Optimization of Piezoelectric Actuator Configuration on a Flexible Fin for Vibration Control using Genetic Algorithms
Bibliographic record
Abstract
This study presents a novel approach to optimizing the configuration of piezoelectric actuators for vibration control of a flexible aircraft fin. The fitness (cost) function for optimization using a genetic algorithm is derived directly from the frequency response function (FRF) obtained from a finite element model of the fin. In comparison to existing approaches, this method allows optimization on much more complex geometries where the derivation of an analytical fitness function is prohibitive or impossible. This technique is applied to two optimization problems for vibration control of the fin. First, the position of a single actuator is optimized anywhere within a judiciously pre-determined area of the fin using a genetic algorithm for polynomial surface fitting of the FRF in order to obtain a continuous fitness function. Next, the configuration of a pre-determined number of up to 48 separate actuators is optimized within the same area. The optimization approach is verified against experimental results obtained from a set of 12 actuators fixed to an experimental model of the fin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".