Topology Optimization for Stiffened Panels: A Ground Structure Method
Bibliographic record
Abstract
Abstract Reducing the weight of structures remains a major challenge in the aviation industry in order to reduce fuel consumption. The stiffened panel is the main assembly method for primary structures in aircraft, e.g. fuselage or wing. Density-based topology optimization has been used in research and in industry as a tool to help create new stiffening patterns for aircraft components, such as ribs, spars, bulkheads or even floor design. One critical aspect of stiffened panel design for wing structures is the buckling resistance. However, most work found in the literature does not include buckling analysis during optimization which leads to sub-optimal results when the stiffening layout is validated for buckling. Including buckling as a constraint for the density-based topology optimization has proven to be a complex task, mainly caused by the fact that the buckling of the stiffeners is not captured accurately. As such, this work presents an optimization method for stiffened panels based on the ground structure approach usually used for truss topology optimization. The main novelty of the method is the use of a stiffener activation variable (SAV) to activate only one variable at a time, either the height or density variable associated with each stiffeners of the ground structure. This work shows that while ground structure topology optimization requires more setup time and limiting the degrees of freedom of the optimization, it finds the best stiffening layout efficiently when compared to the density method.
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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) | 0.001 | 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 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".