Inverse acoustical characterization of open cell porous media using impedance tube measurements
Bibliographic record
Abstract
Unlike porous models developed for particular absorbing materials and frequency ranges, the Johnson-Champoux-Allard model is a generalized model for sound propagation in porous materials over a wide range of frequencies. This model is nowadays used widely across the acoustic research community and by industrial sector. However to use this model, the knowledge, particularly, of the intrinsic material properties defining the model is necessary. Using the proposed porous model and with the knowledge of the intrinsic properties, the calculation of the desired acoustical indicators as well as the design and optimization of several acoustic treatments for noise reduction can be done efficiently and rapidly. The model of Johnson-Champoux-Allard is based on five intrinsic properties of the porous medium: the flow resistivity, the porosity, the tortuosity, the viscous characteristic length, and the thermal characteristic length. While the open porosity and airflow resistivity can be directly measured without great difficulties, the direct measurements of the three remaining properties are usually complex, less robust, or destructive. To circumvent the problem, an inverse characterization method based on impedance tube measurements is proposed. It is shown that this inverse acoustical characterization can yield reliable evaluations of the tortuosity, and the viscous and thermal characteristic lengths. The inversion algorithm contains an optimization process and hence it is verified that the identified optimal three parameter, even though derived from a mathematical optimum for a given experimental configuration (sample's thickness, measured frequency range), are the intrinsic properties of the characterized porous material.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".