Nonlinear vibration characterization of the synthetic jet actuator to reduce noise levels
Bibliographic record
Abstract
Synthetic jet actuators (SJAs) generate unwanted noise levels that can be detrimental to human well-being if exposed to these noise levels over long periods of time. SJAs produce noise through the pressure variations from the jet and through the vibration of the piezoelectric actuator. Laser Doppler velocimetry has shown that harmonic excitation causes vibration response at overtone frequencies. The reasoning for excitation at overtone frequencies is not well understood. Understanding of vibrational nonlinearities can lead to developing methods or selecting design parameters to minimize noise levels while maximizing jet performance. The vibration of the SJA is characterized at three separate levels: the piezoelectric disk, the single membrane, and the full SJA structure. The piezoelectric disk is subject to hysteresis in the piezoelectric effect and electrostriction. The single membrane is a sandwich structure consisting of two piezoelectric disks with a laminate material as the sandwich layer, which may contribute to additional nonlinearities due to material properties. Finally, the complete SJA structure has coupling between the structural and acoustical system. Characterization of the sources of nonlinear behavior is to be used in establishing a more accurate numerical simulation, which can be used to optimize design parameters such as dimensions and voltage input to minimize noise levels while maximizing jet performance.
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 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 | 0.000 | 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".