Functional Hazard Assessment of a Modular Re-Configurable Morphing Wing Using Taguchi and Finite Element Methodologies
Bibliographic record
Abstract
Growing concerns over the CO2 footprint due the exponential demand of the aviation industry, along with the requirements for high aerodynamic performance, cost saving, and manoeuvrability during different phases of a flight, pave the path towards adaptable wing design. Morphing wing design encompasses most, if not all, of the flight condition variations, and can respond interactively. However, functional failure of the morphing wing might bring devastating impacts on the passengers, crew, and/or aircraft. In the present work, the dynamic characteristics of a re-configurable modular morphing wing developed in-house by a research group at the Toronto Metropolitan University, are investigated from the perspective of a functional hazard assessment (FHA). This modular morphing wing, developed based on the idea of a parallel robot, consists of a number of structural elements connected to each other and to the wing ribs through eye-bolt joints. Timoshenko’s bending beam theory, in conjunction with the finite element method (FEM), is exploited to model the structural members. Possible hazards, assumed here to be the structural failure of the beam components, have been identified and their failure conditions are assessed. Numerical simulations have been presented to show the impact of various combinations of the identified hazards on the vibration signature of the morphing wing in unmorphed and morphed configurations. Identification of changes in the wing’s vibration signature is a vital component in the fail-safe structural and aeroelastic design of an aircraft. The present study is geared towards the structural response of the system in the absence of any aerodynamic loads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".