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Record W4251111481 · doi:10.1121/1.4798987

Design, optimization and testing of door-grille silencers

2013· article· en· W4251111481 on OpenAlexafffund
Vivek Vasudevan Shankar, Murray Hodgson

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsSilencerDoorsAirflowNoise (video)AcousticsFinite element methodTransmission lossVentilation (architecture)SoftwareNoise controlEngineeringComputational fluid dynamicsComputer scienceSound transmission classMarine engineeringMechanical engineeringNoise reductionStructural engineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

It is a common practice to install doors that have openings in them to improve cross airflow through horizontal ventilation.However, excessive outdoor noise and poor noise privacy are known associated issues.Grilles are often installed in these door openings to address this issue.While they may reduce the noise level slightly, they have proven not to be very effective.Effective silencers would be too thick to be installed in doors.This work investigates the design and development of a novel door silencer that reduces the sound transmission to acceptable limits without compromising the airflow.A model of the silencer has been modelled using the Acoustics module of the COMSOL Finite Element software in a diffuse field environment, and validated with STC ratings.The airflow was modeled using the COMSOL CFD module.The dimensions of the ventilation opening and the silencer have been optimized and tested.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.215
Teacher spread0.196 · 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 designBench or experimental
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
Published2013
Admission routes2
Has abstractyes

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