Multi-wave-based semi-analytical homogenization approaches for the vibroacoustics of elastic multilayered structures including curvature, orthotropy, asymmetry, high shear and high damping
Bibliographic record
Abstract
This work aims to highlight the limitations of three homogenization strategies using a single layer plate to represent the vibroacoustics of flat and curved multilayered structures. Equivalent material properties and equivalent stiffness coefficients are obtained from the dispersion curves of the three main freely-propagating waves of the reference structure and thereafter utilized to build an equivalent simple structure. To demonstrate the accuracy and limitations of the homogenization strategies, three carefully selected flat and curved multilayered structures are presented. The particular effects of asymmetry, orthotropy, high shear and high damping (structural loss factor, η > 0.5) are addressed. A wave and forced analysis is performed utilizing the General Laminate Model (GLM) and four different vibro-acoustic indicators (total energy, input, radiated power and diffuse field transmission loss) are computed and compared with reference solutions. Results obtained from three homogenization strategies from literature are presented and compared with the proposed models. The comparisons show that, in general, the use of the three main freely-propagating waves and a single layer plate can equivalently represent flat and curved multilayered structures within the studied frequency range and with good potential applicability to complex heterogeneous structures. In particular, comparisons with literature results highlight that the use of only the first freely-propagating wave in a homogenization strategy leads to inaccuracies in the forced analysis of highly damped sandwich panels and thick orthotropic cross-laminates. Major challenges of the proposed strategies are noted when highly damped structures are homogenized. • Three wave-based models for identifying equivalent properties of multilayered structures are assessed. • The use of the three main waves of a structure presents better performance than the use of only the first one. • A good performance in wave context does not ensure a good performance in forced response context. • Using the transmission loss is not enough to assess the performance of equivalent plate models. • Highly damped elastic multilayered panels present a major challenge for equivalent properties computation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".