On the Theory of Smart Composite Structures
Bibliographic record
Abstract
The present paper is concerned with the basic aspects of a newly suggested theory of smart composite structures based on the continuum mechanics approach. The governing equations describing the behavior of a smart composite structures incorporating sensors and actuators are derived, and the basic optimization problems in the design of these controllable structures are formulated. This theory deals mainly with the extremal features of the controllable smart structures. The objective of modeling is to determine limiting properties of the smart structure. This also allows to determine whether the properties of the presently existing materials, sensors and actuators are sufficient for the optimal design of smart structure, or the development of some new materials, sensors or actuators is required. The basic optimization problems for the smart composite structures are illustrated by three examples in which the three main sources of control are emphasized. These are the residual strains, material properties, and the geometry of a structure. In the first example, we derive the optimal residual stress in an actuator which provides the minimum deflection of a composite cantilevered beam under static loading. It is shown that the effect of actuator allows to reduce the maximum deflection by 28 times compared with the same beam without active control. The second example is concerned with the optimal design of the controllable Winkler’s foundation in the problem of vibration damping for a simply supported beam under the dynamic loading. The controllable property here is a rigidity of foundation. It is shown that by using the optimally designed controllable foundation, the maximum deflection of a beam can be reduced by about 8 times. The third example deals with the optimal design of an actuator for a smart composite beam. The objective is to reduce the maximum deflection by applying a constant residual strain to the actuator. It is shown, in particular, that for the strains which exceed the obtained critical value, the optimal length of the actuator is smaller than the length of the beam, and it diminishes up to zero with the growth of the applied strain. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".