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Preliminary results of a MIMO Harmonic Controller to perform active sound power attenuation with a RHAPSODI

2022· article· en· W4287846167 on OpenAlexaff
Philippe Micheau, Julien Drant, Alain Berry

Bibliographic record

Venue2022 IEEE 17th International Conference on Control & Automation (ICCA) · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMIMOSound powerAttenuationAcousticsController (irrigation)Computer scienceAcoustic attenuationDuct (anatomy)HarmonicCompensation (psychology)Control theory (sociology)Electronic engineeringEngineeringPhysicsTelecommunicationsSound (geography)BeamformingOpticsControl (management)

Abstract

fetched live from OpenAlex

A Harmonic Acoustic Pneumatic Source (HAPS) is an acoustic device developed to perform active harmonic sound control in turbofans. In order to develop a demonstrator, a ring of multiple HAPS (RHAPSODI) is assembled around a cylindrical duct and a dedicated MIMO harmonic controller was developed to perform active sound power attenuation. The control objective is to develop a MIMO feedback controller adapted to multiple HAPS and using in-duct microphones close to them. Three points are addressed : the control of HAPS as a mechanical modulator, the in-duct error microphones located in the near field of HAPS, and the tuning of the controller. The presented method is based on a dedicated MIMO feedback controller of complex envelops with compensation of near field. The compensation matrix of the near field is tuned during a learning phase based on the measurement of a set of optimal commands experimentally obtained with a dedicated controller using the out-duct microphones. When the matrix of compensation is estimated, the MIMO controller using induct microphones can be used. Preliminary experimental results show that a RHAPSODI of 5 HAPS can attenuate the first harmonic of a radiated tonal sound power by more than 20dB.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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