Structural and Topological Optimization of a Novel Elephant Trunk Mechanism for Morphing Wing Applications
Bibliographic record
Abstract
A novel mechanism for seamless morphing trailing edge flaps is presented in this paper. This bio-inspired morphing concept is derived from an elephant’s trunk and is called the Elephant Trunk Mechanism (ETM). The structural flexibility of an elephant’s trunk and its ability to perform various types of deformations make it a promising choice in morphing technology for increasing the performance of continuous and smooth downward bending deformation at a trailing edge. This mechanism consists of a number of tooth-like elements attached to a solid wing box; the contractions of these tooth-like elements by external actuation forces change the trailing edge shape in the downwards direction. The main actuation forces are applied through wire ropes passing through tooth-like elements to generate the desired contractions on the flexible teeth. A static structural analysis using the Finite Element Method (FEM) is performed to examine this novel morphing concept and ensure its structural feasibility and stability. Topology optimization is also performed to find the optimum configuration with the objective of reducing the structural weight. The optimized mechanism is then attached to the flap section of a UAS-S45 wing. Finally, a skin analysis is performed to find its optimum skin material, which corresponds to the requirements of the morphing flap. The results of structural analysis and topology optimization reveal the reliability and stability of the proposed mechanism for application in the Seamless Morphing Trailing Edge (SMTE) flap. The optimization results led to significant improvements in the structural parameters, in addition to the desired weight reduction. The ETM maximum vertical displacement increased by 8.6%, while the von Mises stress decreased by 10.43%. Furthermore, the factor of safety improved from 1.3 to 1.5, thus indicating a safer design. The mass of the structure was reduced by 35.5%, achieving the primary goal of topology optimization.
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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) | 0.002 | 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".