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Record W2030818491 · doi:10.1177/0021998305050738

Finite Element Modeling of a Membrane Sector of a Satellite Reflector Made of Triaxial Composites

2005· article· en· W2030818491 on OpenAlexaff
Qi Zhao, Suong V. Hoa

Bibliographic record

VenueJournal of Composite Materials · 2005
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsDeflection (physics)Materials scienceStiffnessComposite materialStructural engineeringFinite element methodEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

Single layers of triaxial woven fabric composites have been used to make communication satellite reflectors due to their extremely light weight with good stiffness and strength property. Special superfinite elements have been developed for the analysis of these lightweight materials. These superfinite elements are used for the deflection and stress analysis of a membrane sector of the satellite reflector subjected to lateral pressure. Due to the significant difference in the size of the satellite structure (order of meters) and the size of the superfinite elements (order of millimeters) it is not possible to model the satellite structure using the superfinite elements and computer facilities available in most labs (personal computers). In the process of finding a solution to this problem, a special similitude behavior for the deflection of the curved panels made of triaxial fabrics was discovered. Using this behavior, the deflection of a large-size reflector panel (order of meters) can be analyzed using models in the millimeters size range. Stresses in the triax reflector model were also calculated. However, no special similitude behavior was obtained for the stresses.

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)0.000
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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