Feedforward Harmonic Suppression for Noise Control of Piezoelectric Synthetic Jet Actuators
Bibliographic record
Abstract
Piezoelectrically driven Synthetic Jet Actuators (SJAs) are a class of pulsatile flow generation devices that promises to improve upon steady forced cooling methods in air flow generation, surface cleaning and heat transfer applications. Their acoustic emissions and vibrations, an intrinsic by-product of their operation, needs to be mitigated for applications in noise-sensitive contexts. Already used for aerodynamic control [1, 2], thrust vectoring [3], spray control [4], and heat transfer [5, 6], they are increasingly being considered for sensor lens cleaning in automobiles. In this study, the sound generation mechanisms of SJAs are discussed and an active noise reduction method is proposed and evaluated. Driven with a single frequency sinusoidal input, SJAs produce acoustic emissions at harmonic frequencies within the frequency range of speech communication. The sound pressure levels of those emissions are commensurate with that of other automotive subsystems and electronic components. Previous attempts at noise control include passive strategies, such as, nozzle design [7, 8], enclosure design [5], and mufflers. Active strategies, such as out-of-phase actuator pairs [9], and signal modulation have also been considered. Given their relative novelty, further reduction is possible at the source through the tuning of prominent tonal components of their emissions. The present study demonstrates that feedforward active control, achieved through input harmonic tuning, results in significant drops in the loudness of the radiated sound with minimal effects on flow and cleaning performance. Acoustic emissions from all prominent harmonics could be reduced using this method. Tuning is local to the location of the error microphone and further work is required to achieve global tuning. Preliminary performance investigations are presented.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".