A semi-empirical model relating micro structure to acoustic properties of bimodal porous material
Bibliographic record
Abstract
Complex morphology of open cell porous media makes it difficult to link microstructural parameters and acoustic behavior of these materials. While morphology determines the overall sound absorption and noise damping effectiveness of a porous structure, little is known on the influence of microstructural configuration on the macroscopic properties. In the present research, a novel bimodal porous structure was designed and developed solely for modeling purposes. For the developed porous structure, it is possible to have direct control on morphological parameters and avoid complications raised by intricate pore geometries. A semi-empirical model is developed to relate microstructural parameters to macroscopic characteristics of porous material using precise characterization results based on the designed bimodal porous structures. This model specifically links macroscopic parameters including static airflow resistivity (σ), thermal characteristic length (Λ′), viscous characteristic length (Λ), and dynamic tortuosity (α∞) to microstructural factors such as cell wall thickness (2t) and reticulation rate (Rw). The developed model makes it possible to design the morphology of porous media to achieve optimum sound absorption performance based on the application in hand. This study makes the base for understanding the role of microstructural geometry and morphological factors on the overall macroscopic parameters of porous materials specifically for acoustic capabilities. The next step is to include other microstructural parameters as well to generalize the developed model. In the present paper, pore size was kept constant for eight categories of bimodal foams to study the effect of secondary porous structure on macroscopic properties and overall acoustic behavior of porous media.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".