Wing component allocation for a morphing variable span of tapered wing using finite element method and topology optimisation – application to the UAS-S4
Bibliographic record
Abstract
Abstract This work presents the Topology Optimisation of the Morphing Variable Span of Tapered Wing (MVSTW) using a finite element method. This topology optimisation aims to assess the feasibility of internal wing components such as ribs, spars and other structural components. This innovative approach is proposed for the telescopic mechanism of the MVSTW, which includes the sliding of the telescopically extended wing into the fixed wing segment. The optimisation is performed using the tools within ANSYS Mechanical, which allows the solving of topology optimisation problems. This study aims to minimise overall structural compliance and maximise stiffness to enhance structural performance, and thus to meet the structural integrity requirements of the MVSTW. The study evaluates the maximum displacements, stress and strain parameters of the optimised variable span morphing wing in comparison with those of the original wing. The optimised wing analyses are conducted on four wingspan extensions, that is, 0%, 25%, 50% and 75%, of the original wingspan, and for different flight speeds to include all flight phases (17, 34, 51 and 68m/s, respectively). Topology optimisation is carried out on the solid wing built with aluminium alloy 2024-T3 to distribute the wing components within the fixed and moving segments. The results show that the fixed and moving wing segments must be designed with two spar configurations, and seven ribs with their support elements in the high-strain area. The fixed and moving wing segments’ structural weight values were reduced to 16.3 and 10.3kg from 112 to 45kg, respectively. The optimised MVSTW was tested using different mechanical parameters such as strains, displacements and von Misses stresses. The results obtained from the optimised variable span morphing wing show the optimal mechanical behaviour and the structural wing integrity needed to achieve the multi-flight missions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".