Inverse acoustical characterization of porous material: A novel approach using diffuse sound field and transmission loss
Bibliographic record
Abstract
Passive noise control employing porous material is widely used in aircraft cabins. Inverse acoustical characterization has become popular to retrieve porous material macroscopic parameters due to its straightforwardness in terms of experimental practicality, costs, and time involved. Most of those methods have as input data some impedance tube measurement. However, this approach has well-known drawbacks, mainly concerning the sample boundary conditions. In this study, the sound transmission loss measured in reverberation rooms is adopted as input data to obtain the macroscopic parameters by inverse characterization. Two-meter-squared samples were measured. Three low-density porous materials, typically applied in the aerospace industry, were analyzed. Transfer Matrix Method is combined with Johnson-Champoux-Allard equivalent fluid model on an optimization process using Differential Evolution algorithm. The cost function uses the mean square error between model prediction and measured values. The frequency range is selected to minimize the uncertainties from the diffuse sound field. Satisfactory repeatability on the optimized parameters was achieved. The macroscopic parameters obtained from the transmission loss measurements as input seem to be more robust than those obtained from the impedance tube measurements. The results suggest that the boundary conditions have less influence on measured data in reverberation rooms setup than in impedance tube.
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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 | 0.000 |
| 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) | 0.000 | 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".