Numerical modelling of sandwich panels with soft core and different rib configurations
Bibliographic record
Abstract
This paper presents numerical modeling of the flexural behaviour of sandwich panels composed of [0/90] woven glass fibre reinforced polymer skins and polyurethane foam core, including various patterns of glass fibre reinforced polymer ribs, as well as cores of different densities. A robust finite element model has been developed. It accounts for material nonlinearities; most pronounced in soft cores and [0/90] glass fibre reinforced polymer ribs in shear, as well as geometric nonlinearities arising in panels without ribs, in the form of a reduction in panel thickness and excessive shear deformation. The model captures both material failures and stability failure, essentially skin wrinkling in compression. The model is successfully validated using a large experimental database and predicts well full flexural responses. It is shown that ribs allow compression skin to reach its full material strength. Panels without ribs fail by skin wrinkling under concentrated loads, while those under distributed loads fail either by excessive shear deformation or diagonal fracture of the core, depending on core density. Failure of glass fibre reinforced polymer tension skin never occurred in this study. For panels without ribs, the three-dimensional finite element model agrees closely with a simplified two-dimensional model. A parametric study addressing longitudinal rib spacing showed that flexural strength increases as rib spacing reduces, until it stabilizes at a rib spacing-to-panel thickness ratio of 2.93.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".