Optimization of Aircraft Aeroelastic Response Using Level Set Methods
Bibliographic record
Abstract
In this paper, we propose a new approach to the optimization of aircraft aeroelastic response that greatly reduces the number of design variables, thus enhancing the performance of multidisciplinary design tools. The current method is an extension of the Level Set Methods, which represent an interface as the zero level set of a function. According to the proposed formulation, the Fourier coefficients of the level set function are the design variables assigned to describe the interface. Two structural topology optimization examples and two applications to aircraft structures are presented. The first two examples deal with the optimal configuration of short and long cantilevered beams for maximum stiffness. In the first application, a system of actuators provides morphing capability to an airfoil by operating on its camber to increase lift. The problem consists in determining the airfoil profile that minimizes the power consumption while improving the airfoil effectiveness. In the second application, the aileron reversal speed is maximized by applying reinforcements to the upper skin of a wing torsion box. These four problems demonstrate that the proposed methodology is able to modify the topology of the interface while using a reduced number of design variables. Other advantages of this methodology include the partial avoidance of local non-global minima, by providing a mechanism for nucleation of new holes, and avoidance of checkerboard-like designs and sucessive remeshing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".