Active shape control of composite structures under thermal loading
Bibliographic record
Abstract
Maintaining the shape of high-precision structures such as space antennas and optical mirrors is still a challenging issue for designers. These structures are subjected to varying temperature conditions which often introduce thermal distortions. The development of smart materials offers great potential to correct the shape and to minimize the surface error. In this study, shape control of a composite structure under thermal loading using piezocomposites is investigated. The composite structure is made of a foam core and two carbon–epoxy face sheets. Macro-fiber composite (MFC™) patches are bonded on one side of the structure. The structure is subjected to a through-the-thickness temperature gradient which induces thermal distortion, essentially in the form of bending. The objective is to apply electric potential to the MFC™ actuators such that the deflection can be minimized. Finite-element analyses are conducted using the commercial software ABAQUS. Experiments are performed to study thermally induced distortion, piezoelectric actuation, and compensation of thermal distortion using MFC™ actuators. Numerical and experimental results are compared. A control loop based on strain measurements is used to actively control the structure. The results show that MFC™ actuators can compensate thermal distortion at all times, and that this is an efficient methodology.
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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".