Topology optimization of the internal structure of an aircraft wing subjected to self-weight load
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Bibliographic record
Abstract
A topology optimization design framework for structures including self-weight loads is presented. The proposed methodology applied to lifting structures aims to eliminate numerical instabilities due to the weight load while improving the overall solution quality. In this model, a power-law function is used to update the element density in the determination of the self-weight. The algorithm is tested and verified for a two-dimensional benchmark problem subjected to self-weight loads and a point force. Results show that the proposed method improves the discreteness of the design of structures subjected to lifting surface loads. Following the verification step, the proposed method is used to optimize the internal structure of an aircraft wing. The aerodynamic load is computed assuming a rigid wing body, and the loading condition is completed with the structure self-weight. Results show that, in this particular example, the self-weight load has a negligible influence on the optimal design.
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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