Lightweight damping of composite sandwich beams: Experimental analysis
Bibliographic record
Abstract
The purpose of this article is to experimentally study the damping of composite sandwich beams with lightweight honeycomb core. The top and bottom facesheets are made of carbon/epoxy layers with partial interleaved viscoelastic layers. A new damping approach consisting of selectively targeting the inflection points of the bending mode shapes is proposed. At the nodes, the shearing deformation in the beam is maximal, and so is the strain in the viscoelastic layers. The experimental investigation of damping is made by means of standard impact tests using an instrumented hammer performed on beam specimens. The nodes are determined experimentally by moving a small accelerometer along the beam axis and by measuring the amplitude of the acceleration at each point. This novel damping approach keeps the damping ratio as high as the ratio obtained with standard (full coverage) surface damping treatment while reducing the added mass by almost 50%. A comparison of the results obtained in this study with experimental and numerical results found in the literature leads to the conclusion that the most efficient way of damping this type of sandwich structure is to modify and/or improve the viscoelastic properties of the core.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".