Overview of the porous material characterization methods and impedance tube measurements, by Raymond Panneton (University of Sherbrooke)
Bibliographic record
Abstract
DENORMS Action's Training School Experimental techniques for acoustic porous materials and metamaterials, Le Mans, 4-6th December 2017 Website of DENORMS Action Programme of the Training School Lecturer: Raymond Panneton (University of Sherbrooke) Abstract: This course will present direct and inverse methods, standards, and tools for the characterization of the acoustical and non-acoustical properties of acoustic porous materials. It will present the main challenges related to their characterization and guidelines to ensure their fine characterization. It will also address solutions to recurrent problems observed during characterization in relation with acoustical leaks, frame vibrations, boundary conditions, variations of environmental conditions, badly shaped samples, mounting of the samples, and non-symmetry and non-homogeneity of the samples. Examples of the characterization of different porous media (foams, fibers, felts, films) with rigid-frame, limp-frame, and elastic-frame behaviors will be presented. The acoustical properties covered in this course are the normal incidence sound absorption coefficient, sound transmission loss, surface and characteristic acoustic impedance, complex wave number, dynamic density and bulk modulus, and transfer matrices. The non-acoustical properties are notably the open porosity, static airflow resistivity or permeability, tortuosity, viscous and thermal characteristic lengths, static thermal permeability, and bulk density. While the acoustical properties are obtained with acoustical impedance/transmission tubes, the non-acoustical properties are obtained by direct methods and by inverse methods based on impedance/transmission tube measurements.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".