On the modeling of sound radiation from poroelastic materials
Bibliographic record
Abstract
Numerical approaches based on finite element discretizations of Biot’s poroelasticity equations provide efficient tools to solve problems where the porous material is coupled to elastic structures and finite extent acoustic cavities. Sometimes, it may be relevant to evaluate the radiation of a poroelastic material into an infinite fluid medium. Examples include (i) the evaluation of the diffuse field sound absorption coefficient of a porous material and/or the sound transmission loss of an elastic plate coupled to a porous sheet, (ii) the assessment of the acoustic radiation damping of a porous material coupled to a vibrating structure. The latter is particularly important for the correct experimental characterization of the intrinsic damping of the material’s frame. Up to now, the acoustic radiation of a porous medium into an unbounded fluid medium has usually been neglected. The classical approaches for modeling free field radiation of porous materials (i) assumes the interstitial pressure at the radiation surface to be zero or (ii) fixes the radiation impedance to an approximate value. This paper discusses the limitations of these assumptions and presents a numerical formulation for evaluating the sound radiation of baffled poroelastic media including fluid loading effects. The problem is solved using a mixed FEM-BEM approach where the fluid loading is accounted for using an admittance matrix. Both numerical examples and a transmission loss test are presented to illustrate the performance of the technique and its applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".