Strain-Based Shape Estimation for Annular Plates
Bibliographic record
Abstract
Large flexible structures are used in many space applications, for example, satellite communication antennae, space robotic systems, and space station, etc. The flexibility of these large space structures results in problems of structure shape deformation and vibration, etc. In recent years, active shape control is developed to improve the performance of these flexible space structures. However, it is difficult to measure the surface shape of large flexible space structures in space environment. In this paper, a shape estimation method was developed to determine deflection of annular structures under arbitrary loading and boundary conditions. The shape estimation method utilizes strain information from strain gage sensors mounted on the structure. The strain field is calculated using polar components of stress in terms of Airy’s stress function. The coefficients of each function are determined based on the relationship of strain, displacement, and strain compatibility. The strain field is constructed by least squares smoothing procedure. This shape estimation method was verified by the numerical method, finite element and the experimental results. The results show that the shape estimation method can be used to determine deformation shape of the annular plate for active shape control of flexible structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".