Thermo-mechanical analysis of thin membranes and application in active flatness control design
Bibliographic record
Abstract
Membrane structures are excellent candidates for many lightweight large space structures, which can be utilized to improve the performance and to reduce the cost of space exploration and earth observation missions. In-orbit thermal disturbance is the main cause of membrane wrinkling, which deteriorates membrane surface accuracy. In order to maintain surface accuracy in the time-varying environment, active flatness control is regarded as a very important technology. In order to properly design active flatness control system, understanding of the thermo-mechanical coupling and the effects of tensioning forces in reducing membrane wrinkling is required. Based on the von-Karman nonlinear plate theory, a theoretical framework is developed in this paper. An FE model for a square membrane is developed as an example for case studies. Using thin shell elements, this model is capable of predicting the amplitude of out-of-plane displacements. Using this model, the effect of thermal disturbance is studied, which qualitatively agrees with experimental observations. By varying corner loads in the numerical model, it is demonstrated that corner loads can efficiently reduce surface deviation caused by a center-located heat source. In order to study the effect of thermal disturbance locations, different temperature distributions are applied to the membrane. With these temperature distributions, different tension forces combinations are evaluated in terms of improving surface accuracy.
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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) | 0.001 | 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".