Buckling of sandwich panels with transversely flexible core: Correction of the equivalent single-layer model using thick-faces effect
Bibliographic record
Abstract
Modeling a periodic structure as a homogeneous continuum allows for an effective structural analysis. This approach represents a sandwich panel as a two-dimensional plate of equivalent stiffness. Known as the equivalent single-layer, the method is used here to analyze bifurcation buckling of three types of sandwich panels with unidirectional stiffeners in the core: truss-core, web-core and corrugated-core panels made of an isotropic material. The transverse shear stiffnesses of these panels can differ by several orders of magnitude, which cause incorrect buckling analysis when using the equivalent single-layer model with the first-order shear deformation theory. Analytical solution of the problem predicts critical buckling loads that feature infinite number of half-waves in the direction perpendicular to the stiffeners. Finite element model also predicts buckling modes that have non-physical, saw-tooth shape with infinite curvature at nodes. However, such unrealistic behavior is not observed when using detailed three-dimensional finite element models. The error in the prediction of the critical buckling load is up to 85% for the cases considered here. The correction of the equivalent single-layer model is proposed by modeling the thick-faces effect to ensure finite curvature. This is performed in the finite element setting by introducing an additional plate with tied deflections to the equivalent single-layer plate. The extra plate is represented with bending and transverse shear stiffness of the face plates. As a result, global buckling is predicted accurately. Guidelines are proposed to identify the sandwich panels where ordinary model is incorrect. Truss-core and web-core sandwich panels need the correction. Corrugated-core panels without a gap between plates in the core have smaller shear orthotropy and do not need the correction. Modeling the thick-faces effect ensures correct results for all cases considered in this study, and thus one should resort to this approach in case of uncertainty whether the ordinary equivalent single-layer model is valid.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".