Identification of chaotic phenomena in a flexible deployable solar panel with multiple clearances
Bibliographic record
Abstract
Deployable solar panels are widely used in spacecraft, and the dynamic characteristics of the deployment process directly affect the accuracy, stability, and reliability of the deployment. The flexibility and hinge clearance of a solar panel are important factors affecting the dynamic characteristics of the deployment system. The finite element method (FEM) was used to deal with the deformations of the solar panel. A dynamic model of the deployment process of a flexible solar panel with multiple clearances was established by combining the Lagrange equation with the FEM. The dynamic characteristics of solar panel deployment with multiple clearances and flexibility coupling were analyzed through a numerical solution, and the chaotic phenomena caused by clearances were identified. The results show that reasonably matching the clearance and flexibility of the system structure could quickly stabilize the collision force, improve the system life, and effectively improve the stability of the solar panel deployment process. Chaotic phenomena could be induced by the deployment velocity in a certain range, and the boundary value of the range changed with different clearance radii. The velocity variation law inducing chaotic phenomena also varied with the radius of clearance. This research provides important guidance for the optimum design and manufacturing of deployable solar panel mechanisms.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".