Nonlinear Deployment Dynamics and Wrinkling of a Membrane Attached to Two Axially Moving Beams
Bibliographic record
Abstract
The out-of-plane and in-plane deployment dynamics of a flexible space structure, namely, a solar sail quadrant consisting of a membrane attached to two support booms, are considered. The equations of motion of the system are obtained using a time-varying generalization of the extended Hamilton’s principle. They are then discretized via quasi-modal expansion of the deflections as truncated series involving both time- and space-dependent basis functions. Because all directions are accounted for and plate strains are used to capture the potentially significant effect of even a small stiffness on the dynamics, nonlinear terms appear in the discretized equations. To increase computational efficiency, coordinate transformations and linear algebraic manipulations are performed to make all spatial integrals time invariant. In addition, attempts are made to predict wrinkling using the Miller–Hedgepeth model: a coarse mesh is defined, the instantaneous state of each region is determined using a wrinkling criterion and averaged principal stresses, and constitutive relation of each region is adjusted based on its wrinkling state. Numerical simulations provide basic validation, sample deployment results, and a comparison against the results of an earlier linear model with only out-of-plane deflections. The stress predictions are also partially validated using previous results based on constant-size loaded membrane experiments.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".