Reconstruction of Porous Microstructure Models for Validation and Estimation of Phenomenological Acoustic Parameters
Bibliographic record
Abstract
Porous materials are widely used for sound absorption, yet accurately modeling their acoustic properties remains challenging. The widely adopted phenomenological models calculate equivalent density and bulk modulus based on macro material parameters (porosity, tortuosity, resistivity, etc.), usually obtained from estimated physical measurements or computer tomography (CT) images of samples. However, a fully computational simulation approach (i.e., no experiment is needed to estimate parameters) is desired, where the phenomenological macro parameters can be obtained and validated via micro-scale fluid dynamics models. In this work, we present an efficient procedure to construct detailed 3D geometry of porous materials based on CT images of a material sample. The microstructure information is extracted mainly using a watershed-based image segmentation technique. These high-fidelity geometries enable micro-scale fluid dynamics simulations, providing a virtual testbed. By comparing these micro-scale simulation results against predictions from equivalent continuum models, we can rigorously validate the phenomenological parameters. This multi-scale framework facilitates the fundamental understanding of material microstructures, enabling more accurate design and optimization of acoustic treatments for noise control applications in transportation and building systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".