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Record W2315121864 · doi:10.2514/6.2012-1914

Finite Element Analysis of a Wrinkled Rectangular Membrane with Elliptical Boundary Cuts

2012· article· en· W2315121864 on OpenAlexafffund
Ryan Orszulik, Jinjun Shan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsYork University
FundersCanadian Space Agency
KeywordsFinite element methodTension (geology)Materials scienceBoundary value problemStructural engineeringBoundary (topology)ThermalMechanicsReduction (mathematics)Thermal expansionComposite materialGeometryEngineeringMathematicsPhysicsThermodynamicsMathematical analysisCompression (physics)

Abstract

fetched live from OpenAlex

This paper presents finite element analysis of a rectangular membrane with elliptical boundary cuts under various loading conditions. A thermo-mechanical analysis investigates the effects of heat loads on the wrinkling and wrinkle reduction using boundary forces. A localized heat load will cause thermal expansion of the membrane close to the heat source while further areas will remain unaffected. The expansion of the heated area will cause compressive stresses where it meets unaffected regions causing wrinkles to form. To remove the wrinkling due to thermal loads, various tension force combinations are analyzed and the results show that it is possible to do so, and an appropriate tension scheme is set up. To try to validate the results from analysis, preliminary experimental data is compared to the results obtained from Abaqus.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

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