MétaCan
Menu
Back to cohort
Record W4285538043 · doi:10.54570/atpet2021/04/01/0016

Determination of Pressure Loss of Silencers Used in Air Conditioning

2021· article· en· W4285538043 on OpenAlexaff
Šimon Kubas, Andrej Kapjor, Martin Vantúch, Juraj Drga

Bibliographic record

VenueAdvances in Thermal Processes and Energy Transformation · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical and Thermal Properties Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSilencerMufflerPressure dropDuct (anatomy)DamperAcousticsDimensioningAir conditioningNoise (video)AttenuationTransmission lossSound pressureEngineeringAnnoyanceComputer scienceMechanical engineeringStructural engineeringInletMechanicsPhysicsOpticsAerospace engineering

Abstract

fetched live from OpenAlex

When designing air conditioning systems, it is necessary to pay attention to the level of noise generated during the operation of such a system. Each of the components of the air conditioning system either absorbs or generates noise. Noise in pipes and fittings can be reduced to the required level by dimensioning the pipes. However, noise generated by the fan itself must be eliminated in another way. To eliminate fan noise, silencers are used in the duct just behind the air handling unit. For the correct design of the silencer, it is necessary to pay attention not only to its acoustic attenuation, but also to the pressure loss. If the pressure drop of the muffler is too high, noise will occur directly in the muffler. The pressure losses of the dampers are determined mainly experimentally. Based on the performed measurement, a CFD model of the selected damper was constructed, where the influence of various parameters on the value of the pressure loss of the selected damper was investigated.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Thermal Processes and Energy TransformationSame topicMechanical and Thermal Properties AnalysisFrench-language works237,207