Implementation of a self-sensing piezoelectric actuator for vibro-acoustic active control
Bibliographic record
Abstract
Significant reduction of airplane interior noise may be obtained by active structural acoustic control (ASAC) of fuselage panels. This requires to accurately measure the vibrations of the aircraft panels while injecting anti-vibrations. Co-located piezoelectric sensors and actuators, spatially distributed on the structure, are an interesting avenue since they can lead to the implementation of distributed virtual impedances. When the same piezoelectric device is used to simultaneously measure and actuate, it is called a self-sensing piezoelectric actuator (SSPA). When a SSPA is submitted to a voltage, the measured current is the sum of the electric current due to the capacitive effect of the transducer plus the mechanical current induced by the strain of the structure. The latter is an order of magnitude smaller than the total current measured. Provided the measured current is digitized with sufficient accuracy, adequate numerical processing can subtract the capacitive current from the total measured current. A similar processing can also be used to subtract from the sensor information, near-field vibrations induced by the collocated actuator. Hence, information related to the global, vibrational flexural modes of the plate is extracted without complicated electronics. The numerical method of current separation has been programmed and validated with MATLAB/SimulinkR® and implemented on Speedgoat hardware. A shunt resistor is used to measure the current simultaneously with the voltage measurement. Strain-induced current has been successfully extracted from SSPA signal with this method. Numerical simulations show good agreement with experimental data.
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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) | 0.003 | 0.001 |
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".