Dynamics of Harmonic Active Sound Control with a Harmonic Acoustic Pneumatic Source
Bibliographic record
Abstract
During takeoff, the harmonic noise of the turbofan is the main acoustic nuisance for people near airports. Much research has been done on the topic of active reduction of turbofan noise. Loudspeakers used as secondary sources present the disadvantage of being fragile and need a high consumption to produce the required sound intensity. In this context, an alternative solution, called the Harmonic Acoustic Pneumatic Source (HAPS), has been designed to generate a high harmonic noise level controllable in amplitude, phase and frequency. Previous studies on the subject have demonstrated the possibility to perform active noise control with a ring of HAPS in a cylindrical duct, but the convergence time of the controller was about several seconds. The dynamics of the controller is thus the limiting factor for active noise control applications. The objective of this study is to address this problem by characterizing and designing a controller with a short response time, typically less than a second. Therefore, we present an experimental configuration with a loudspeaker acting as a primary source and a HAPS mounted laterally on a rectangular duct. A microphone is placed in acoustic near-field. The control strategy, based on integral feedback, aims at minimizing the pressure at the microphone location. Simulations are realized with a first order model for the HAPS and compared to the experimental results which include measurements of the HAPS response time and the controller response time for active noise reduction. This study is a first step to the objective of performing active noise control in presence of flow, with an experimental campaign planned on the MaineFlow duct (Le Mans, France). This work is supported by Association Nationale Recherche Technologie (ANRT, France) and Safran Nacelles (Le Havre, France). We gratefully acknowledge Marc Versaevel from Safran Nacelles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".