Refined design curves for compressive buckling of curved panels using nonlinear finite element analysis
Bibliographic record
Abstract
The present work deals with implementation of a nonlinear finite element technique for the prediction of the initial buckling in simply-supported curved panels subjected to pure compression. A single nonlinear design curve is derived, representing the compressive buckling stress coefficient as a function of the curved panel parameter for a radius-to-thickness ratio between 1000 and 2000. The proposed curve represents an update and expansion of the currently-used NACA design curves, and it includes the effect of a pre-existing level of imperfection in the panel on the buckling coefficient.The results presented in this work show a strong dependency of the buckling stress on the degree of initial imperfection in the panel, especially for highly-curved panels. The results also indicate that the actual amount of imperfection in a given panel is dependent upon the curved panel parameter and its radius-to-thickness ratio. A detailed knowledge of this dependency would lead to a better prediction of the buckling stress of the panel.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".