Design and Optimization of Droop Nose Leading Edge (DNLE) Morphing Wing Skin for the UAS-S45
Bibliographic record
Abstract
This study investigates the design and optimization of a flexible Droop Nose Leading Edge (DNLE) based on the composite laminate skin for the UAS-S45. The concept of a morphing DNLE airfoil has excellent potential for drag and airframe noise reduction. The essential part of this DNLE airfoil type is the easiness of its mechanism deformation while maintaining the wing's structural integrity. The morphing DNLE must change the baseline shape under the influence of the aerodynamic loads to obtain its desired optimized target shape. This study proposes an optimization method to evaluate the skin design's feasibility and check the composite's failure index when the morphing DNLE changes shape. The approach yielded the desired aerodynamics shape of flexible droop nose leading edge. Wing leading edge topology depends on composite wing properties, such as ply-orientation, ply-thickness, and other composite wing parameters. This paper presents a design methodology of stiffness coefficients and lamination parameters for the stacking sequence optimization during the DNLE morphing deformation. In addition to increasing the deformation accuracy of the final airfoil shape made of composite skin, the use of stiffness coefficients and lamination parameters also effectively and efficiently defines the sequence of the composite lay-up. The deformed airfoil shapes between the optimized lay-ups and their modified shapes are compared to confirm their abilities to morph and aerodynamically optimize their shapes. The numerical results of the droop nose morphing with composite materials proved structure morphing capacity and showed feasibility of wing leading edge design mechanism and show the ability and accuracy of the methodology to obtain their morphing wing shapes.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".