Multidisciplinary Optimization for Weight Saving in a Variable Tapered Span-Morphing Wing Using Composite Materials—Application to the UAS-S4
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it