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Record W2053291557 · doi:10.1121/1.4778748

Method of designing layered sound absorbing materials

2002· article· en· W2053291557 on OpenAlexaffabout
Youssef Atalla, Raymond Panneton

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTortuosityRigid frameMaterials sciencePorous mediumPorosityAbsorption (acoustics)Acoustic impedanceAcousticsNoise reduction coefficientElectrical impedanceFrame (networking)Computer scienceComposite materialPhysicsUltrasonic sensor

Abstract

fetched live from OpenAlex

A widely used model for describing sound propagation in porous materials is the Johnson–Champoux–Allard model. This rigid frame model is based on five geometrical properties of the porous medium: resistivity, porosity, tortuosity, and viscous and thermal characteristic lengths. Using this model and with the knowledge of such properties for different absorbing materials, the design of a multiple layered system can be optimized efficiently and rapidly. The overall impedance of the layered systems can be calculated by the repeated application of single layer impedance equation. The knowledge of the properties of the materials involved in the layered system and their physical meaning, allows to perform by computer a systematic evaluation of potential layer combinations rather than do it experimentally which is time consuming and always not efficient. The final design of layered materials can then be confirmed by suitable measurements. A method of designing the overall acoustic absorption of multiple layered porous materials is presented. Some aspects based on the material properties, for designing a flat layered absorbing system are considered. Good agreement between measured and computed sound absorption coefficients has been obtained for the studied configurations. [Work supported by N.S.E.R.C. Canada, F.C.A.R. Quebec, and Bombardier Aerospace.]

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score0.416

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.279
Teacher spread0.245 · 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
Published2002
Admission routes2
Has abstractyes

Explore more

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