Optimizing Loudspeakers Placement for Active Noise Control in Double-Glazing.
Bibliographic record
Abstract
Active noise control (ANC) in double-glazing (DG) systems generally follows three strategies: minimizing acoustic pressure in the cavity, downstream of the panel, or through active vibration control. While controlling the pressure field within the air gap shows potential for reducing transmitted sound, existing studies often rely on symmetric, corner-mounted loudspeaker configurations, which may be suboptimal for maximizing attenuation. This study explores how many loudspeakers are needed and where to position them inside the cavity to improve downstream attenuation. A finite element model simulating the transmission loss (TL) of a 2.1m×0.85m DG panel was developed in COMSOL and validated using measurements in a coupled-room facility. To assess ANC performance from 100-500Hz, this model was coupled with MATLAB to compute the anti-noise generated by each loudspeaker. A pseudo brute-force optimization in MATLAB was then used to identify the most effective loudspeaker positions along the inner perimeter of the cavity. This presentation details the optimization method and compares simulated TL and attenuation for different layouts. Results are discussed in relation to classical configurations from the literature, demonstrating that optimized source placement can significantly enhance ANC performance. Future work will focus on experimental validation of both harmonic and broadband active DG systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".