Progressive Damage Simulation of Thick Viscoelastic Laminate with Homogenization Technique
Bibliographic record
Abstract
For a thick viscoelastic laminate, modeling of individual lamina and cracks is impractical because it requires huge computational expense. To reduce the computational time, homogenization methods at the lamina and sublaminate (groups of plies) levels were used to develop a progressive damage analysis of viscoelastic laminates with transverse matrix cracks. At the lamina level, homogenization is used to determine the effective lamina properties, which are degraded due to matrix cracks. At the sublaminate level, the effective properties of sublaminates were obtained from effective lamina properties using the sublaminate homogenization method. The current study focused on combining these methods to develop an efficient progressive damage analysis of thick laminates that accounts for the effect of the time-history of matrix cracking and viscoelasticity. Examples of the progressive damage analysis are provided. The study showed that the multilevel homogenization technique developed herein for progressive damage analysis of a thick viscoelastic laminate with cracks is very efficient and accurate.
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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 | 0.001 | 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".