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Record W4403266474 · doi:10.3397/in_2024_3690

Pros and cons of equipping Harmonic Acoustic Pneumatic Sources (HAPS) with quarter wave tube: analytical model versus experimental validation

2024· article· en· W4403266474 on OpenAlexaffabout
Pierre Grandjean, Alexandre Schiavini, Philippe Micheau

Bibliographic record

VenueNOISE-CON proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsQuarter (Canadian coin)Tube (container)consHarmonicComputer scienceEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Pneumatic acoustic sources enable high Sound Pressure Levels (SPL). Harmonic Acoustic Pneumatic Sources (HAPS), utilizing airflow modulation to create precise acoustic pressure fields with controlled amplitude and phase, emerge as solutions for active harmonic-noise control at high SPLs. While theoretically capable of generating pure tones, HAPS appear to be non-linear sources with significant harmonic distortion in practice. Additionally, the mass airflow consumption of this source could be a drawback in certain applications. The aims of this study is to evaluate the advantages of a Quarter-Wave Tube (QWT) located at the HAPS output. A numerical analytical linear model has been developed to compute the radiated acoustic pressure and consumed mass air flow, according to the QWT and HAPS characteristics. For the cases of HAPS equipped with various QWT, this presentation will compare experimental results with the analytical model. This aims to highlight the advantages, including increased SPL of the radiated fundamental, reduced fundamental/harmonic distortion over a wide frequency range, and decreased airflow consumption. The disadvantage is related to the space required by the QWT because its diameters must be greater than the HAPS output. Whatever, this study demonstrate that HAPS can be improved when equipped with a dedicated exhaust.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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
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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNOISE-CON proceedingsSame topicHydraulic and Pneumatic SystemsFrench-language works237,207