Prediction of effective properties and sound absorption of random close packings of monodisperse spherical particles: Multiscale approach
Bibliographic record
Abstract
The transport and sound absorption properties of random close packings of monodisperse spherical particles are explored following a multiscale approach. First, the discrete element method is used to simulate the free fall of the monodisperse particles in a bounded domain to create virtual samples that are representative of real samples. Different particle diameters ranging from 1 to 16 mm are studied. From the virtual samples, representative volume elements (RVEs) are defined. Local partial differential equations governing the transport properties are numerically solved on the RVEs. From the discretized RVEs and the numerical solutions, eight transport properties (porosity, tortuosity, and viscous and thermal static tortuosities, permeabilities, and characteristic lengths) are derived. Micro-macro relationships between these properties and the particle diameter are developed. They are validated against experimental measurements of the open porosity and sound absorption coefficients. The relationships are used to analyze the salient sound absorption features of such media, notably the resonant sound absorption behavior. Expressions allowing identification of the optimal particle diameter for a given thickness, or conversely, the optimal thickness for a given particle diameter, for achieving 100% absorption at the first resonant absorption are derived.
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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 | 0.001 | 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 | 0.001 |
| 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".