Fiber Element Model of Sandwich Panels with Soft Cores and Composite Skins in Bending Considering Large Shear Deformations and Localized Skin Wrinkling
Bibliographic record
Abstract
This paper studies the flexural performance of sandwich panels composed of a soft polyurethane foam core and glass-fiber-reinforced polymer (GFRP) skins. A robust numerical model is developed to predict the full load-deflection and strain responses of the panel. It is based on equilibrium and strain compatibility and accounts for the excessive shear deformation and material nonlinearity of the core. It also accounts for geometric nonlinearity in the form of localized deflection of the loaded skin using the principals of beam-on-elastic foundation and the change in core thickness due to its softness. The model incorporates various failure criteria, namely core shear failure, core flexural tension or compression failure, compression skin crushing or wrinkling, or tensile rupture of skin. The model has the advantage of being able to isolate quantitatively the individual contributions of flexure, shear, and localized skin deformations, to overall deflection. A parametric study is performed to examine the effects of core density and skin thickness on panel behavior. It is shown that as the core density increases from 32 to 192 kg/m3, the contribution of shear to overall deflection reduces from about 90 to 10%. It also appears that the optimal core density of the sandwich panels is within 96 to 128 kg/m3, which represents the lowest density necessary to achieve the highest ultimate strength and stiffness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".