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Record W4389273272 · doi:10.3397/in_2023_0302

Producing a High Level Harmonic Acoustic Pressure Field with Harmonic Acoustic Pneumatic Source (HAPS): Experimental Validation of the Harmonic Distortion Reduction

2023· article· en· W4389273272 on OpenAlexaff
Pierre Grandjean, Philippe Micheau, Pierre-Olivier Lajoie

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsHarmonicsHarmonicSound pressureAnechoic chamberDistortion (music)Total harmonic distortionNoise (video)Phase distortionPhysicsElectrical engineeringPhase (matter)EngineeringVoltageComputer scienceAmplifier

Abstract

fetched live from OpenAlex

Applications such as active control of fane noise, require high Sound Pressure Levels (SPL). Pneumatic acoustic sources are usually used for that purpose (e.g. the WAS 3000 and the Mark VI and VII). The harmonic acoustic pneumatic sources (HAPS) - which use the chopping of an airflow to generate an acoustic pressure field - are being studied for applications requiring high-level harmonic acoustic pressure fields. A recent study has shown that the amplitude, phase, and frequency generated by HAPS are accurately controllable, and can reach 120 dBSPL. However, higher harmonics are also generated at high SPL and this could be detrimental to the intended applications (e.g. active control of tonal noise). An experimental campaign dedicated to understanding the harmonic distortion of HAPS has been conducted on a single HAPS in a semi-anechoic chamber. Measurements with quarter-wave tube at the output of the HAPS were also carried out to reduce this distortion. Levels of 120 to 130 dBSPL for the fundamental were reached, with a difference of 10 dB between the fundamental and the second harmonic (H1/H2 distorsion). Adding a quarter-wave tube increased the acoustic pressure level from 4 to 6 dBSPL, and the H1/H2 distorsion from 2 to 8 dBSPL.

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 categoriesMeta-epidemiology (narrow)
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 score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNOISE-CON proceedingsSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207