Experimental Mechanics of a Wrinkled Multi-Layer Deployable Membrane Space Antenna
Bibliographic record
Abstract
This paper presents an investigation into the properties of membrane structures for space-based antenna applications. Various experimental activities that have been undertaken at the early phase of development of a multi-layer, deployable membrane structure are outlined. The membranes described here are prototypes for the development of a possible on-orbit flight experiment in thin structure deployment, where such an experiment addresses necessary questions related to the use of this technology for membrane synthetic aperture radar. These experiments have used a Dynamic Mechanical Analyzer to examine the elastic and microplastic behaviour of the membrane material. The aim of these experiments is to be able to better predict the flatness variations that would result from the wrinkling, folding, and stowage of the structure prior to deployment. The experiments described in this paper explore how variations in elastic behaviour due to material viscosity and microplastic behaviour affect the strain in a material when folded, and also explore how these properties vary with changes in temperature. Two types of thin film material are considered as possible membrane structures for antenna applications. It is expected that the stowage and deployment method, as well as the tensioning system for the multi-layer membrane once deployed, will depend on the results of these investigations of fundamental properties.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".