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Record W4411037466 · doi:10.18280/jesa.580415

Vibration Performance on the 3D-Printed UAV Wing

2025· article· fr· W4411037466 on OpenAlexvenueno aff
Mohammed Jawad Mohammed, Tamarah Ayad Kareem

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2025
Typearticle
Languagefr
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsnot available
Fundersnot available
KeywordsWing3d printedVibrationAeronauticsComputer scienceAerospace engineeringEngineeringAcousticsPhysicsBiomedical engineering

Abstract

fetched live from OpenAlex

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 (=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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.233
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicAeroelasticity and Vibration ControlFrench-language works237,207