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Record W2997389066 · doi:10.1299/jsmeenv.2019.29.j117

Study on acoustic and flow induced noise characteristics of L-shaped duct with a shallow cavity

2019· article· en· W2997389066 on OpenAlexaff
Masaaki Mori, Kunihiko ISHIHARA

Bibliographic record

VenueThe Proceedings of the Symposium on Environmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsDuct (anatomy)AcousticsPipingAerodynamicsInflowInletGeologyMaterials sciencePhysicsEngineeringMechanicsMechanical engineeringGeomorphologyAnatomy

Abstract

fetched live from OpenAlex

An aerodynamic sound generated by a flow inside a duct is one of the noise problems. Flows in ducts with uneven surfaces such as grooves or cavities can be seen in various industrial devices and industrial products such as air conditioning equipment in various plants or piping products. In this paper, we have performed experiments and simulations to clarify acoustic and flow induced sound characteristics of L - shaped duct with a shallow cavity installed. The experiments and simulations were performed under several inflow velocity conditions. The results show that the characteristics of the flow induced sound in the duct are strongly affected by the acoustic characteristics of the duct interior sound field and the location of the shallow cavity. Especially, it was found that the acoustic characteristics were affected by the location of the shallow cavity at high frequencies above 1000 Hz.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.163
Teacher spread0.158 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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