Preliminary Numerical and Experimental Studies of Active Acoustic Control of Double-Glazed Partition Walls
Bibliographic record
Abstract
Glass partitions are used in workspaces to separate areas. To improve acoustic insulation, these partitions are composed of double glazing separated by an air space. However, double glazing offers weak insulation at low frequencies. The aim of this study is to improve low-frequency insulation in double-glazing using an active control system. The assumption is that minimizing the acoustic pressure in the cavity between the partitions will decouple the vibration of the two partitions and increase the insulation of the double glazing. This approach will be compared with the optimal strategy of directly reducing sound pressure in the receiving medium. The double-glazing numerical model consists of 30.48 cm square partitions with 6 mm thickness and 60 mm separation. The double glazing has free boundary conditions and is placed in an impedance tube. To create the active system, an error microphone and loudspeaker are placed in the cavity between the partitions. The numerical study is conducted in the frequency domain. Experimental tests are carried out for harmonic disturbances in the frequency range from 50 to 550 Hz. The control law minimizes the squared pressure at the error microphone and is obtained using Newton's algorithm. As expected, numerical simulations show a greater improvement in transmission loss with transmitted pressure control than with minimization of the acoustic pressure between the partitions. Additionally, experiments have shown that the optimal experimental transmission loss, up to 35 dB, is higher than the attenuation obtained by minimizing the acoustic pressure in the cavity, up to 20 dB. Thus, simulations and experiments have shown that controlling low-frequency sound pressure in the cavity of double-glazed partitions is an acceptable, if not optimal, approach. Future work would involve considering the presence of several loudspeakers and microphones in the cavity and compensating the measured sound pressure to optimize sound insulation.
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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 | 0.000 | 0.000 |
| 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".