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Record W1981942327 · doi:10.1121/1.4805128

Design, optimization, and testing of door-grille silencers

2013· article· en· W1981942327 on OpenAlexaff
Vivek Vasudevan Shankar, Murray Hodgson

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDoorsSilencerAirflowAcousticsNoise (video)Computer scienceTransmission lossComputational fluid dynamicsFinite element methodSoftwareVentilation (architecture)Marine engineeringEnvironmental scienceSimulationAutomotive engineeringMechanical engineeringEngineeringTelecommunicationsStructural 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 designed and tested—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 its position in the door have also been optimized. A real prototype of the model has then been built and its performance tested. Various design guidelines have then been proposed for the design of these doors.

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 score0.676
Threshold uncertainty score0.223

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 science0.0000.000
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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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
Published2013
Admission routes1
Has abstractyes

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