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Record W2140981804 · doi:10.2514/6.2001-1328

Refined design curves for compressive buckling of curved panels using nonlinear finite element analysis

2001· article· en· W2140981804 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue19th AIAA Applied Aerodynamics Conference · 2001
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsBucklingFinite element methodStructural engineeringNonlinear systemRADIUSStress (linguistics)Compression (physics)Dependency (UML)Work (physics)Materials scienceEngineeringComputer scienceComposite materialMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.248
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it