Vibration Performance on the 3D-Printed UAV Wing
Bibliographic record
Abstract
The fabrication of an Unmanned Air Vehicle (UAV) wing using a 3D printer and the study of vibration behavior were conducted in this research.The study of vibration behavior is an important factor to preserve the structures from destruction, especially when the frequency of the bodies is closed to the natural frequency.The aircraft airfoil was designed based on Naka 2416 and the wings were printed using PLE metal.Then, air currents were directed at speeds of 10, 15, and 20 m/s to study the effect of vibrations in order to find the frequency for each speed using a fan whose speed is controlled by an inverter and compare it to the natural frequency.Pitot-tube and accelerometer were used to measure the air speed and vibration, respectively.The results showed that the damping coefficient is relatively small (ζ=0.0644),indicating that the damping in the system is weak, which is in line with the design of UAV that need low damping to maintain a fast response during flight.Also, the wing frequencies reached up to 2.66 Hz and 5.66 at speeds of 20 m/s and 15 m/s, respectively were closest to the natural frequency of the UAV wings, which was 3.94 Hz, compared to the frequency at speed of 10 m/s.The results also showed that the difference in frequencies were due to the emergence of non-linear phenomena in addition to the aerodynamic and structural forces, which is called aeroelastic effects, which leads to dynamic instability.
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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".