Enhanced Active Constrained Layer Damping Treatment for Broadband Vibration Suppression
Bibliographic record
Abstract
In this paper, the Enhanced Active Constrained Layer (EACL) treatment is investigated for broadband damping augmentations on beam structures. The EACL concept was originally proposed to improve the damping performance of the Active Constrained Layer (ACL) by introducing edge elements at the treatment boundaries. It has been recognized that the edge elements can increase ACL performance by enhancing the direct active authority of the piezoelectric constraining layer. It has also been demonstrated that the edge element stiffness and the host structure strain field have significant influence on the overall closed-loop system damping and its various components: the active damping, the closed-loop passive damping, and the open-loop passive (fail-safe property - without any active action) damping. Through utilizing this finding, the present study explores how the EACL performance can be synthesized for multiple mode broadband applications using symmetric configurations. Although the edge elements will tend to reduce the maximum possible open-loop damping of one (or a few) vibration mode, open-loop damping of the other higher order modes could actually be increased. Moreover, the modal damping reduction in the open-loop system can generally be compensated by the significant increase of the closed-loop damping. In other words, the closed-loop EACL system damping over a wide frequency range can be significant, which makes it attractive for broadband vibration and noise suppression.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".