ANALYSIS OF THE EFFECTS OF LAMINATE DEPTH AND MATERIAL PROPERTIES ON THE DAMPING ASSOCIATED WITH LAYERED STRUCTURES IN A PRESSURIZED ENVIRONMENT
Bibliographic record
Abstract
In aerodynamic and machine structures, one of the effective ways of dissipating unwanted vibration or noise is to exploit the occurrence of slip at the interface of structural laminates where such members are held together in a pressurised environment. The analysis and experimental investigation of such laminates have established that when subjected to either static or dynamic loading, non-uniformity in interface pressure can have significant effect on both the energy dissipation and the logarithmic damping decrement associated with the mechanism of slip damping. Such behaviour can in fact be effectively exploited to increase the level of damping available in such a mechanism. What has however not been examined is to what extent is the energy dissipation affected by the relative sizes or the material properties of the upper and lower laminates? In this paper the analysis is extended to incorporate such effects. In particular, by invoking operational methods, it is shown that variation in laminate thickness may provide less efficacious means of maximizing energy dissipation than varying the choice of laminate materials but that either of these effects can in fact dwarf those associated with non-uniformity in interface pressure. To achieve this, a special configuration is required for the relative sizes and layering of the laminates. In particular, it is shown that for the case of two laminates, the upper laminate is required to be thinner and harder than the lower one. These results provide a basis for the design of such structures.
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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) | 0.000 | 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) | 0.001 | 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".