Optimum design of nonlinear symmetric truss structures under system stability constraint
Bibliographic record
Abstract
Among the multidisciplinary analysis and optimization problems, structural optimization of geometrically nonlinear stability problems is of great importance, especially in structures used in space applications because of their long and slender configurations. Structural optimization of nonlinear problems is costly due to iterative nature of both the nonlinear analysis and optimization. In this study, application of Group Theoretic Approach (GTA) in structural optimization of geometrical nonlinear problems under system stability constraint has been investigated. According to GTA, the number of displacement degrees of freedom in the initial configuration can be reduced significantly by using the set of symmetry transformations of the undeformed structure to construct a projection matrix from full space to a reduced subspace spanned by the symmetry modes. The limit point during the each optimization iteration has been obtained efficiently and accurately using a combination of the displacement control technique and the GTA. A structural optimization algorithm is developed for shallow structures undergoing large deflections subject to system stability constraint. The method combines the nonlinear buckling analysis based on the displacement control technique using GTA, and the optimality criteria approach based on the potential energy of the system. A closed form solution has been introduced to obtain the Lagrange multiplier, which allows the design process to be started in the infeasible region. A shallow dome truss structure has
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".