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Record W2897341839 · doi:10.1121/1.5068064

Reactive acoustic liner design

2018· article· en· W2897341839 on OpenAlexaff
Ramani Ramakrishnan, David H. Van Every

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNozzleAcousticsWind tunnelDiffuser (optics)Materials scienceNoise (video)Noise reductionSound powerGas compressorCross section (physics)PhysicsComputer scienceSound (geography)Mechanical engineeringEngineeringMechanicsOptics

Abstract

fetched live from OpenAlex

Acoustic treatment to reduce fan noise levels in a wind tunnel circuit consists of fibrous materials such as fibreglass or rockwool. Open cell foam materials are also used as acoustic treatments. The acoustic treatments are conventionally applied at fan tail cone regions, tunnel walls along fan diffuser section, cross-legs, test section diffuser, and nozzle contraction areas. However, conventional treatments are not possible in cryogenic wind tunnels, since bulk absorber materials with required resistivity, when operating at cryogenic temperatures, are not available. One possible solution is to design reactive silencers tuned to dominant frequencies. One such approach was used as noise control technique so as to satisfy test section noise specifications. The sound power spectrum of the compressor at different speeds were evaluated. The estimated test section sound pressure levels showed noise reduction at two dominant frequencies were required. The acoustic treatment, therefore, resulted in a double layer reactive design tuned to the two dominant frequencies. The design process will be highlighted in the presentation. The final treatment details will also be presented.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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
GenreMethods

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

Citations0
Published2018
Admission routes1
Has abstractyes

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