Vibration of microperforated plate with spatial distribution of multiple-sized perforations
Bibliographic record
Abstract
Recent works by the authors on homogeneous MPPs have highlighted the structural damping capabilities of MPPs in the low frequency range. The developed theoretical approach was based on the analogy between an MPP and a porous plate. The added damping is due to visco-thermic effects coupled to fluid-structure interactions. The added damping maxes out at a characteristic frequency depending on perforation diameter. In order to reduce plate vibrations, it was advised to match the characteristic frequency to a plate mode. It is proposed here to maximize the added damping effect on several vibration modes by focusing on MPPs with multiple-sized perforations and with spatial distribution of perforations. As an extension of the previous analytical model, an approach based on the electro-acoustic analogy is established to capture the effect of multiple-sized perforations. Moreover, a perforation ratio gradient is included in the approach to model an MPP with inhomogeneous spatial distribution of perforations. Experimental measurements on MPPs validate the proposed analytical model. Results show that: (i) MPP with multiple-sized perforations increases the frequency band of the effective damping; (ii) the added damping increases when the perforations are distributed around the antinodes of the considered mode, (iii) the two effects can be combined.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".