental data. Specifically the predicted averaged velocity field was within the range of measure values. The overall radiated sound pressure was within 2-3dB of measured sound at all directional angles.

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 score0.282
Threshold uncertainty score0.970

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 head0.009
GPT teacher head0.215
Teacher spread0.207 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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