Analytical solutions for characterizing tortuosity and characteristic lengths of porous materials using acoustical measurements: Extrapolation model
Bibliographic record
Abstract
Following sophisticated descriptive models on sound propagation in porous media, five acoustical parameters usually describe the complexity of the porous network: porosity, static air flow resistance, tortuosity, and viscous and thermal characteristic lengths. While porosity and airflow resistance can be measured directly and accurately using simple apparatuses, simple and accurate direct measurements of the tortuosity and characteristic lengths are still an open research area. Actual researches on the characterization of these parameters use acoustical methods in the ultrasound frequency range. In this paper, a similar approach is investigated, but applying this time in the frequency range 2000–6000 Hz. The method is based on analytical solutions linking the tortuosity and characteristic lengths to measured dynamic density and compressibility. Following an extrapolation procedure to infinite frequency, the tortuosity and characteristic lengths are deduced without prior knowledge of porosity and air flow resistance. Experimental results from Kundt’s tube are compared to simulations on various materials to show the accuracy and limitations of the method.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".