Multidisciplinary Optimization for Weight Saving in a Variable Tapered Span-Morphing Wing Using Composite Materials—Application to the UAS-S4
Bibliographic record
Abstract
This paper is a follow-up to earlier work on applying multidisciplinary numerical optimization to develop a morphing variable span of a tapered wing (MVSTW) to reduce its weight by using composite materials. This study creates a numerical environment of multidisciplinary optimization by integrating material selection, structural sizing, and topological optimization following aerodynamic optimization results with the aim to assess whether morphing wing optimization is feasible. This sophisticated technology is suggested for developing MVSTWs. As a first step, a problem-specific optimization approach is described for specifying the weight-saving structure of wing components using composite materials. The optimization was performed using several approaches; for example, aerodynamic optimization was performed with CFD and XFLR5 codes, the material selection was conducted using MATLAB code, and sizing and topology optimization was carried out using Altair’s OptiStruct and SolidThinking Inspire solvers. The goal of this research is to achieve the MVSTW’s structural rigidity standards by minimizing wing components’ weight while maximizing stiffness. According to the results of this optimization, the weight of the MVSTW was reduced significantly to 5.5 kg in comparison to the original UAS-S4 wing weight of 6.5kg. The optimization and Finite Element Method results also indicate that the developedmorphing variable span of a tapered wing can complete specified flight missions perfectly and without any mechanical breakdown.
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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".