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Record W4320182854 · doi:10.3397/in_2022_0416

Active Sound Power Attenuation with a Ring of Harmonic Acoustic Pneumatic Sources fOr Destructive Interference (RHAPSODI) and near field in-duct microphones

2023· article· en· W4320182854 on OpenAlexaffabout
Philippe Micheau, Julien Drant, Alain Berry

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsSound powerDuct (anatomy)MicrophoneActive noise controlSecondary sourceEngineeringLoudspeakerComputer sciencePhysicsNoise reduction

Abstract

fetched live from OpenAlex

Much research has been conducted to investigate active noise control of turbofans with loudspeakers as noise cancellation sources. However, the required power consumption, fragility, and weight and volume penalty make them unsuitable for engine nacelle applications. An alternative source technology, developed at Sherbrooke, is a harmonic acoustic pneumatic source (HAPS). They are mounted on a ring to perform multimodal active noise control in duct by destructive interference of primary noise (RHAPSODI). A dedicated MIMO harmonic control strategy based on complex envelopes is required to control the RHAPSODI with in-duct microphones located at a very close distance from it or with external in-duct microphones located 1 meter away from the duct mouth. During a training phase, active radiated power minimization is performed with the external microphones for different modal components of the primary harmonic noise. The optimal HAPS control and the optimal signals from the in-duct microphones are then used to tune the specific MIMO controller with a field compensation matrix close to the in-duct microphone signals. A typical result shows that RHAPSODI using 5 in-duct microphones and 5 HAPS can provide high attenuation of radiated sound power (22 dB-SPL, 1 kHz, primary sound power 130 dB).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.676

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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 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 routes2
Has abstractyes

Explore more

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