Virtual mechanical impedance approach for the active structural acoustic control of panels
Bibliographic record
Abstract
This work investigates harmonic Active Structural Acoustic Control of flexural panels using structural, collocated and dual actuator-sensor pairs. Two types of transducer technologies are envisioned: (1) thin piezoelectric actuators and sensors; (2) electrodynamic inertial actuators and transverse velocity sensors. The control strategy is to locally impose a complex, virtual mechanical impedance to the structure via a linear relation between the actuator input and sensor output of each pair, at each frequency of interest. This virtual impedance is optimized to minimize the sound radiation of the structure at the corresponding frequency. The approach is implemented as a two-step process: (1) the optimal virtual impedance matrix is derived from identification of the primary sound and transfer functions between the control actuators, structural sensors and far-field acoustic sensors; (2) the optimal virtual impedance matrix is imposed using a real-time, iterative controller. Numerical and experimental results are discussed to highlight the underlying physical interpretation of the virtual impedances for sound radiation or sound transmission control. The implication of the different actuator and sensor technologies in terms of sensing the global vibration and acoustic response is also discussed.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".