Physical investigation on the sound transmission loss of heterogeneous metastructures using wave-based methodologies
Bibliographic record
Abstract
Existing methodologies employed for computing acoustic indicators of heterogeneous metastructures have limitations in terms of accuracy and efficiency, especially for metastructures showing complex phenomenon that involves the interaction between different wavetypes (flexural, shear, and high-order waves), which significantly influences the sound transmission characteristics of the structure. The present study focuses on providing an efficient wave-based methodology to study the wave coupling effect and the accurate estimation of Sound Transmission Loss (STL) of these metastructures. It develops the Wave Finite Element (WFE) scheme to compute the STL by applying a plane wave excitation from the ambient fluid domain to a representative Unit Cell (UC) of the periodic structures, this methodology is also exploited to study the sophisticated phenomena of wave coupling on the accurate computation of acoustic indicators. STL under Diffuse Acoustic Field (DAF) is computed by integrating the wave STL over all possible incidence angles. Various configurations, laminated glass with Polyvinyl Butyral (PVB) core, sandwich plates with a Shape Memory Polymer (SMP) core, sandwich plates with a thick soft core, Cross-Laminated Timber (CLT) plate, and a curved structure configuration, are presented to highlight the advantages of the proposed wave-based methodology for investigating the physics of sound transmission in heterogeneous metastructures. • Wave-based methods can accurately predict the acoustic indicators of metastructures. • Modeshapes from WFE method provide a visualization tool for the k-wave analysis. • Conventional plate theory fails to predict the symmetric/dilatation motion. • WFE method visually depicts the wave coupling effect especially in curved structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".