On the modeling of the diffuse field sound transmission loss of finite thickness apertures
Bibliographic record
Abstract
The modeling of the diffuse field sound transmission loss (TL) of apertures has been rarely considered in the literature. The aims of this paper are (i) to give a comprehensive review of the existing models, (ii) to propose a general efficient and rigorous numerical method to predict the diffuse field TL of apertures of rectangular and circular cross section, (iii) to provide the reader with numerical results regarding this indicator together with its relation with the normal incidence case for various geometrical configurations, and (iv) to conclude on the relevance of using such a sophisticated model compared to more classical normal incidence ones. The proposed approach is based on the description of the sound field inside the aperture in terms of propagating and evanescent acoustic modes. The radiation of the aperture is accounted for using a modal radiation impedance matrix. The coupled problem is solved in terms of modal contribution factors. The convergence of the approach is then investigated and the model is validated by comparisons with existing models for various configurations and excitations. Several numerical examples are provided regarding the normal incidence and diffuse field TL for various apertures and the relationship between these two indicators is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".